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Working Capital Efficiency
and Firm Profitability - A Quantitative Study of Listed
Swedish Firms 2000-2015
Master’s Thesis 30 credits
Department of Business Studies
Uppsala University
Spring Semester of 2017
Date of Submission: 2017-05-30
Oscar Gustén
Tobias Pahkamaa
Supervisor: Jan Lindvall
II
Abstract
This thesis examines the relationship between working capital efficiency and firm
profitability, and how this relationship is affected by economic fluctuations. In the existing
literature, the relationship between working capital efficiency and firm profitability has been
extensively researched. However, the impact of economic fluctuations on the relationship
between working capital efficiency and firm profitability is sparsely researched. To the best of
our knowledge, only Enqvist, Graham and Nikkinen (2014) have addressed the impact of
economic fluctuations on the relationship between working capital efficiency and firm
profitability. This thesis is a replication of their study in another geographical setting, another
time period and studying different types of firms.
Using a sample of 2,589 firm-year observations of listed Swedish firms for the years 2000-
2015, this thesis conducts multiple regression analysis to examine the relationship between
working capital efficiency and firm profitability. The findings of this thesis propose that firms
can enhance profitability by improving their working capital efficiency. However, the
relationship between working capital efficiency and firm profitability does not appear to be
significantly affected by economic fluctuations. This thesis contributes to the existing
literature by further strengthening the understanding of the relationship between working
capital efficiency and firm profitability. In addition, it also adds to the existing literature on
the relationship between working capital efficiency and firm profitability in a Swedish
context.
Keywords: working capital management, working capital efficiency, firm profitability, cash
conversion cycle, days of inventory, days of accounts receivables, days of accounts payables,
economic fluctuations
III
Table of Contents
Abstract ................................................................................................................................................. II List of tables ......................................................................................................................................... IV List of figures ....................................................................................................................................... IV List of abbreviations ............................................................................................................................. V 1. Introduction ....................................................................................................................................... 6 2. Literature review ............................................................................................................................... 8
2.1 Working capital management ........................................................................................................ 8 2.1.1 Strategies of working capital management ............................................................................ 9 2.1.2 Efficiency of working capital management .......................................................................... 11
2.2 Cash conversion cycle ................................................................................................................. 11 2.2.1 Inventory, accounts receivables and accounts payables ...................................................... 12
2.3 Working capital management and economic fluctuations ........................................................... 14 2.4 The original study: Frame, results, discussion and implications ................................................. 15 2.5 Hypotheses formulation .............................................................................................................. 17
3. Methodology..................................................................................................................................... 19 3.1 Replication................................................................................................................................... 19
3.1.1 The original study: Methodological review .......................................................................... 20 3.1.2 Thesis frame ......................................................................................................................... 21
3.2 Operationalization ....................................................................................................................... 22 3.3 Variable selection ........................................................................................................................ 23
3.3.1 Dependent variable .............................................................................................................. 23 3.3.2 Independent variables .......................................................................................................... 24 3.3.3 Dummy variables .................................................................................................................. 25 3.3.4 Control variables .................................................................................................................. 25
3.4 Regression models ....................................................................................................................... 26 3.5 Data ............................................................................................................................................. 27
3.5.1 Population and sample ......................................................................................................... 29 3.5.2 Statistical tools ..................................................................................................................... 30 3.5.3 Descriptive statistics ............................................................................................................. 30 3.5.4 Correlation matrix ................................................................................................................ 32 3.5.5 Data testing .......................................................................................................................... 33
4. Results .............................................................................................................................................. 35 4.1 Regression analysis: Return on assets ......................................................................................... 35 4.2 Regression analysis: Gross operating income ............................................................................. 38 4.3 Hypothesis testing ....................................................................................................................... 40
5. Discussion ......................................................................................................................................... 43 5.1 Working capital efficiency and firm profitability........................................................................ 43
5.1.1 Descriptive statistics and correlations ................................................................................. 47 5.2 Contributions to research ............................................................................................................. 47 5.3 Limitations of this thesis ............................................................................................................. 48 5.4 Replication as a scientific method ............................................................................................... 49
6. Conclusion ........................................................................................................................................ 51 6.1 Suggestions for future research ................................................................................................... 51
References ............................................................................................................................................ 53 Appendices ........................................................................................................................................... 58
Appendix A: Previous research ......................................................................................................... 58 Appendix B: Variable summarization and formulas ......................................................................... 62 Appendix C: Descriptive statistics .................................................................................................... 63 Appendix D: Statistical testing .......................................................................................................... 64
IV
List of tables
Table 1: Previous research on CCC and firm profitability .................................................................... 12 Table 2: Previous research on the individual components of CCC and firm profitability .................... 14 Table 3: Hypotheses group 1 ................................................................................................................. 18 Table 4: Hypotheses group 2 ................................................................................................................. 18 Table 5: Classification of economic downturns and economic booms ................................................. 29 Table 6: Descriptive statistics full sample ............................................................................................. 30 Table 7: Pearson’s correlation matrix.................................................................................................... 32 Table 8: Regression analysis ROA ........................................................................................................ 36 Table 9: Regression analysis GOI ......................................................................................................... 39 Table 10: Hypothesis testing group 1 .................................................................................................... 41 Table 11: Hypothesis testing group 2 .................................................................................................... 42
List of figures
Figure 1: Annual GDP growth (%) of Sweden 2000-2015 ................................................................... 28 Figure 2: Number of observations per year ........................................................................................... 30
V
List of abbreviations
AR Days of accounts receivables
AP Days of accounts payables
CCC Cash conversion cycle
CR Current ratio
DEBT Firm debt ratio
GDP Gross domestic product
GOI
INV
Gross operating income
Days of inventory
OLS Ordinary least squares
ROA Return on assets
SIZE
SMEs
Firm size
Small and medium-sized enterprises
6
1. Introduction
Working capital is a hot and ever-present topic in both business practice and research
(Arvidsson & Engman 2013 p. 9). It is an interesting and important topic because it affects the
operations and strategies of firms (Shin & Soenen 1998). Following the global financial crisis
in 2007-2008, working capital management has gained increased attention in business
practice due to its potential to free capital and cover the liquidity needs of firms (Kaiser &
Young 2009; PwC 2015). In business research, working capital management has been
suggested to impact firm profitability through its impact on firm liquidity (Wang 2002; Eljelly
2004) and a number of studies have concluded that firms can enhance profitability through
efficient management of working capital (e.g. Jose, Lancaster & Stevens 1996; Deloof 2003;
Garcia-Teruel & Martinez-Solano 2007).
The relationship between working capital efficiency and firm profitability has been suggested
to be dependent on country (Koralun-Bereźnicka 2014), industry (Jose et al. 1996) and firm
type (Howorth & Westhead 2003). In addition, working capital management has been
connected to the economic environment in different ways. Merville and Tavis (1973)
discussed how the economic environment affects the constituents of working capital, while
Filbeck and Krueger (2005) more precisely concluded that interest rates, innovation and
competition are the main determinants of working capital over time. Previous studies have
suggested that the working capital of firms is countercyclical (Einarsson & Marquis 2001) and
that firms with higher investments in working capital are hit harder in economic downturns
(Braun & Larrain 2005). However, how the relationship between working capital efficiency
and firm profitability is affected by economic fluctuations is barely researched.
Enqvist, Graham and Nikkinen (2014) studied the impact of business cycles on the
relationship between working capital efficiency and firm profitability among listed Finnish
firms for the years 1990-2008. They concluded that the importance of the relationship
between working capital efficiency and firm profitability varies with the economic
environment and, more specifically, increases in economic downturns. However, there is a
significant gap in the existing literature about the impact of economic fluctuations on the
relationship between working capital efficiency and firm profitability. To fill this gap, this
thesis is a replication study of Enqvist et al. (2014). This adds to the existing literature by
accumulating empirical knowledge to an under-researched field of working capital
management. Enqvist et al. (2014) suggested that their findings are generalizable to the
7
Nordic region as a whole. By drawing upon data from listed Swedish firms for the years
2000-2015, this thesis replicates their study in another geographical setting, another time
period and studying different types of firms. The relationship between working capital
efficiency and firm profitability is sparsely researched in the Swedish context, only addressed
by Yazdanfar and Öhman (2014) in their study of Swedish small and medium-sized
enterprises (SMEs) for the years 2008-2011. This approach will contribute to the
understanding of the relationship between working capital efficiency and firm profitability,
and how this relationship is affected by economic fluctuations in a Swedish context.
Additionally, this thesis contributes to the discussion about the importance of replication
studies in business research.
The aim of this thesis is to examine how the relationship between working capital efficiency
and firm profitability is affected by economic fluctuations among a sample of 2,589 firm-year
observations of listed Swedish firms for the years 2000-2015. Thus, the aim is two-folded.
Firstly, it examines the relationship between working capital efficiency and firm profitability.
In so doing, it contributes to the existing literature by adding findings from listed Swedish
firms. Secondly, it investigates how the aforementioned relationship is affected by economic
fluctuations. This thesis replicates the study of Enqvist et al. (2014) and tests their approach in
another context. The research question of this thesis is formulated:
What is the relationship between working capital efficiency and firm profitability, and
how is this relationship affected by economic fluctuations?
The findings of this thesis suggest that firms can enhance profitability by improving their
working capital efficiency. However, the relationship between working capital efficiency and
firm profitability does not appear to be affected by economic fluctuations among sampled
firms. Therefore, more research is encouraged in order to create an increased understanding of
how the economic environment impacts the relationship between working capital efficiency
and firm profitability.
The remainder of this thesis is structured as follows: in section 2 a review of the literature and
the hypotheses are presented; in section 3 the methodological approach is outlined; in section
4 the results of the statistical data analysis and hypothesis testing are presented; in section 5
the results are discussed, and in section 6 the main conclusions are discussed and suggestions
for future research are presented.
8
2. Literature review This thesis is a replication study of Enqvist et al. (2014). Replication studies are conducted
with the purpose to amass a cumulative body of knowledge. Therefore, it is crucial that the
replication study matches the research of the original study to the extent it is possible (Bettis,
Helfat & Shaver 2016). In this thesis, the study of Enqvist et al. (2014) is replicated in another
geographical setting, covering a different time frame and researching different types of firms
with a deeper discussion of the theoretical framework. In this section, a review of the
literature is presented in order to contextualize the research problem and provide the
theoretical background of this research field. Following the discussion of the literature, the
hypotheses of this thesis are formulated.
2.1 Working capital management
Working capital management is a key aspect of capital allocation because it has a direct
impact on the long-term growth opportunities of firms (Arvidsson & Engman 2013 p. 9).
Mauboussin and Callahan (2014) recognize capital allocation as the most fundamental
responsibility of top management. According to them, capital allocation is primarily
concerned with how capital is divided between business operations and claimholders. Within
business operations, capital allocation deals with the division of capital between capital
expenditure, research and development, mergers and acquisitions and working capital. As
such, working capital is a part of a wider business scope. This is an important notion because
working capital is not an isolated issue. By placing working capital in a wider context, its
importance for firms can be better understood.
Working capital is commonly defined as the excess of current assets over current liabilities
(Preve & Sarria-Allende 2010 p. 16). By this definition, working capital management is
concerned with how firms manage their current assets and current liabilities (Arvidsson &
Engman 2013 p. 9). These definitions of working capital and working capital management are
standard definitions in this field of research (e.g. Jose et al. 1996; Deloof 2003). As such,
working capital management has been viewed as all decisions made by management that
affect working capital (Kaur 2010). This means working capital management is a broad
concept including different managerial aspects. In early business research, working capital
was discussed as an analytical tool to analyze the financial position of firms (Guthmann 1934)
and later as an important information source for management (Park 1951). Sagan (1955)
suggested that active management of working capital is a determinant of firm strategy and
9
ultimately of firm performance. Following this discussion, Bierman, Chopra and Thomas
(1975) proposed that working capital serves two purposes. Firstly, working capital is a
financial buffer. This is important because it protects the firm from uncertainty and provides
the means necessary to act on opportunities that arise. Secondly, working capital is a means of
increased earnings. Through investment in working capital, firms can e.g. employ more
generous credit policies which are assumed to increase profits. Filbeck and Krueger (2005)
concluded that working capital management is a determinant of firm strategy and a crucial
success factor of firms. Working capital management has been suggested to be firm-specific
(Merville & Tavis 1973) but more often it has been discussed as dependent on firm type
(Howorth & Westhead 2003), industry-specific (Shin & Soenen 1998) or country-specific
(Koralun-Bereźnicka 2014).
Bierman et al. (1975) proposed working capital management to be concerned with both firm
liquidity and firm profitability. However, others have regarded working capital management
rather as a trade-off between firm liquidity and firm profitability (Raheman & Nasr 2007;
Eljelly 2004). Ebben and Johnson (2011) concluded that efficient utilization of working
capital can improve both firm liquidity and firm profitability. Capital has an opportunity cost
and working capital management is concerned with a balance between the components of
working capital in order to maintain firm operations, while not binding excessive capital in
working capital (Garcia-Teruel & Martinez-Solano 2007). As such, working capital
management is a crucial part of firm operations that ultimately affects firm profitability
through its effects on firm liquidity (Arunkumar & Radharamanan 2011). In recent years, a
growing body of research has connected increased efficiency of working capital management
to improved firm profitability (e.g. Jose et al. 1996; Deloof 2003; Garcia-Teruel & Martinez-
Solano 2007).
2.1.1 Strategies of working capital management
Garcia-Teruel and Martinez-Solano (2007) discuss how firms must consider a balance
between profitability and risk when deciding strategy of working capital management. They
argue that this is because decisions that tend to increase profitability tend to increase the risk
and vice versa. Following their discussion, they suggest that working capital management can
be divided in two principal strategies with direct impact on the working capital of firms.
Aggressive strategies are directed towards the minimization of working capital in order to
maximize profitability. Conservative strategies are accepting higher levels of working capital
10
in order to maintain stable and uninterrupted firm operations. These contrasting strategies will
impact firm operations through different effects on the relationship between risk and
profitability.
Aggressive and conservative strategies of working capital management affect firm operations
in different ways (Jose et al. 1996; Garcia-Teruel & Martinez-Solano 2007). All such
decisions affect the interaction between risk and profitability. More specifically, aggressive
strategies of working capital management actively seek a reduction in working capital in order
to maximize the efficiency of operations. This can be achieved through e.g. lower levels of
inventory holdings (Jose et al. 1996) or tighter credit policies (Garcia-Teruel & Martinez-
Solano 2007). Such decisions free capital for use elsewhere within firms (Arvidsson &
Engman 2013 p. 12) and can be seen as a flexible source of financing that lower the capital
costs of firms (Deloof 2003). It also comes with a potential risk of lower sales due to e.g.
stock-outs (Jose et al. 1996) or customers requiring credits buying elsewhere (Garcia-Teruel
& Martinez-Solano 2007). Conservative strategies of working capital management will accept
higher levels of working capital. This will bind more capital in working capital and increase
the capital cost of firms (Arvidsson & Engman 2013 p. 12). Such decisions will lower the risk
of operations but these benefits are often at the price of lower profitability due to higher
investment in current assets (Garcia-Teruel & Martinez-Solano 2007). Deloof (2003) discuss
that higher levels of working capital can be a result of increased sales or a necessity in order
to meet the demands from customers. He concludes that firm profitability will increase as
long as the costs of increasing sales do not exceed the benefits of the same.
Deloof and Jegers (1996) suggest that firms’ strategy of working capital management will be
affected by their positions in different networks. This has been discussed by Ng, Smith and
Smith (1999) who suggest that the credit policies of firms will affect their long-term
relationships with customers. Further, Deloof (2003) proposes that more profitable firms pay
their suppliers earlier, while Garcia-Teruel and Martinez-Solano (2007) argued that it makes
economic sense to keep capital within firms as long as possible in order to lower the capital
costs of firms. This must be put in relation to firms’ positions in networks and their long-term
relationship with suppliers. These perspectives have often been neglected in previous
research. However, it is possible that the benefits of long-term relationships may counter the
increasing costs of higher investments in working capital.
11
2.1.2 Efficiency of working capital management
How firms approach working capital will affect firm strategy through its effects on especially
the management of inventory, accounts receivables and accounts payables (Deloof 2003).
Filbeck and Krueger (2005) proposed that the purpose of the working capital management of
firms is to obtain and maintain the optimum of each of the components of working capital. In
their view, efficient management of inventory, accounts receivables and accounts payables are
crucial in order to reduce external and internal financing costs, and ultimately increase firm
profitability. Traditional measures of the financial health of firms, such as current ratio (CR)
or quick ratio, will focus attention on the wrong things (Hager 1976; Richards & Laughlin
1980). Instead of optimizing working capital, they will encourage firms to maintain higher
levels of inventory and accounts receivables in relation to accounts payables and these must
be financed by debt and equity (Ebben & Johnson 2011). Working capital efficiency is better
captured by the cash conversion cycle (CCC), as it measures the efficiency of firms’
management of inventory, accounts receivables and accounts payables, and not only the
relation between current assets and current liabilities (Gitman 1974).
2.2 Cash conversion cycle
The CCC is a dynamic measure of the ongoing liquidity management of firms that combines
both balance sheet and income statement data, to create a measure with a time dimension
(Jose et al. 1996). The CCC consists of days of inventory (INV), days of accounts receivables
(AR) and days of accounts payables (AP), and captures the time lag between the purchase of
input goods and services to the collection of payment from customers (Arvidsson & Engman
2013 p. 23). As such it is also an operating variable measuring the working capital efficiency
of firms (Garcia-Teruel & Martinez-Solano 2007). While the CCC has been regarded as a
better measurement of firm liquidity than traditional measures (Eljelly 2004), it must be clear
that it has its limitations. Cagle, Campbell and Jones (2013) note that the CCC does not fully
consider current liabilities. As an effect, constituents of current liabilities, such as interest,
payroll and taxes are not considered. These may also have a significant impact on firm
liquidity, and ultimately on firm profitability. Despite this limitation, the CCC is the most
commonly used measure of working capital efficiency in a vast research tradition of working
capital (e.g. Jose et al. 1996; Shin & Soenen 1998; Deloof 2003; Garcia-Teruel & Martinez-
Solano 2007).
12
In this research tradition, a lower CCC is an indicator of higher working capital efficiency. A
vast majority of previous research has found a negative relationship between CCC and firm
profitability (see table 1 below). To the best of our knowledge, there are only two studies
presenting a positive relationship between CCC and firm profitability. Lyroudi and Lazaridis
(2000) examined the relationship between working capital efficiency and firm profitability
among firms in the Greek food industry for the year 1997, and found a positive relationship
between CCC and firm profitability. However, other studies on specific industries have
consistently found a negative relationship between CCC and firm profitability (e.g. Padachi
2006; Ganesan 2007). Sharma and Kumar (2011) studied the effects of working capital
efficiency on firm profitability of Indian firms for the years 2000-2008, and found a positive
relationship between CCC and firm profitability. They suggest that the relationship between
working capital efficiency and firm profitability may be different in emerging markets.
However, other studies in emerging markets have consistently found a negative relationship
between CCC and firm profitability (e.g. Falope & Ajilore 2009; Mathuva 2010). While
Lyroudi and Lazaridis (2000) and Sharma and Kumar (2011) present a positive relationship
between CCC and firm profitability, a large number of studies present a strong case for a
negative relationship between CCC and firm profitability. Previous research is summarized in
table 1 below (for a more detailed presentation of previous research, see appendix A.1).
Table 1: Previous research on CCC and firm profitability
Effect →
Variable
↓
Significant negative relationship with firm
profitability
Significant positive relationship with firm
profitability
CCC Jose, Lancaster & Stevens (1996)
Shin & Soenen (1998)
Wang (2002)
Deloof (2003)
Eljelly (2004)
Lazaridis & Tryfonidis (2006)
Garcia-Teruel & Martinez-Solano (2007)
Falope & Ajilore (2009)
Gill, Biger & Mathur (2010)
Mathuva (2010)
Ebben & Johnson (2011)
Yazdanfar & Öhman (2014)
Lyroudi & Lazaridis (2000)
Sharma & Kumar (2011)
2.2.1 Inventory, accounts receivables and accounts payables
Jose et al. (1996) conceptualize how the strategy of working capital will affect a firm’s CCC.
An aggressive strategy will aim to minimize working capital through shortening of the CCC,
while a conservative strategy will accept higher levels of working capital. Expressed as the
13
function of CCC, an aggressive strategy will aim to achieve lower INV, lower AR and higher
AP in order to minimize the capital bound in working capital and thereby maximizing firm
profitability. A conservative strategy will accept higher levels of working capital in order to
maintain stable and uninterrupted firm operations. This view is problematized by Deloof
(2003) who suggests that a longer CCC might be a consequence of higher sales and thus
increasing profitability. However, if the costs of higher levels of working capital rise faster
than the benefits of e.g. larger inventory holdings or looser credit policies, firm profitability
will be negatively affected by a higher CCC.
Management of inventory, accounts receivables and accounts payables will affect the risk and
profitability of firm operations (Garcia-Teruel & Martinez-Solano 2007). Thus, the
optimization of the components of CCC must affect firm operations. Firstly, INV can either
be kept at a bare minimum or above minimum levels. Lower INV will decrease the cost of
holding inventory and therefore increase the efficiency of operations (Deloof 2003; Enqvist et
al. 2014). However, it will also increase the risk as firms are e.g. risking losing sales due to
stock-outs (Jose et al. 1996; Gill et al. 2010). Secondly, AR can be kept higher or lower.
Lower levels of accounts receivables will decrease the cost of capital and increase firm
efficiency (Deloof 2003; Lazaridis & Tryfonidis 2006). It can also increase the risk because
the firm might e.g. lose customers that require credits (Jose et al. 1996; Sharma & Kumar
2011). Thirdly, AP can be adjusted with different outcomes on the relationship between risk
and efficiency. The longer a firm delays their payments to suppliers, the lower the CCC. This
will decrease the cost of capital, because the capital is kept within the firm for as long as
possible. Higher AP might lead to e.g. negative responses from suppliers (Jose et al. 1996).
However, Deloof (2003) points out that accounts payables can be used as an inexpensive and
flexible source of financing which can be used to increase profitability.
In previous research, increased working capital efficiency has been regarded as lower INV,
lower AR and higher AP. INV and AR have consistently been found to have a negative
relationship with firm profitability. From a theoretical point of view, AP has been assumed to
have a positive relationship with firm profitability. However, previous research has more
often found a negative relationship between AP and firm profitability. Previous research is
summarized in table 2 below (for a more detailed presentation of previous research, see
appendix A.2).
14
Table 2: Previous research on the individual components of CCC and firm profitability
Effect →
Variable
↓
Significant negative relationship with firm
profitability
Significant positive relationship with firm
profitability
INV Deloof (2003)
Lazaridis & Tryfonidis (2006)
Garcia-Teruel & Martinez-Solano (2007)
Falope & Ajilore (2009)
Sharma & Kumar (2011)
Enqvist, Graham & Nikkinen (2014)
Mathuva (2010)
AR Deloof (2003)
Lazaridis & Tryfonidis (2006)
Falope & Ajilore (2009)
Gill, Biger & Mathur (2010)
Mathuva (2010)
Sharma & Kumar (2011)
AP Lazaridis & Tryfonidis (2006)
Falope & Ajilore (2009)
Sharma & Kumar (2011)
Enqvist, Graham & Nikkinen (2014)
Mathuva (2010)
2.3 Working capital management and economic fluctuations
Merville and Tavis (1973) suggest that the different components of working capital
management are in interplay with each other as well as with the economic environment.
According to them, working capital must be affected by economic fluctuations. A natural
consequence of this is that the optimization of the individual firm’s working capital
management must take economic fluctuations into consideration. Filbeck and Krueger (2005)
discussed how working capital management changes over time. They concluded that working
capital management change over time because of influences from interest rates, innovation
and competition. In addition, they found that many of the variations in working capital
practices and working capital performance may be caused by economic fluctuations. Different
types of firms are affected differently by economic fluctuations, which lead to different
influences on working capital management.
Einarsson and Marquis (2001) examined the relationship between working capital
management policies and business cycles. They focused on the degree to which firms rely on
external financing to finance working capital over business cycles in the US for the years
1960-1994. Their findings suggest that firms’ external financing of working capital is
countercyclical and that it increases in economic downturns. In line with this, Braun and
Larrain (2005) studied a sample of 57,538 observations from over 100 countries to investigate
15
the link between external financing and growth over business cycles for the years 1963-1999.
Their results suggest that industries that are more dependent on external financing are hit
harder in economic downturns. Industries with higher working capital also relied more on
external financing. An implication of this is that working capital management plays an
important role in the financing of firms, and the way that firms are financed will affect their
performance. Higher working capital efficiency will decrease the need of external financing
and ultimately increase firm profitability.
In previous research, working capital management has been connected to the economic
environment (e.g. Merville & Tavis 1973; Filbeck & Krueger 2005; Enqvist et al. 2014). The
economic environment has been discussed as economic uncertainty over time (Merville &
Tavis 1973) and changes across time due to the factors interest rates, innovation and
competition (Filbeck & Krueger 2005). In addition, Enqvist et al. (2014) discussed business
cycles on the basis of the variation in the annual growth of the gross domestic product (GDP)
in relation to the long-term growth trend of GDP. This is a narrow definition of business
cycles. This thesis is not concerned with understanding the business cycle, but rather
examining the effects of a changing economic environment on the relationship between
working capital efficiency and firm profitability. While there are different economic theories
making different claims regarding the origins and functions of business cycles, in this thesis it
is sufficient to conclude that economic fluctuations have persisted and forms unsystematic but
regular patterns of economic activity (Schön 2013 p. 24). Aggregated economic activity on a
national level is normally measured using GDP. GDP captures the market value of all final
goods and services produced within a nation for a given time period. Economic fluctuations
are normally measured as fluctuations in annual GDP growth (e.g. Neumeyer & Perri 2005;
Enqvist et al. 2014). For this reason, unlike Enqvist et al. (2014), this thesis does not
conceptualize economic fluctuations as business cycles. The same concept is measured in this
thesis but with a different definition in comparison to Enqvist et al. (2014).
2.4 The original study: Frame, results, discussion and implications
Enqvist et al. (2014) examined the role of business cycles, measured as GDP fluctuations, on
the relationship between working capital efficiency and firm profitability (for a
methodological review, see 3.1.1). Firm profitability was measured by return on assets (ROA)
and gross operating income (GOI). Using a sample of 1,136 firm-year observations of listed
Finnish firms for the years 1990-2008, they found a negative relationship between CCC and
16
firm profitability. In addition, the significance of this relationship increased in economic
downturns. Further, they examined the relationship between INV, AR and AP, and firm
profitability. Their findings suggest a significant negative relationship between INV and firm
profitability. This relationship was enhanced in economic downturns. They found a negative
but insignificant relationship between AR and firm profitability. This relationship was
enhanced in economic downturns but unchanged in economic booms. The relationship
between AP and firm profitability was significantly negative, but did not change with
economic fluctuations. Lastly, the relationship between the control variables and firm
profitability was tested. They found a significant positive relationship between CR and firm
profitability. Firm debt ratio (DEBT) was found to have a negative relationship with firm
profitability, however, this relationship was only significant in relation to ROA. Firm size
(SIZE) was found to be negatively related to firm profitability. For the dummy variables, they
found that firm profitability increased during economic booms and decreased during
economic downturns.
Enqvist et al. (2014) suggest there are optimal levels of working capital for firms and that this
optimum will vary with economic fluctuations. This leads them to conclude that quicker
inventory turnover, quicker collection of accounts receivables and shorter cycles of accounts
payables enhance firm profitability. Further, the business environment affects firm
profitability. In economic downturns the profitability of firms decreases, while in economic
booms it increases. In addition, the impact of efficient working capital management increases
in economic downturns because the importance of efficient inventory management and
efficient collection of receivables increases.
A practical business implication of this is that investments in working capital are crucial for
firms and firms should incorporate working capital management in their daily routines.
Enqvist et al. (2014) further suggest their findings have implications for national economic
policy. Firms generate income and employment opportunities, why they are important
functions of the economy. As such, national economic policy has an interest and opportunity
to boost the cash flow of firms. This would increase firms’ ability to finance working capital
internally, especially in economic downturns. Lastly, they suggest that their findings can be
generalized to the Nordic region as a whole.
17
2.5 Hypotheses formulation
There are two groups of hypotheses in this thesis. The first group, hypothesis 1-4, tests the
relationship between working capital efficiency and firm profitability. The second group,
hypothesis 5-8, tests how the relationship between working capital efficiency and firm
profitability is affected by economic fluctuations. Working capital efficiency has in previous
studies been measured using the CCC. The CCC captures the time lag between the purchases
of inputs to the collection of sales. As such, it adds a time dimension that static measurements
do not have. Working capital efficiency has been suggested to shorten the CCC (e.g. Jose et
al. 1996; Shin & Soenen 1998; Deloof 2003). This because a shorter CCC means less capital
is bound in working capital and therefore firms should aim to decrease their CCC through
lower INV, lower AR and higher AP (Arvidsson & Engman 2013 p. 11).
From a theoretical perspective, this is intuitive as less capital bound in working capital frees
capital to be used elsewhere within firms. As a result, firms will be less dependent on external
financing (Filbeck & Krueger 2005) which lower the cost of capital of firms (Deloof 2003).
Through increased working capital efficiency, firms can generate increased internal financing
as a means to e.g. increase profitability (Bierman et al. 1975). In addition, previous research
has found a negative relationship between CCC, INV, AR, AP, and firm profitability. As
such, there is a discrepancy between theory and empirical findings regarding the relationship
between AP and firm profitability. Deloof (2003) argues that more profitable firms pay their
suppliers earlier, while Garcia-Teruel and Martinez-Solano (2007) suggest that higher AP
should lead to increased firm profitability because capital will be kept within firms resulting
in lower capital costs and higher internal financing.
Therefore, in this thesis, there is an expected negative relationship between CCC and firm
profitability, an expected negative relationship between INV and firm profitability, an
expected negative relationship between AR and firm profitability, and an expected positive
relationship between AP and firm profitability. To examine the relationship between working
capital efficiency and firm profitability the same hypotheses as Enqvist et al. (2014) are
tested. The hypotheses are formulated:
18
Table 3: Hypotheses group 1
Hypothesis Prediction
Hypothesis 1 There is a negative relationship between CCC and firm profitability
Hypothesis 2 There is a negative relationship between INV and firm profitability
Hypothesis 3 There is a negative relationship between AR and firm profitability
Hypothesis 4 There is a positive relationship between AP and firm profitability
Working capital management has been discussed to be affected by economic fluctuations
(Einarsson & Marquis 2001; Braun & Larrain 2005). In addition, CCC, INV, AR and AP
have been suggested to depend on the economic environment (Merville & Tavis 1973;
Filbeck & Krueger 2005; Enqvist et al. 2014). An interpretation of this is that firms must take
economic fluctuations in consideration if they want to optimize their working capital
management. Enqvist et al. (2014) suggested that the relationship between working capital
efficiency and firm profitability is more pronounced in economic downturns. To examine the
impact of economic fluctuations on the relationship between working capital efficiency and
firm profitability the same hypotheses as Enqvist et al. (2014) are tested. The hypotheses are
formulated:
Table 4: Hypotheses group 2
Hypothesis Prediction
Hypothesis 5a The significance of the relationship between CCC and firm profitability increases
during economic downturns
Hypothesis 5b The significance of the relationship between CCC and firm profitability decreases
during economic booms
Hypothesis 6a The significance of the relationship between INV and firm profitability increases
during economic downturns
Hypothesis 6b The significance of the relationship between INV and firm profitability decreases
during economic booms
Hypothesis 7a The significance of the relationship between AR and firm profitability increases during
economic downturns
Hypothesis 7b The significance of the relationship between AR and firm profitability decreases during
economic booms
Hypothesis 8a The significance of the relationship between AP and firm profitability increases during
economic downturns
Hypothesis 8b The significance of the relationship between AP and firm profitability decreases during
economic booms
19
3. Methodology
The aim of the thesis is to examine the relationship between working capital efficiency and
firm profitability, and how this relationship is affected by economic fluctuations. In addition,
there is also a methodological discussion in regards to the use of replication in business
research. In this section, the methodological approach of this thesis is presented. Firstly,
replication as a research method is discussed in order to provide a foundation and
understanding for replication as a scientific method as well as motivating the relevance of
replication in this specific case. Secondly, a summarization of the methodology of Enqvist et
al. (2014) is presented. This is a necessity, as it provides the background of the frame of
replication of this thesis. Thirdly, the operationalization of the relationship between working
capital efficiency and firm profitability in regards to economic fluctuations is discussed.
Fourthly, the variables of the study are presented, defined and discussed. The variable
discussion is concluded with a presentation of the regression models. Lastly, the data is
presented and discussed.
3.1 Replication
Replication forms an essential part of the scientific method (Dewald, Thursby & Anderson
1986). While replication is common practice in the natural sciences, there has been a
reluctance to conduct replication studies in the social sciences (Evanschitzky, Baumgarth,
Hubbard & Armstrong 2007). Common arguments against replication studies, within the
social sciences, are that researchers will not be awarded for replicating other researchers’
findings, that researchers conducting replication studies lack imagination or that replication
studies are a reflection of lack of trust in the original study (Dewald et al. 1986; Bryman &
Bell 2015 p. 50). While these are valid concerns, they do not form enough support to discard
replication as a necessary part of the scientific method. Mittelsteadt and Zorn (1984) conclude
that what cannot be replicated is not worth knowing. They mean that both confirmation and
disconfirmations contribute to the existing knowledge. Confirmations increase the
generalizability of the findings of previous studies while disconfirmations propose that new
approaches must be tested.
Bettis et al. (2016) call for more replication studies for reasons based on both business
practice and research. What business practice is concerned, business research studies
oftentimes give implications for business practice. Single empirical studies are bound to the
specific context of the study and cannot establish whether the findings can be generalized to
20
other contexts. Replication, on the other hand, can amass an accumulated body of knowledge
that better supports the implications from business research to business practice. What
business research is concerned, replication in its nature, tests the replicability of prior studies
and if they can be reproduced in different contexts. An interpretation of those arguments holds
that, in order to accumulate knowledge, replication studies are crucial. If studies and findings
are not tested or replicated, there are only one-shot studies, and practitioners and researchers
alike should request more support before basing decisions on untested results (Evanschitzky
et al. 2007).
Evanschitzky et al. (2007) noted that replication studies saw an increasing trend during the
years 1974-1989 but that there was a slow down during the years 1990-2004. As a result, the
editorial policies of leading journals started to encourage more replication studies.
Duvendack, Palmer-Jones and Reed (2015) noted an up going trend in replication studies
from the early 2000s and forwards. However, they conclude that replication studies are still a
small element in the social sciences.
3.1.1 The original study: Methodological review
The study of Enqvist et al. (2014) “The impact of working capital management on firm
profitability in different business cycles: Evidence from Finland” was published in Research
in International Business and Finance. Research in International Business and Finance seek to
highlight the interaction between finance and broader societal concerns. This affects the focus
of the journal and the studies that are published. Research in International Business and
Finance is an American based journal, ranked 458 of 1370 by Scimago Journal & Country
Rank in the category of business, management and accounting (Scimagojr 2017).
Enqvist et al. (2014) used a quantitative method in their study. Using a sample of non-
financial listed Finnish firms, they examined the relationship between working capital
efficiency and firm profitability and how it is affected by business cycles. The data was
collected for the years 1990-2008 from the Research Institute of the Finnish Economy. The
sample consisted of an unbalanced panel of 1,136 firm-year observations. To study the
relationship between working capital efficiency and firm profitability, and how it is affected
by business cycles, multiple regression analysis was conducted. The variables used in the
study were ROA, GOI, CCC, INV, AR, AP, CR, SIZE and DEBT. Working capital efficiency
was operationalized as CCC and its individual components. Firm profitability was
21
operationalized as ROA and GOI respectively. CCC, INV, AR and AP were the independent
variables, while ROA and GOI were proxies of the dependent variable firm profitability. The
control variables in the study were CR, SIZE and DEBT. The regression models were also
controlled for influences of year and industry. Business cycles were captured using changes in
annual GDP growth, and the five years showing the lowest respectively the highest annual
GDP growth forms two different states of the economy used to capture the influences of
business cycles. As such, two dummy variables were included in the study to compare the
different states of the economy with the full time period. D1 captured economic downturns
and D2 captured economic booms. The hypotheses were formulated as in this thesis (see table
3 and table 4) and were tested at the 1%, 5% and 10% significance levels (Enqvist et al.
2014).
3.1.2 Thesis frame
In the original study, Enqvist et al. (2014) draw conclusions with implications for both
business practice and research. However, these findings must be tested further in order to
build a cumulative body of knowledge. With a focus on another geographical setting, another
time frame and different types of firms, this thesis contributes to the understanding of the
relationship between working capital efficiency and firm profitability. While the relationship
between working capital efficiency and firm profitability has been extensively researched,
how this relationship is affected by economic fluctuations is less researched. Therefore, it is
important to further examine the impact of economic fluctuations on the relationship between
working capital efficiency and firm profitability. By replicating the study of Enqvist et al.
(2014), the replicability of their study and the generalizability of their findings can be tested
in another context. This approach adds to the understanding of the relationship between
working capital efficiency and firm profitability and how this relationship is affected by
economic fluctuations.
Bettis et al. (2016) point at the importance to match the research design of the original study
as closely as possible. The reasoning behind is, that it is crucial to calibrate the replication
with the original study. If this cannot be done, it is difficult to build a cumulative body of
knowledge. For this reason, this thesis employs the same method as Enqvist et al. (2014) to
the extent it is possible. The same theoretical starting points are used, the same statistical
approach is conducted and the hypotheses are formulated and tested in the same manner.
22
However, in order to contribute to the cumulative body of knowledge, the replication is done
in another context. The replication is conducted in another geographical setting during another
time period studying different types of firms. In addition, there is a deeper theoretical
discussion of the relationship between working capital efficiency and firm profitability. This
will increase the understanding of how working capital efficiency affects firm profitability in
a broader context, and more specifically, how this relationship is influenced by economic
fluctuations.
3.2 Operationalization
In accordance with the reasoning of Bettis et al. (2016), this thesis tries to match the original
study to the extent it is possible. Therefore, the hypotheses in this thesis are formulated and
tested in the same manner as in the study of Enqvist et al. (2014). Multiple regression analysis
is conducted in order to examine the relationship between working capital efficiency and firm
profitability. Working capital efficiency is measured using the CCC and firm profitability is
measured by ROA and GOI respectively. More specifically, there are two groups of
hypotheses. The first group tests the relationship between working capital efficiency and firm
profitability for the entire time period. In so doing, the CCC and its individual components are
tested against ROA and GOI. The second group tests how this relationship is affected by
economic fluctuations. Economic fluctuations are captured as changes in annual GDP growth
around its long-term trend. Firm profitability is the dependent variable and ROA and GOI are
used as proxies of firm profitability. However, firm profitability may be explained by other
factors. For this reason, control variables are included in the analysis. The control variables
are CR, SIZE and DEBT. In addition, the regression models also control for the influence of
firm, year and industry. The independent variables are CCC, INV, AR and AP and thus
measures working capital efficiency.
In order to examine how the relationship between working capital efficiency and firm
profitability is affected by economic fluctuations, changes in annual GDP growth around its
long-term trend has been used to categorize different states of the economy. The states of the
economy are categorized by the five years showing the lowest respectively the highest annual
GDP growth, and are then compared to the entire time period. The five years showing the
lowest annual GDP growth are referred to as economic downturns. The five years showing the
highest annual GDP growth are referred to as economic booms. D1 is the dummy variable for
23
the conditions of economic downturns and D2 is the dummy variable for the conditions of
economic booms.
3.3 Variable selection
In regression analysis, the relationship between two or more variables is studied in order to
assess whether the variation in one can explain the variation in the other. In multiple
regression analysis, there is more than one independent variable. The independent variables
are the variables that explain the variation in the dependent variable. As such, multiple
regression analysis examines the causal relationship between the dependent variable and the
independent variables. In this section, each of the variables used in this study are presented.
For a summarization of the variables and how they have been defined, see appendix B.
3.3.1 Dependent variable
The dependent variable in this thesis is firm profitability. Firm profitability is measured as
both ROA and GOI. The variation in the dependent variable is assumed to depend on the
variation in the independent variables. ROA has been the most commonly used measure of
firm profitability in the research tradition of working capital efficiency (e.g. Wang 2002;
Garcia-Teruel & Martinez-Solano 2007). In addition, GOI has been used as an alternative
measure of firm profitability (Deloof 2003; Lazaridis & Tryfonidis 2006) or as a
complementary measure in combination with ROA (Enqvist et al. 2014).
3.3.1.1 Return on assets
ROA is an overall indicator of firm profitability (Padachi 2006). As such, ROA avoids capital
structure differences because it does not take into account how the assets of firms are
comprised (Enqvist et al. 2014). In line with the research tradition of working capital
efficiency (e.g. Padachi 2006; Garcia-Teruel & Martinez-Solano 2007; Enqvist et al. 2014),
ROA is calculated as the ratio between net income and total assets.
3.3.1.2 Gross operating income
Deloof (2003) argues that ROA is unfit as a measure of firm profitability in samples
containing firms with high levels of financial assets. Instead he used GOI1 as the measure of
firm profitability. Lazaridis and Tryfonidis (2006) suggested that GOI is a more suitable
1 Gross operating income has been used in the research tradition of working capital efficiency (e.g. Deloof 2003)
and the definition used in this thesis is in line with this research tradition. However, the authors of this thesis are
aware of a potential conceptual confusion as gross operating income has the characteristics of a margin.
24
measure of firm profitability in regards to working capital efficiency. This because GOI is an
operating variable. As such, GOI measures the operational performance of firms (Enqvist et
al. 2014). In line with the research tradition (e.g. Deloof 2003; Lazaridis & Tryfonidis 2006;
Enqvist et al. 2014), GOI is defined as revenue adjusted for cost of goods sold divided by total
assets minus financial assets.
3.3.2 Independent variables
The independent variables in this thesis are used in order to measure working capital
efficiency. Thus, working capital efficiency is operationalized as CCC, INV, AR and AP. The
variation in the independent variables is assumed to explain the variation in firm profitability.
This is the standard approach in previous research (e.g. Jose et al. 1996; Deloof 2003).
3.3.2.1 Cash conversion cycle
Gitman (1974) introduced the CCC as a measure of working capital efficiency. It is a dynamic
measure of working capital efficiency (Jose et al. 1996). In contrast to static measures, it
captures the time lag between the purchases of inputs to the collection of sales of finished
products (Eljelly 2004). The CCC consists of INV, AR and AP and, as such, is a measurement
combining data from both balance sheet and income statement with a time dimension (Jose et
al. 1996). The CCC is measured in days and is calculated as INV plus AR minus AP.
3.3.2.2 Days of inventory
INV measures the average number of days of inventory held by a firm. An interpretation of
this is that it captures how long it takes for a firm to turn its inventory into sales (Jose et al.
1996). Longer INV reflects more capital bound in inventory for a particular level of
operations (Garcia-Teruel & Martinez-Solano 2007). Therefore, lower INV indicates higher
working capital efficiency (Enqvist et al. 2014). INV is calculated as the ratio between
inventory and cost of goods sold multiplied with 365 days.
3.3.2.3 Days of accounts receivables
AR measures the average number of days it takes for a firm to collect payments for sales
outstanding. Higher AR means that more capital is bound in accounts receivables (Jose et al.
1996). Therefore, lower AR indicates higher working capital efficiency (Enqvist et al. 2014).
AR is calculated as the ratio between accounts receivables and revenue multiplied with 365
days.
25
3.3.2.4 Days of accounts payables
AP reflects the average time it takes for firms to pay their suppliers. The higher the AP, the
longer it takes for firms to settle their payment commitments with their suppliers (Garcia-
Teruel & Martinez-Solano 2007). From a theoretical point of view, higher AP is assumed to
enhance firm profitability because capital is kept within the firm and thus lowers the capital
cost (Jose et al. 1996). AP is calculated as the ratio between accounts payables and cost of
goods sold multiplied with 365 days.
3.3.3 Dummy variables
In line with Enqvist et al. (2014), dummy variables are used in order to analyze the impact of
economic fluctuations on the relationship between working capital efficiency and firm
profitability. Two dummy variables are used to distinguish how the relationship between
working capital and firm profitability is affected by economic fluctuations. Economic
fluctuations are measured as changes in annual GDP growth around its long-term trend for the
years 2000-2015. In line with Enqvist et al. (2014), the dummy variables used in this study
capture the five years with the lowest respectively the highest annual GDP growth. As such,
dummy variable 1 (D1) captures the five years with the lowest annual GDP growth and
dummy variable 2 (D2) captures the five years with the highest annual GDP growth.
3.3.4 Control variables
In order to control that the variation in the dependent variable do not depend on factors other
than the independent variables, control variables are included in all regression models. In line
with Enqvist et al. (2014), the control variables used in this study are CR, SIZE and DEBT.
These control variables have been shown to impact firm profitability (Shin & Soenen 1998;
Koralun-Bereźnicka 2014; Enqvist et al. 2014). In addition, the regression models control for
firm, year and industry.
3.3.4.1 Current ratio
Wang (2002) and Eljelly (2004) suggest that working capital management is concerned with
firm liquidity and that firm liquidity ultimately affects firm profitability. In addition, a number
of studies have shown that CR is associated with firm profitability (e.g. Shin & Soenen 1998).
For this reason, CR is included to control for the impact of liquidity on firm profitability. CR
is calculated as the ratio between current assets and current liabilities.
26
3.3.4.2 Firm size
SIZE is one of the most important determinants of working capital (Koralun-Bereźnicka
2014). In previous research in the field of working capital management, revenues or sales has
normally been used to capture SIZE. However, to make it comparable, previous research has
used the natural logarithm of sales (e.g. Deloof 2003; Enqvist et al. 2014). Therefore, in this
thesis SIZE is measured as the natural logarithm of sales.
3.3.4.3 Firm debt ratio
Working capital efficiency has been suggested to both impact and be impacted by the
financing of firms (Filbeck & Krueger 2005). In addition, how firms are financed has been
suggested to affect firm profitability (Braun & Larrain 2005). DEBT measures the relation
between external financing of the firm and its total assets (Lazaridis & Tryfonidis 2006).
DEBT is calculated as the sum of short term loans and long term loans divided by total assets.
3.3.4.4 Firm, year and industry
Using firm-year observations means using panel data in the regression analysis. For this
reason, firm and year effects have been controlled for. In addition, working capital has been
suggested to be dependent on industry (e.g. Jose et al. 1996; Shin & Soenen 1998; Niskanen
& Niskanen 2006). Therefore, industry has been controlled for. Industry was coded in
accordance with the industry categorization of the Stockholm Stock Exchange (Nasdaq 2017).
3.4 Regression models
To examine how the relationship between working capital efficiency and firm profitability
was affected by economic fluctuations, ordinary least squares (OLS) regression analysis was
conducted. The data used in this thesis consists of an unbalanced panel over a 16-year period.
Therefore, the regression models were specified with clustered robust standard errors around
firm ID. The dependent variable was measured as both ROA and GOI. Four independent
variables were tested, why four regression models are used for each firm profitability
measurement. In addition, the regression models are controlled for CR, SIZE and DEBT as
well as for year and industry effects. The regression models are used to assess if the variation
in the independent variables explain the variation in the dependent variable. The regression
models were formulated:
27
Firm profitability = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Firm profitability = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Firm profitability = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Firm profitability = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
In the regression models above, the dependent variable, firm profitability, is measured by both
return on assets (ROA) and gross operating income (GOI). The constituents of working
capital efficiency, the independent variables, are measured by the cash conversion cycle
(CCC), days of inventory (INV), days of accounts receivables (AR) and days of accounts
payables (AP). The dummy variables are economic downturns (D1) and economic booms
(D2). The control variables are current ratio (CR), the natural logarithm of sales (SIZE) and
firm debt ratio (DEBT) while also controlling for year effects (YEAR) and industry effects
(INDUSTRY). β is coefficient term and ε is the error term.
3.5 Data
The data collection was done through Thomson Reuters Datastream. As such, the data used in
this thesis is secondary. By using secondary data, a satisfying amount of data can be collected
in order to fulfill the aim of the thesis. However, collecting secondary data from a database
has problems that need to be identified and understood in order to conduct a proper analysis
(Bryman & Bell 2015 p. 328-329). Thomson Reuters Datastream contains data from active
firms. This may lead to a survival bias, as the data sample will not include firms that no
longer exist, have been acquired by other firms, have merged with other firms or are no longer
publicly traded on the Stockholm Stock Exchange. An effect of this could be that the sample
is biased towards firms with higher performance. This effect should be greater the farther
back in time the data is collected because the available data is diminishing from year 2015
and downwards. In the regression models, this has been taken into consideration and the
potential impact has been limited through control for firm and year effects. In addition,
secondary data is not necessarily standardized and different accounting practices lead to
different definitions and reporting practices of financial data. This unavoidably leads to a
certain degree of errors in the collected data. For this reason, the data sample has been
28
screened and cleaned from missing values and extreme values. Real GDP data was collected
from Statistics Sweden for the years 2000-2015.
The time period of this thesis is the 16-year period between 2000-2015. The reason behind the
choice of these years is three folded. Firstly, the years 2000-2015 covers three economic
downturns with significant impact on the Swedish annual GDP growth. Secondly, the years
2000-2015 are as recent as possible as 2015 is the latest year with published accounting data
for listed Swedish firms. Thirdly, the survival bias discussed above increases the farther back
in time the data is collected. 2000-2015 is balancing economic fluctuations and the potential
survival bias in a satisfying manner. It provides the opportunity to examine the impact of
economic fluctuations on the relationship between working capital efficiency and firm
profitability, while also minimizing the impact of the survival bias in the database. However,
it must be noted that the firm-year observations diminish gradually from year 2015 down to
year 2000. As such, the data is an unbalanced panel over the 16-year period between 2000-
2015.
Figure 1: Annual GDP growth (%) of Sweden 2000-2015
Source: Statistics Sweden (2017)
The annual GDP growth for the years 2000-2015 is shown in figure 1 above. It provides the
data on which the different states of the economy are based. In line with Enqvist et al. (2014),
the five years with the lowest annual GDP growth are categorized as D1 and the five years
with the highest annual GDP growth are categorized as D2. These dummy variables are then
-6
-4
-2
0
2
4
6
8
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
29
compared to the results for the whole time period 2000-2015. Table 5 below shows how the
categorization of years in D1 and D2 has been done.
Table 5: Classification of economic downturns and economic booms
Economic downturns (D1) Economic booms (D2)
Year Annual GDP
growth (%)
Year Annual GDP
growth (%)
2001 1.6 2000 4.7
2008 -0.6 2004 4.3
2009 -5.2 2006 4.7
2012 -0.3 2010 6.0
2013 1.2 2015 4.1
Source of annual GDP growth (%): Statistics Sweden (2017)
3.5.1 Population and sample
The aim of this thesis is to examine how the relationship between working capital efficiency
and firm profitability is affected by economic fluctuations among listed Swedish firms. Thus,
the target population of the thesis is listed Swedish firms. More precisely, the constituents of
OMXSPI on the Stockholm Stock Exchange. OMXSPI consists of the firms listed on Large
Cap, Mid Cap and Small Cap on the Stockholm Stock Exchange. From Thomson Reuters
Datastream, financial data from the constituent firms of OMXSPI was collected. The
extracted database consisted of 325 yearly observations and a total of 5,200 observations. In
order to conduct proper statistical analysis, the data was screened and cleaned from missing
values and extreme values because these might distort the results (Pevalin & Robson 2009 p.
289). After adjusting for missing data, 2,666 observations remained in the sample. Thereafter,
the remaining sample was screened for outliers and extreme values. The screening was done
for all variables. From the sample 77 observations were excluded because of theoretically
unrealistic values that would have a significant impact on the results. As a result, the sample
used in the statistical analysis consists of 2,589 firm-year observations, which is 49.8% of the
original sample. The remaining sample constitutes an unbalanced panel of 2,589 firm-year
observations. Figure 2 below shows the yearly distribution of observations.
30
Figure 2: Number of observations per year
3.5.2 Statistical tools
The data was prepared and processed in Microsoft Excel 2016 and the statistical data analysis
was conducted in Stata 14. Microsoft Excel was used to prepare the extracted data into a
database usable in Stata. Stata was used to screen and clean the data and to conduct the
statistical data analysis. The statistical data analysis conducted in this study was descriptive
statistics, correlations and multiple regression analysis. In addition, the statistical testing of
the sample and the regression models were done in Stata. Descriptive statistics were used to
show the features and the characteristics of the variables in the sample, correlations were used
to show how the variables in the sample correlate and multiple regression analysis was
conducted to assess the causal relationship between the dependent variable and the
independent variables. Statistical testing was done to show that the data was appropriate to
use in the multiple regression analysis.
3.5.3 Descriptive statistics
Table 6: Descriptive statistics full sample
Variable Mean SD Min Max
CCC 87.485 74.121 -299.582 596.012
INV 69.407 64.739 0 439.379
AR 71.882 41.991 0.839 487.189
AP 53.803 46.049 0 393.451
ROA 0.028 0.164 -1.186 0.767
GOI 0.320 0.312 -1.023 4.662
CR 2.012 1.840 0.143 26.632
SIZE 14.422 2.143 6.921 19.560
DEBT 0.469 0.192 0.007 1.408 Notes: Descriptive statistics for the sampled variables for the years 2000-2015, for a total of number of 2,589 observations.
0
50
100
150
200
250
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
31
Table 6 above presents the descriptive statistics of the variables in the full sample used in the
statistical analysis. For the descriptive statistics of the economic states referred to as economic
downturns and economic booms, see appendix C.
For the period 2000-2015, the data shows an average CCC of 87.5 days for the sampled firms.
In contrast to Enqvist et al. (2014), the average CCC is considerably lower than listed Finnish
firms (108.8 days). In regards to other studies, the average CCC is higher than the findings of
Deloof (2003) among Belgian firms (44.5 days) and the findings of Yazdanfar and Öhman
(2014) among Swedish SMEs (47.3 days). However, the average CCC is considerably lower
than the findings of Jose et al. (1996) among listed US firms (164 days) and the findings of
Lazaridis and Tryfonidis (2006) among listed Greek firms (189 days). The average CCC is
somewhat matching the findings of Garcia-Teruel and Martinez-Solano (2007) among
Spanish SMEs (76.3 days) and the findings of Gill et al. (2010) among listed US firms (90
days).
INV, AR and AP in the sample are 69.4 days, 71.9 days and 53.8 days respectively. The
average INV is considerable lower than the findings of Enqvist et al. (2014) who found an
average INV of 117.6 days. The average AR too differs considerably from the findings of
Enqvist et al. (2014) who found an average AR of 47.6 days. Average AP is similar to the
findings of Enqvist et al. (2014) who found an average AP of 56.4 days. In regards to other
studies in different contexts, the average INV, AR and AP are somewhat matching (e.g.
Garcia-Teruel & Martinez-Solano 2007; Gill et al. 2010).
For the profitability measures, ROA is on average 2.8% and GOI is on average 32% for the
sampled firms for the years 2000-2015. Both profitability measures in this sample are
considerably lower than in the study of Enqvist et al. (2014) who found an average ROA of
8.4% and an average GOI of 101%. In comparison to other studies, the ROA in our sample is
considerably lower (e.g. Garcia-Teruel & Martinez-Solano 2007; Yazdanfar & Öhman 2014).
The average GOI exceeds the findings of Deloof (2003) for listed Belgian firms of 12.2% and
is similar to the findings of Gill et al. (2010) of 30% among listed US firms.
The difference between studies can be derived from the setting of the studies. Different
studies are conducted in different countries, studying different types of firms, different time
periods and using different variables. All these factors have been suggested to affect working
32
capital management and firm profitability. For a more detailed review of previous research,
see appendix A.
3.5.4 Correlation matrix
Table 7: Pearson’s correlation matrix
ROA GOI CCC INV AR AP CR SIZE DEBT
ROA
GOI 0.383***
CCC -0.079*** -0.143***
INV -0.048** 0.079*** 0.707***
AR -0.248*** -0.208*** 0.421*** 0.015
AP -0.166*** 0.152*** -0.231*** 0.281*** 0.255***
CR 0.034** -0.069*** 0.242*** 0.159*** 0.167*** -0.014
SIZE 0.308*** 0.137*** -0.048** 0.028 -0.238*** -0.099*** -0.321***
DEBT -0.097*** 0.0461** -0.199*** -0.172*** -0.075*** 0.010 -0.545*** 0.327***
ROA 0.387*** -0.008 0.037 -0.268*** -0.153*** 0.056 0.284*** -0.081**
GOI 0.319*** -0.130*** 0.082** -0.203*** 0.120*** -0.054 0.107*** 0.036
CCC -0.087** -0.161*** 0.679*** 0.285*** -0.316*** 0.116*** 0.041 -0.148***
INV -0.111*** 0.037* 0.713*** -0.052 0.252*** 0.118*** 0.073** -0.166***
AR -0.222** -0.189*** 0.462*** 0.085** 0.330*** 0.034 -0.199*** -0.036
AP -0.200*** 0.122*** -0.252*** 0.290*** 0.219*** 0.013 -0.122*** -0.029
CR 0.044 -0.081** 0.330*** 0.227*** 0.245*** -0.015 -0.326*** -0.527***
SIZE 0.281*** 0.113*** -0.066* 0.002 -0.234*** -0.089*** -0.309*** 0.329***
DEBT -0.131*** 0.054 -0.233*** -0.219*** -0.082** 0.008 -0.333*** 0.322***
The table shows Pearson’s correlation matrix for the full sample, economic downturns and economic booms. In
the table, the top correlation matrix represents the full sample, the middle represents economic booms and the
bottom represents economic downturns. ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *.
Significant at the 0.1 level. Full sample observations: 2,589, economic downturns: 847 and economic booms:
779.
Table 7 above presents the correlation matrix of the variables. For the full sample, CCC has a
negative significant correlation with both ROA (-0.079***) and GOI (-0.143***). During
economic downturns, CCC has a significant negative correlation with both ROA (-0.087**)
and GOI (-0.161***). During economic booms, CCC has an insignificant negative correlation
with ROA (-0.008) and a significant negative correlation with GOI (-0.130***). Thus, there
are slight differences between the full sample and the different economic states. However,
there are indications that the correlation is stronger in economic downturns, than in the full
sample and economic booms. In addition, the correlation is stronger between CCC and GOI
than between CCC and ROA.
33
For the full sample, INV has a significant negative correlation with ROA (-0.048***) and a
significant positive correlation with GOI (0.079***). During economic downturns, INV has a
significant negative correlation with ROA (-0.111***) but a positive correlation with GOI
(0.037*). During economic booms, INV has an insignificant positive correlation with ROA
(0.037) and a significant positive correlation with GOI (0.082**). Thus, there is varying and
low correlations between INV and both ROA and GOI. There are indications that the
correlation between INV and firm profitability is stronger in economic downturns when firm
profitability is measured as ROA. However, this is not the case for GOI. The correlations
between INV and both ROA and GOI are varying and low.
For the full sample, AR has a significant negative correlation with both ROA (-0.248***) and
GOI (-0.208***). During economic downturns, AR has a significant negative correlation with
ROA (-0.222**) and a significant negative correlation with GOI (-0.189***). During
economic booms, AR has a significant negative correlation with ROA (-0.268***) and a
significant negative correlation with GOI (-0.203***). Thus, there is a consistently moderate
negative correlation between days of AR and both ROA and GOI. However, the negative
correlations are stronger in economic booms than in economic downturns.
For the full sample, AP has a significant negative correlation with ROA (-0.166***) but a
significant positive correlation with GOI (0.152***). During economic downturns, AP has a
significant negative correlation with ROA (-0.200***) but a significant positive correlation
with GOI (0.122***). During economic booms, AP has a significant negative correlation with
ROA (-0.153***) but a significant positive correlation with GOI (0.120***). Thus, there is a
difference between how AP correlates with the ROA and GOI. ROA has a consistently
negative relationship and GOI has a consistently positive relationship. The correlation is
stronger in economic downturns than for the full sample when firm profitability is measured
as ROA, but not when measured as GOI.
3.5.5 Data testing
In order to conduct OLS regression, the sample needs to fulfill certain criteria (Pevalin &
Robson 2009 p. 288-289). Firstly, the sample size needs to be large enough in order to make
the regression replicable and thus the results generalizable. The sample size in this thesis is in
line with previous research in the research field (e.g. Deloof 2003; Enqvist et al. 2014).
Secondly, OLS regression is sensitive towards outliers and extreme values. These have been
34
checked and removed from the sample (see appendix D.1). However, in this thesis we have
chosen to remove as few observations as possible. The reason for this is that there is a
discrepancy between reality and stylized theoretical examples. While some observations may
be interfering statistically, they are still actual outcomes. This lowers the strength of the
statistical analysis but is a conscious choice. Thirdly, OLS regression requires that the
association between the independent variables and the dependent variable is linear. The
variables have been tested for linearity and this criterion is fulfilled (see appendix D.2).
Fourthly, OLS regression requires that the residuals are normally distributed. Normal
distribution has been checked for and the residuals are normally distributed in all regression
models (see appendix D.3). Fifthly, OLS regression requires that the variance of the residuals
is constant. This has been checked for and the variance of the residuals show
homoscedasticity which means that the variance of the residuals is constant (see appendix
D.4). Lastly, OLS regression requires that the independent variables are tested for
multicollinearity. This assumption has been tested through the VIF-test and multicollinearity
does not impact the regression models (see appendix D.5). Using OLS regression on panel
data requires that there is no autocorrelation between firm-year observations. This has been
controlled for in all regression models by clustering the regression models around robust
standard errors for firm ID and controlling for year effects. To test the robustness of the
regression models, several robustness checks were conducted. In line with Garcia-Teruel and
Martinez-Solano (2007) endogeneity was identified as a potentially distorting effect. This
effect was minimized by including control variables and also measuring firm profitability
using two different profitability measurements. In addition, the regression models were run
with and without control variables and showed consistent results with the regression models
used in the final statistical data analysis. This increases the robustness of the regression
models and also minimized the risk of overfitting in the regression models.
35
4. Results
In this section, the results of the regression analysis and the hypothesis testing are presented.
Firstly, the result of the regression analysis with ROA as the proxy of firm profitability is
presented. Secondly, the result of the regression analysis with GOI as the proxy of firm
profitability is presented. Working capital efficiency is measured as CCC and its individual
components. Lastly, the results of the hypothesis testing are presented.
4.1 Regression analysis: Return on assets
The impact of working capital efficiency on ROA in different economic states was analyzed
to assess how the relationship between working capital efficiency and ROA was affected in
the different economic states. Below are the regression models for ROA:
Model (CCC): ROA = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +
β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (INV): ROA = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti +
β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (AR): ROA = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (AP): ROA = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Table 8 below, presents the results of the regression analysis for ROA. The explanatory levels
(R-square) of the regression models are somewhat similar as in the study of Enqvist et al.
(2014). Model (CCC) examines the relationship between CCC and ROA and the results show
that Model (CCC) explains 20% of the variation in ROA. The results show a significant
negative relationship between CCC and ROA. This result is in line with the result of Enqvist
et al. (2014). At first sight, this result may seem low. However, it means that a decrease of one
day of CCC would lead to an increase in ROA of 0.037%. This is a considerable increase of
ROA. The strength of the relationship between CCC and ROA decreases significantly in both
economic downturns (D1*CCC) and economic booms (D2*CCC) in relation to the full
sample. In addition, there are no indications that there are any differences between economic
downturns (D1*CCC) and economic booms (D2*CCC). ROA is more pronounced in D2 in
relation to D1.
36
Table 8: Regression analysis ROA
Dependent: ROA
Coefficient
estimator
Expected
Sign
Model
(CCC)
Model
(INV)
Model
(AR)
Model
(AP)
Constant -0.34970***
(0.03612)
-0.35543***
(0.03783)
-0.27202***
(0.0353)
-0.31206***
(0.04534)
D1 - -0.02679*
(0.01585)
-0.00014
(0.01448)
-0.03608*
(0.01808)
-0.01864
(0.0162)
D2 + -0.05583*
(0.02803)
-0.05999*
(0.02591)
-0.02832
(0.02628)
-0.04895*
(0.02384)
CCC - -0.00037***
(0.00008)
(D1*CCC) - 0.00005
(0.00011)
(D2*CCC) + 0.00015
(0.00012)
INV - -0.0002*
(0.0002)
(D1*INV) - -0.00014
(0.00014)
(D2*INV) + 0.0002
(0.00013)
AR - -0.00072***
(0.00018)
(D1*AR) - 0.00017
(0.00022)
(D2*AR) + -0.00009
(0.00025)
AP + -0.00037*
(0.00022)
(D1*AP) + -0.00006
(0.00024)
(D2*AP) - 0.00006
(0.0003)
CR 0.00848***
(0.00233)
0.00682**
(0.00241)
0.00797**
(0.00229)
0.00574*
(0.0024)
SIZE 0.02993***
(0.00231)
0.0306***
(0.00237)
0.02725***
(0.00216)
0.0298***
(0.00239)
DEBT -0.16443***
(0.02133)
-0.16351***
(0.02203)
-0.14505***
(0.02164)
-0.15428***
(0.02174)
R-square 0.20 0.19 0.21 0.18
F-value 10.32 9.44 12.34 13.18
N 2,589 2,589 2,589 2,589
Year Included Included Included Included
Industry ID Included Included Included Included
Clustered around
Firm ID
Yes Yes Yes Yes
The table reports the result of the estimated regression models when firm profitability is measured as ROA. D1
and D2 are dummy variables for economic downturns respectively economic booms; CCC is cash conversion
cycle; (D1*independent) and (D2*independent) are interaction variables; INV is days of inventory; AR is days
of accounts receivables; AP is days of accounts payables; CR is current ratio; SIZE; is natural logarithm of sales;
DEBT is firm debt ratio. Year and industry ID have been controlled. The regression models are clustered around
firm ID. ()- Standard deviation, ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *. Significant
at the 0.1 level.
Model (INV) examines the relationship between INV and ROA and the results show that
Model (INV) explains 19% of the variation in ROA. The results show a significant negative
relationship between INV and ROA. This result is in line with Enqvist et al. (2014). The
37
strength of the relationship between INV and ROA decreases significantly in both economic
downturns (D1*INV) and economic booms (D2*INV) in relation to the full sample. However,
there are indications that the relationship is stronger in economic downturns (D1*INV) than in
economic booms (D2*INV). ROA is more pronounced in D2 in relation to D1.
Model (AR) examines the relationship between AR and ROA and the results show that Model
(AR) explains 21% of the variation in ROA. The results show a significant negative
relationship between AR and ROA. This result is in line with Enqvist et al. (2014). However,
Enqvist et al. (2014) do not find a significant relationship between AR and ROA. The strength
of the relationship between AR and ROA decreases significantly in both economic downturns
(D1*AR) and economic booms (D2*AR) in relation to the full sample. In addition, there are
no significant differences between economic downturns (D1*AR) and economic booms
(D2*AR). However, there are indications that the relationship between AR and ROA is
stronger in economic booms (D2*AR) than in economic downturns (D1*AR). ROA is more
pronounced in D1 in relation to D2.
Model (AP) examines the relationship between AP and ROA and the results show that Model
(AP) explains 18% of the variation in ROA. The results show a significant negative
relationship between AP and ROA. This result is in line with Enqvist et al. (2014). However,
Enqvist et al. (2014) do not have a significant relationship between AP and ROA. The
strength of the relationship between AP and ROA decreases significantly in both economic
downturns (D1*AP) and economic booms (D2*AP) in relation to the full sample. In addition,
there are no significant differences between economic downturns (D1*AP) and economic
booms (D2*AP). However, there are indications that the relationship between AP and ROA is
stronger in economic booms (D2*AP) than in economic downturns (D1*AP). ROA is more
pronounced in D2 in relation to D1.
The regression models for ROA as a proxy of firm profitability are influenced by the control
variables. There is a significant positive relationship between CR and ROA in all models, this
indicates that firms can increase ROA by increasing the margin of liquidity. In addition, there
is also a significant positive relationship between SIZE and ROA. Between DEBT and ROA
there is a significant negative relationship, this indicates that higher leverage of firms
negatively affects ROA.
38
4.2 Regression analysis: Gross operating income
The impact of working capital efficiency on GOI in different economic states was analyzed to
assess how the relationship between working capital efficiency and GOI were affected in the
different economic states. Below are the regression models for GOI:
Model (CCC): GOI = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +
β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (INV): GOI = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (AR): GOI = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Model (AP): GOI = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Table 9 below, presents the results of the regression models for GOI. The explanatory levels
(R-square) of the regression models are lower than in the study of Enqvist et al. (2014).
Model (CCC) examines the relationship between CCC and GOI and the results show that
Model (CCC) explains 8% of the variation in GOI. The results show a significant negative
relationship between CCC and GOI. This result is in line with the result of Enqvist et al.
(2014). The strength of the relationship between CCC and GOI decreases significantly in both
economic downturns (D1*CCC) and economic booms (D2*CCC) in relation to the full
sample. In addition, there are no indications that there are any differences between economic
downturns (D1*CCC) and economic booms (D2*CCC). GOI is more pronounced in D2 in
relation to D1.
Model (INV) examines the relationship between INV and GOI and the results show that
Model (INV) explains 7% of the variation in GOI. The results show an insignificant positive
relationship between INV and GOI. This result is in contradiction with Enqvist et al. (2014).
The strength of the relationship between INV and GOI increases in both economic downturns
(D1*INV) and economic booms (D2*INV) in relation to the full sample. However, the results
are insignificant. In addition, there are indications that the relationship is stronger in economic
downturns (D1*INV) than in economic booms (D2*INV). GOI is more pronounced in D2 in
relation to D1.
39
Table 9: Regression analysis GOI
Dependent: GOI
Coefficient
estimator
Expected
Sign
Model
(CCC)
Model
(INV)
Model
(AR)
Model
(AP)
Constant 0.23343***
(0.06312)
0.19752**
(0.06456)
0.45605***
(0.06377)
0.11769
(0.07726)
D1 - -0.01761
(0.04612)
-0.00583
(0.04317)
-0.02734
(0.04325)
-0.01875
(0.03417)
D2 + -0.09213*
(0.03985)
-0.11009*
(0.03857)
-0.04024
(0.04381)
-0.1036*
(0.03566)
CCC - -0.00051**
(0.00016)
(D1*CCC) - -0.00003
(0.00022)
(D2*CCC) + -0.00012
(0.00022)
INV - 0.00019
(0.00019)
(D1*INV) - -0.00019
(0.00023)
(D2*INV) + -0.00002
(0.00025)
AR - -0.0017***
(0.00024)
(D1*AR) - 0.00006
(0.00032)
(D2*AR) + -0.00055
(0.00037)
AP + 0.00061**
(0.00035)
(D1*AP) + -0.00001
(0.00028)
(D2*AP) - -0.00015
(0.00032)
CR -0.00006
(0.00338)
-0.00468
(0.0033)
0.00114
(0.00344)
-0.00398
(0.00333)
SIZE 0.01962***
(0.04621)
0.01919***
(0.00462)
0.0124**
(0.00433)
0.019923***
(0.00463)
DEBT -0.05579
(0.04725)
-0.02738
(0.04771)
-0.01419
(0.04752)
-0.0312
(0.04758)
R-square 0.08 0.07 0.12 0.07
F-value 6.29 7.01 10.16 6.66
N 2,589 2,589 2,589 2,589
Year Included Included Included Included
Industry ID Included Included Included Included
Clustered around
Firm ID
Yes Yes Yes Yes
The table reports the result of the estimated regression models when firm profitability is measured as GOI. D1
and D2 are dummy variables for economic downturns respectively economic booms; CCC is cash conversion
cycle; (D1*independent) and (D2*independent) are interaction variables; INV is days of inventory; AR is days
of accounts receivables; AP is days of accounts payables; CR is current ratio; SIZE; is natural logarithm of sales;
DEBT is firm debt ratio. Year and industry ID have been controlled. The regression models are clustered around
firm ID. ()- Standard deviation, ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *. Significant
at the 0.1 level.
Model (AR) examines the relationship between AR and GOI and the results show that Model
(AR) explains 12% of the variation in GOI. The results show a significant negative
relationship between AR and GOI. This result is in line with Enqvist et al. (2014). However,
40
the strength of the relationship between AR and GOI decreases in both economic downturns
(D1*AR) and economic booms (D2*AR) in relation to the full sample. There are no
significant differences between economic downturns (D1*AR) and economic booms
(D2*AR). There are indications that the relationship is stronger in economic booms (D2*AR)
than in economic downturns (D1*AR). GOI is more pronounced in D2 in relation to D1.
Model (AP) examines the relationship between AP and GOI and the results show that Model
(AP) explains 7% of the variation in GOI. The results show a significant positive relationship
between AP and GOI. This result is in contradiction with Enqvist et al. (2014). However, the
strength of the relationship between AP and GOI decreases in both economic downturns
(D1*AR) and economic booms (D2*AP). There are no significant differences between
economic downturns (D1*AP) and economic booms (D2*AP). There are indications that the
relationship is stronger in economic booms (D2*AP) than in economic downturns (D1*AP).
GOI is more pronounced in D2 in relation to D1.
The regression models for GOI as a proxy of firm profitability are influenced by the control
variables. There is no significant relationship between CR and GOI in the models, which
indicates that the margin of liquidity does not affect GOI. Between SIZE and GOI there is a
significant positive relationship. There is a significant negative relationship between DEBT
and GOI, this indicates that higher leverage of firms negatively affects GOI.
4.3 Hypothesis testing
The hypotheses in this thesis are divided in two groups. The first group, hypothesis 1-4, tests
the relationship between working capital efficiency and firm profitability for the full sample.
Consequently, these hypotheses test the relationship between working capital efficiency and
firm profitability. The second group, hypothesis 5-8, tests how the aforementioned
relationship is affected by economic fluctuations. More specifically, hypothesis 1-4, tests how
CCC, INV, AR and AP affect ROA and GOI respectively. Hypothesis 5-8, tests how those
relationships are affected in relation to D1 and D2. In line with previous research, the
hypotheses are tested on the 1%, 5% and 10% significance levels (e.g. Deloof 2003; Enqvist
et al. 2014). The results differ between the two proxies of firm profitability, why the
hypothesis testing has been presented for both ROA and GOI individually. This is in
contradiction to Enqvist et al. (2014) who did a weighting of ROA and GOI to answer for
firm profitability, despise varying results for ROA and GOI.
41
In table 10 below, the results of the hypothesis testing are presented for hypothesis 1-4.
Hypothesis 1-4 have different results depending on the measurement of firm profitability.
When firm profitability is measured as ROA, hypotheses 1, 2 and 3 are supported while
hypothesis 4 is rejected. When firm profitability is measured as GOI, hypotheses 1, 3 and 4
are supported while hypothesis 2 is rejected. In contrast to Enqvist et al. (2014), hypotheses
with inconclusive results have been presented if ROA and GOI show contradicting results.
Table 10: Hypothesis testing group 1
Hypothesis Prediction Result Enqvist et al. (2014)
Result
ROA GOI Overall ROA GOI Overall
Hypothesis
1
There is a negative
relationship between CCC
and firm profitability
Support Support Support Support Support Support
Hypothesis
2
There is a negative
relationship between INV
and firm profitability
Support Reject Inconclusive Support Support Support
Hypothesis
3
There is a negative
relationship between AR
and firm profitability
Support Support Support Reject Reject Reject
Hypothesis
4
There is a positive
relationship between AP
and firm profitability
Reject Support Inconclusive Reject Reject Reject
In table 11 below, the results of the hypothesis testing are presented for hypothesis 5-8. The
results for hypothesis testing of hypothesis 5-8 do not differ in regards to the measurement of
firm profitability. There is no significant evidence supporting hypothesis 5-8. Therefore,
hypothesis 5-8 are all rejected. An interpretation of this is that relationship between working
capital efficiency and firm profitability is not affected by economic fluctuations among
sampled firms. This is in contrast to Enqvist et al. (2014), who found support for hypotheses
5a, 6a and 7a.
42
Table 11: Hypothesis testing group 2
Hypothesis Prediction Result Enqvist et al. (2014)
Result
ROA GOI Overall ROA GOI Overall
Hypothesis
5a
The significance of the relationship
between CCC and firm profitability
increases during economic
downturns
Reject Reject Reject Reject Support Support
Hypothesis
5b
The significance of the relationship
between CCC and firm profitability
decreases during economic booms
Reject Reject Reject Reject Reject Reject
Hypothesis
6a
The significance of the relationship
between INV and firm profitability
increases during economic
downturns
Reject Reject Reject Reject Support
Support
Hypothesis
6b
The significance of the relationship
between INV and firm profitability
decreases during economic booms
Reject Reject Reject Reject Reject Reject
Hypothesis
7a
The significance of the relationship
between AR and firm profitability
increases during economic
downturns
Reject Reject Reject Reject Support Support
Hypothesis
7b
The significance of the relationship
between AR and firm profitability
decreases during economic booms
Reject Reject Reject Reject Reject Reject
Hypothesis
8a
The significance of the relationship
between AP and firm profitability
increases during economic
downturns
Reject Reject Reject Reject Reject Reject
Hypothesis
8b
The significance of the relationship
between AP and firm profitability
decreases during economic booms
Reject Reject Reject Reject Reject Reject
43
5. Discussion
The findings of this thesis are broadly consistent with the findings of Enqvist et al. (2014) for
hypotheses group 1. However, for hypotheses group 2 there is a discrepancy between the
findings of this thesis and the original study. In this section, the findings of this thesis will be
discussed in relation to Enqvist et al. (2014) and to the broader context of previous research.
In addition, the limitations of this study will be discussed as well as replication as a scientific
method in business research.
5.1 Working capital efficiency and firm profitability
In this thesis two groups of hypotheses have been tested. The first group, hypothesis 1-4, tests
the general relationship between working capital efficiency and firm profitability. The second
group, hypothesis 5-8, tests how this relationship is affected by economic fluctuations.
Working capital efficiency is measured as CCC and its individual components. Firm
profitability is measured as ROA and GOI respectively. While the coefficient in the
regression analysis may seem low, it is somewhat matching the findings of previous studies
(e.g. Jose et al. 1996; Deloof 2003). Interpreting the results, a decrease in working capital is
suggested to have a significant positive impact on firm profitability. It is noteworthy that the
results of the regression analysis vary with the proxy of firm profitability. This was also found
in the study of Enqvist et al. (2014).
ROA has been the most commonly used measure of firm profitability (e.g. Jose et al. 1996;
Lyroudi & Lazaridis 2000; Wang 2002; Garcia-Teruel & Martinez-Solano 2007; Sharma &
Kumar 2011; Yazdanfar & Öhman 2014) but the suitability of ROA in regards to working
capital management has been discussed by others (Deloof 2003; Lazaridis & Tryfonidis
2006). Derived from this discussion, a number of studies have used GOI to measure firm
profitability (Deloof 2003; Lazaridis & Tryfonidis 2006; Gill et al. 2010; Enqvist et al. 2014).
According to Deloof (2003), the operating activities of firms with high levels of financial
assets will contribute little to ROA. Therefore, he suggests GOI as a measure firm
profitability as it captures the operational performance of firms. Lazaridis and Tryfonidis
(2006) too highlight the importance to connect working capital management to an operating
measure of firm profitability. This debate is important, because as shown in this thesis, and by
Enqvist et al. (2014), the result will vary depending on the measure of firm profitability. An
implication of this is that the findings of previous research depend on how firm profitability
has been measured.
44
Hypothesis 1 examines the relationship between CCC and firm profitability. The findings
provide support for hypothesis 1 when tested against both ROA and GOI. This indicates that
there is a negative relationship between CCC and firm profitability, which means that firms
can enhance profitability through shortening of their CCC. This is in line with Enqvist et al.
(2014) and corresponds to the overall findings in previous studies (see appendix A.1).
Hypothesis 2 examines the relationship between INV and firm profitability. The findings of
this thesis are inconclusive when weighting ROA and GOI together. This is in contradiction to
Enqvist et al. (2014) who found support for hypothesis 2. However, when looking at the
profitability measures individually, there is support for ROA but not for GOI. As such, there
are indications that there is a negative relationship between INV and firm profitability. This is
in line with the findings of previous studies and indicates that firms can increase profitability
by lowering INV (Deloof 2003; Lazaridis & Tryfonidis 2006; Garcia-Teruel & Martinez-
Solano 2007; Sharma & Kumar 2011). It also highlights the aforementioned discussion
regarding the influence of the choice of profitability measure.
Hypothesis 3 examines the relationship between AR and firm profitability. The findings
support hypothesis 3. This too contradicts the findings of Enqvist et al. (2014). While Enqvist
et al. (2014) rejected hypothesis 3, they did find a negative but insignificant relationship
between AR and GOI. Adding the findings of Deloof (2003) and Lazaridis and Tryfonidis
(2006) there are indications that firms can enhance their profitability by lowering AR. While
there is a possibility for firms to increase profitability by reducing AR, this must be weighed
against the effects this could have on long-term relationships with customers (Ng et al. 1999).
Therefore, it is not surprising that previous research has largely failed to find support for this
position.
Hypothesis 4 examines the relationship between AP and firm profitability. The findings of
this thesis are inconclusive when weighting ROA and GOI together. Enqvist et al. (2014)
rejects hypothesis 4 for both profitability measures. When looking at ROA and GOI
individually, there is a significant negative relationship between AP and ROA and a
significant positive relationship between AP and GOI. ROA and GOI differ in their structure
and in how firm profitability is measured. This highlights the aforementioned discussion that
the choice of variables will affect the results. Which hypotheses are supported or rejected will,
in a broader perspective, have a potential impact on the formulation of theory. However, in
45
previous research the impact of AP on firm profitability has been found both negative and
positive. This may seem counterintuitive, but has been discussed by both Deloof (2003) and
Garcia-Teruel and Martinez-Solano (2007). Garcia-Teruel and Martinez-Solano (2007)
discuss that it makes economic sense to assume that higher AP will increase firm profitability
because capital will be kept within firms resulting in lower capital costs and higher internal
financing. However, Deloof (2003) discusses that more profitable firms pay their suppliers
earlier. Excessive capital bound in AP might have a negative effect on firm profitability, but
this must be weighed against e.g. relationships with suppliers (Jose et al. 1996). For this
reason, it is also understandable that there is no consensus in previous research regarding the
relationship between AP and firm profitability. It is an interesting finding. From a theoretical
point of view, higher AP should enhance firm profitability because capital is kept within the
firm and can be used elsewhere within the firm (Garcia-Teruel & Martinez-Solano 2007). The
result in this thesis points in this direction when firm profitability is measured as GOI. On the
other hand, previous research has more often found a negative relationship between AP and
firm profitability. Deloof (2003) argued that more profitable firms pay their suppliers earlier.
The result in this thesis points in this direction when firm profitability is measured as ROA.
Hypothesis 5-8 have all been rejected in this thesis because no statistically significant results
were found. This is in contradiction to Enqvist et al. (2014) who found support for CCC, INV
and AR becoming increasingly important in economic downturns. However, there are
indications that INV are increasingly important in economic downturns while AR and AP are
increasingly important in economic booms. These indications are consistent for both ROA
and GOI. Most importantly, rejecting hypothesis 5-8 indicates that the importance of working
capital management do not vary with economic fluctuations in the sample. This can be viewed
from several aspects. Firstly, economic fluctuations might not affect the relationship between
working capital efficiency and firm profitability. Secondly, the way to measure the impact of
economic fluctuations fails to capture economic fluctuations. There may be other ways to
capture economic fluctuations that can provide better insights into how economic fluctuations
affect working capital management. Thirdly, different economic crises have different origins
and different effects on different firms (Reinhart & Rogoff 2010 p. 3-5). This might make it
hard to capture the impacts of economic fluctuations in general terms. Fourthly, there might
be contextual differences between Finland and Sweden, why the generalization of Enqvist et
al. (2014) cannot be supported for the Nordic region as a whole. In addition, the time period
differs between the studies, this might enhance the differences in economic environments
46
between the two studies. Lastly, in this thesis financial firms have been included. This might
also enhance the differences between this thesis and Enqvist et al. (2014).
Filbeck and Krueger (2005) highlighted interest rates, innovation and competition as
important determinants of working capital. According to Suomen Pankki (2017) and
Riksbanken (2017) there are considerable differences in interest rates during the period 1990-
2008 in Finland and 2000-2015 in Sweden. Even though the time periods overlap, the trend in
interest rates is downwards from the 1990s, accelerating after the financial crisis 2007-2008.
It is not unlikely that this might have an impact on the importance of working capital
efficiency. Cagle et al. (2013) discuss that CCC does not fully consider current liabilities, as
an effect interest among other factors is not fully captured. However, the lower the costs of
external financing, the less important the relationship between working capital efficiency and
firm profitability appears. This is in line with the reasoning of Filbeck and Krueger (2005).
The control variables used have all been shown to impact firm profitability (Enqvist et al.
2014). This was shown in the regression analysis in this thesis too. However, there were
differences between the results for the regression models using ROA and the ones using GOI.
More precisely, CR was shown to have a significant positive relationship with ROA but an
insignificant and varied relationship with GOI. Wang (2002) and Eljelly (2004) argued that
the way working capital efficiency affects firm profitability is through more efficient firm
liquidity. This result indicates that firms can improve their profitability by increasing their
margin of liquidity. However, this was only found for ROA. This is in line with Enqvist et al.
(2014). SIZE was found to be positively associated with firm profitability for both ROA and
GOI. This indicates that bigger firms are more profitable. This is in contradiction to Enqvist et
al. (2014) who found that bigger firms were less profitable. DEBT was found to have a
negative relationship with firm profitability for both ROA and GOI. This indicates that firms
with higher internal financing are more profitable. This is in line with Enqvist et al. (2014).
These results indicate that CR, SIZE and DEBT affect the relationship between working
capital efficiency and firm profitability. However, in comparison to Enqvist et al. (2014),
there is a discrepancy in regards to the effects of CR. Shin and Soenen (1998) found a positive
relationship between CR and firm profitability and this corresponds to other studies (Wang
2002; Eljelly 2004; Ebben & Johnson 2011). Ebben and Johnson (2011) concluded that firms
can improve both profitability and liquidity by shortening their CCC. An interpretation of this
is that firms can increase returns and reduce risk at the same time. The findings of this thesis
47
indicate that this might be the case. SIZE has consistently been associated with higher firm
profitability in previous research (Deloof 2003; Lazaridis & Tryfonidis 2006), why the
findings of Enqvist et al. (2014) are somewhat surprising. DEBT has been found to have a
negative relationship with firm profitability in previous studies (Deloof 2003; Lazaridis &
Tryfonidis 2006). This makes economic sense, as higher external financing should imply
higher costs and thus lower profitability. This has been discussed in general terms as well as
in regards to the economic environment (Einarsson & Marquis 2001; Braun & Larrain 2005;
Filbeck & Krueger 2005).
5.1.1 Descriptive statistics and correlations
In order to widen the discussion above, the regression analysis is complemented with
descriptive statistics and correlations. The characteristics of the variables of the sample do not
vary considerably between the full time period, economic downturns and economic booms
(see 3.5.3 for full sample and appendix C for economic downturns and economic booms).
While this does not necessarily affect the strength of the regression models, it still gives
valuable insights into differences between the studied time periods. Most obvious is the
higher ROA and GOI in economic booms than for the entire time period and particularly in
economic downturns. In addition, CCC and INV are generally lower in economic booms than
for the full time period and in economic downturns. One reason behind this might be that with
higher economic activity, firm performance increases overall. However, the strength of the
relationship between working capital efficiency and firm profitability does not change in this
direction. An interpretation of this is that over time, working capital efficiency, as measured
by CCC, does not vary considerably in the studied time period. It is notable that among the
sampled firms in this study, the average CCC is relatively consistent over time. This is in
contrast to Filbeck and Krueger (2005) who concluded that working capital management will
change over time. Correlation is not causality, but the correlations in the sample are generally
higher for GOI than for ROA. In addition, there are indications that the correlation is higher in
economic downturns than in economic booms (see table 7 under 3.5.4).
5.2 Contributions to research
This thesis contributes to the research of working capital efficiency and firm profitability in
two ways. Firstly, it contributes to the empirical body of knowledge about the relationship
between working capital efficiency and firm profitability. Secondly, it contributes with
insights into how this relationship is affected by economic fluctuations.
48
Firstly, previous research presents a strong case for a negative relationship between CCC and
firm profitability. This thesis contributes further to strengthen this position. In regards to INV
and AR, this thesis has found evidence that INV and AR are negatively associated with firm
profitability. This has been confirmed by previous research (Deloof 2003) and is theoretically
anchored (Jose et al. 1996) but has been difficult to show in other studies (e.g. Gill et al.
2010). In regards to AP, the findings of this thesis increase the ambiguity found in previous
studies (e.g. Deloof 2003; Garcia-Teruel & Martinez-Solano 2007; Mathuva 2010). This
ambiguity constitutes good reason for further investigation of AP. Furthermore, this thesis
fills a gap in the Swedish context. Yazdanfar and Öhman (2014) studied Swedish SMEs 2008-
2011 and found a negative relationship between CCC and firm profitability. This thesis
supports their findings and contributes to the previous literature by adding findings from the
largest listed Swedish firms. In addition, no previous study has been conducted on the
individual components of CCC in the Swedish context. As such, this thesis provides a starting
point for further investigation of INV, AR and AP and their relationships to firm profitability
among Swedish firms.
Secondly, Enqvist et al. (2014) suggested that their findings would be generalizable to the
Nordic region as a whole. The findings of this thesis do not support this position. While
Enqvist et al. (2014) found that the importance of CCC, INV and AR increased in economic
downturns, this thesis did not find any impact from economic fluctuations on the relationship
between working capital efficiency and firm profitability. These differences may depend on
several factors but it cannot be ruled out that economic fluctuations might not impact the
relationship between working capital efficiency and firm profitability. This too requires
further investigation.
5.3 Limitations of this thesis
This thesis is bound to its context and limited by uncertainties derived from the choice of
approach. By addressing these uncertainties, their impact on the results of the thesis should be
both understood and minimized. First it must be noted that this thesis is a replication study in
a long line of research examining the relationship between working capital efficiency and firm
profitability. This thesis does not deviate from the research tradition. Similar concerns have
been discussed in previous research (e.g. Deloof 2003; Garcia-Teruel & Martinez-Solano
2007).
49
Firstly, the use of secondary data may contain errors that distort the results. While the
sampled firms are subject to the same laws and regulations, accounting practices between
firms still exist. In addition, the data sample contained a high amount of missing and invalid
values. It is probable that the Thomson Reuters Datastream is incomplete to the extent that
this affects the results. In addition, firm-year observations are diminishing from year 2015
down to year 2000 because the database only contains data from active firms. For this reason,
firms that no longer exist, have been acquired by other firms, have merged with other firms or
are no longer publicly traded on the Stockholm Stock Exchange are not in the database. This
presents a possible survival bias in the data sample. These uncertainties have been controlled
for in several ways. Firstly, firm observations with missing, invalid or unrealistic values have
been excluded. Secondly, firm, year and industry have been controlled for in all regression
models. Thirdly, the regression models have been tested for the existence of factors that can
distort the statistical data analysis. Fourthly, several robustness checks were conducted. In
addition, the years studied were chosen in order to balance the survival bias and a satisfying
amount of years. In the best of worlds another database would have been used with the
possibility to extend the years studied and include all the firms that existed these particular
years.
Working capital management may be firm-specific to the extent that the straight-forward
theoretical framework may be applied to certain firms, certain types of firms or certain
industries. In this thesis, all listed firms have been included. Previous research presents a case
for working capital and working capital management being more important in certain
contexts. For this reason, it may be fruitful to consider working capital management in more
specific contexts.
5.4 Replication as a scientific method
This thesis shows the importance of replication studies in business research. While the results
of this thesis do not confirm the findings of Enqvist et al. (2014), it does give support for
replication as a scientific method. Mittelsteadt and Zorn (1984) mean that what cannot be
replicated is not worth knowing, and suggest that disconfirmations propose that new
approaches must be tested. The findings of this thesis suggest that alternative approaches to
measure the impacts of economic fluctuations on the relationship between working capital
efficiency and firm profitability should be tested. It also highlights a central problem with
replication studies. Firstly, the argument that neither business researchers nor business
50
practitioners should base decision-making on stand-alone studies appears valid. This thesis
questions the strength of the arguments of Enqvist et al. (2014) and ultimately the
implications for business researchers, business practitioners and policymaker. Secondly,
putting other researchers’ work under increased scrutiny also reveals problems with
replicability. In this case, one must consider how much detail studies in general can omit in
regards to methodology and results. It presents an interesting case, because it highlights a
discussion of how much information research articles should contain in regards to
methodology and results in particular (Bryman & Bell 2015 p. 50). Not being able to fully
comprehend how a study has been conducted, it is impossible to fully understand it and
ultimately replicate it. Thirdly, the implications given by researchers might be affected by the
context of the research. In this case, the scientific journal Research in International Business
and Finance is specialized towards alternative perspectives with articles giving policy
implications (Elsevier 2017). This certainly affects the implications formulated in the article.
Enqvist et al. (2014) proposes that policymakers take the working capital of firms into
account and provide them with liquidity in wake of crisis. This is a far reaching suggestion
from an article studying something that, in a vast literature review, can be perceived as a
matter of internal decision-making, resulting in profitability enhancements for individual
firms. In addition, when not studying business cycles it is hazardous to give policy
implications based on business cycles.
51
6. Conclusion
This thesis has examined the relationship between working capital efficiency and firm
profitability, and how this relationship is affected by economic fluctuations during the years
2000-2015 among listed Swedish firms. The findings of this thesis contribute to business
research in two ways. Firstly, it examines the relationship between working capital efficiency
and firm profitability. Secondly, it examines how this relationship is affected by economic
fluctuations. Together with previous research, this thesis provides evidence that firms can
enhance their profitability through improvements of their working capital efficiency. More
specifically, firm profitability is enhanced through shortening of the CCC and lower AR. In
addition, there are indications that INV has a negative relationship with firm profitability.
However, AP is two-edged and it appears that the effects on firm profitability are ambiguous.
Further, the findings of this thesis do not support that the relationship between working capital
efficiency and firm profitability is affected by economic fluctuations. In this thesis, it is
suggested that working capital management holds the potential to enhance firm profitability
regardless of the economic environment. For this reason, it is imperative to include working
capital management in a broader business scope on a continuous basis.
6.1 Suggestions for future research
In this thesis, a number of gaps in the literature on the relationship between working capital
efficiency and firm profitability have been identified. Increased knowledge about these
specific aspects would increase the understanding of the effects of working capital efficiency
on firm profitability. Firstly, it appears evident that firms can enhance their profitability by
shortening their CCC. However, the impact of the individual components of CCC needs to be
investigated further. Secondly, the effects of AP appear to be a special case. This needs to be
investigated further to better align theory and empirical findings. Thirdly, more research is
encouraged in the Swedish context in order to create a better understanding of specific
Swedish conditions. Fourthly, it has been suggested that working capital is firm-specific,
industry-specific and country-specific. However, there is a lack of research in more specific
contexts. Therefore, valuable insights can be gained by studying different aspects of working
capital management in more specific contexts. Among these are firms’ relationship with
customers and suppliers and how these non-financial values can affect working capital
management in business practice. Fifthly, while there was no support for the assumption that
economic fluctuations will affect the relationship between working capital efficiency and firm
52
profitability in this thesis, it is still an under-researched area. We encourage more research to
be conducted in order to better understand if and how economic fluctuations affect working
capital management.
53
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Appendices
Appendix A: Previous research
A.1 Cash conversion cycle and firm profitability
Table A: Previous research: Cash conversion cycle and firm profitability
Authors Title Context &
Sample
Profitability
measure
Findings Year
Yasdanfar &
Öhman (2014)
The impact of cash
conversion cycle on
firm profitability: An
empirical study based
on Swedish data
Sweden, SMEs ROA CCC has a
significant negative
relationship with firm
profitability
2008-
2011
Enqvist, Graham
& Nikkinen
(2014)
The impact of
working capital
management on
firm profitability
in different business
cycles: Evidence from
Finland
Finland, non-
financial listed
firms
ROA,
GOI
CCC has a
significant negative
relationship with firm
profitability
1990-
2008
Ebben &
Johnson (2011)
Cash Conversion Cycle
Management in Small
Firms: Relationships
with Liquidity, Invested
Capital, and Firm
Performance
USA, small
manufacturing
& retail firms
Asset
turnover,
ROIC
CCC has a negative
relationship with
liquidity and firm
profitability
2002-
2004
Sharma & Kumar
(2011)
Effect of Working
Capital Management on
Firm Profitability:
Empirical evidence
from India
India, non-
financial listed
firms
ROA CCC is significant
positive correlated
with firm profitability
2000-
2008
Mathuva (2010)
Falope & Ajilore
(2009)
The Influence of
Working Capital
Management
Components on
Corporate Profitability:
A Survey on Kenyan
Listed Firms
Working Capital
Management and
Corporate Profitability:
Evidence from Panel
Data Analysis of
Selected Quoted
Companies in Nigeria.
Kenya, listed
firms on Nairobi
Stock exchange
Nigeria, non-
financial firms
Net
operating
profit
ROA
CCC has a significant
negative relationship
with firm profitability
CCC has a
significant negative
relationship with firm
profitability
1993-
2008
1996-
2005
Garcia-Teruel &
Martinez-Solano
(2007)
Effects of working
capital management on
SME profitability
Spain, listed
SMEs
ROA
CCC has a significant
negative relationship
with firm profitability
1996-
2002
Lazaridis &
Tryfonidis
(2006)
Relationship between
working capital
management and
Greece, listed
firms
Gross
operating
profit
CCC has a significant
negative relationship
with firm profitability
2001-
2004
59
profitability of listed
companies in the
Athens stock exchange
Eljelly (2004) Liquidity - profitability
tradeoff: An empirical
investigation in an
emerging market
Saudi Arabia,
listed firms
Net
operating
income
CCC has a significant
negative relationship
with firm profitability
1996-
2000
Deloof (2003) Does Working Capital
Management Affect
Profitability of Belgian
Firms?
Belgium, non-
financial listed
firms
GOI CCC has a significant
negative relationship
with firm profitability
1992-
1996
Wang (2002) Liquidity management,
operating performance,
and corporate value:
evidence from Japan
and Taiwan
Japan &
Taiwan, listed
firms
ROA,
Return on
equity
CCC has a negative
relationship with firm
profitability
1985-
1996
Lyroudi &
Lazaridis (2000)
The cash conversion
cycle and liquidity
analysis of the food
industry in Greece
Greece, food
industry
ROA
Net profit
margin
Significant positive
relationship between
CCC and liquidity.
CCC was positively
related to firm
profitability
1997
Shin & Soenen
(1998)
Efficiency of Working
Capital Management
and Corporate
Profitability
USA, non-
financial firms
Operating
income plus
depreciation
related to
total asset
Negative relationship
between the net trade
cycle and firm
profitability
1975-
1994
Jose, Lancaster
& Stevens (1996)
Corporate returns and
Cash conversion cycle
USA, firms
among seven
industries
ROA,
Return on
equity
CCC has a significant
negative relationship
with firm profitability
1974-
1993
60
A.2 Days of inventory, days of accounts receivables, days of accounts payables and firm
profitability
Table B: Previous research: days of inventory, days of accounts receivables, days of accounts payables and
profitability
Authors Title Context
&
Sample
Profitability
measure
Findings
(INV)
Findings
(AR)
Findings
(AP)
Year
Enqvist,
Graham &
Nikkinen
(2014)
The impact
of working
capital
management
on firm
profitability
in different
business
cycles:
Evidence
from Finland
Finland,
non-
financial
listed
firms
ROA,
GOI
Negative
relationship
between
INV and
firm
profitability
Negative
relationship
between
AR and
firm
profitability
Negative
relationship
between
AP and
firm
profitability
1990-2008
Sharma &
Kumar
(2011)
Effect of
Working
Capital
Management
on Firm
Profitability:
Empirical
evidence
from India
India,
non-
financial
listed
firms
ROA Negative
relationship
between
INV and
firm
profitability
Positive
relationship
between
AR and
firm
profitability
Negative
relationship
between
AP and
firm
profitability
2000-2008
Mathuva
(2010)
Falope &
Ajilore
(2009)
The
Influence of
Working
Capital
Management
Components
on Corporate
Profitability:
A Survey on
Kenyan
Listed Firms
Working
Capital
Management
and
Corporate
Profitability:
Evidence
from Panel
Data
Analysis of
Selected
Quoted
Companies
in Nigeria.
Kenya,
listed
firms on
Nairobi
Stock
exchange
Nigeria,
non-
financial
firms
Net
operating
profit
ROA
Positive
relationship
between
INV and
firm
profitability
Negative
relationship
between
INV and
firm
profitability
Positive
relationship
between
AR and
firm
profitability
Negative
relationship
between
AR and
firm
profitability
Positive
relationship
between
AP and
firm
profitability
Negative
relationship
between
AP and
firm
profitability
1993-2008
1996-2005
Garcia-
Teruel &
Effects of
working
Spain,
listed
ROA Significant
negative
1996-2002
61
Martinez-
Solano
(2007)
capital
management
on SME
profitability
SMEs relationship
between
INV and
firm
profitability
Lazaridis
&
Tryfonidis
(2006)
Relationship
between
working
capital
management
and
profitability
of listed
companies in
the Athens
stock
exchange
Greece,
listed
firms
Gross
operating
profit
Significant
negative
relationship
between
INV and
firm
profitability
Significant
negative
relationship
between
AR and
firm
profitability
Significant
negative
relationship
between
AP and
firm
profitability
2001-2004
Deloof
(2003)
Does
Working
Capital
Management
Affect
Profitability
of Belgian
Firms?
Belgium,
non-
financial
listed
firms
GOI Significant
negative
relationship
between
INV and
firm
profitability
Significant
negative
relationship
between
AR and
firm
profitability
1992-1996
62
Appendix B: Variable summarization and formulas
Table C: Summarization and formulas for the dependent, independent and control variables
Variable Abbreviation Formula
Dependent
Return on asset ROA Net income
Total assets
Gross operating income GOI Revenue-Cost of goods sold
Total assets-Financial assets
Independent
Cash conversion cycle CCC Days of inventory +Days of accounts receivables -
Days of accounts payables
Days of inventory INV 365 (
Inventory
Cost of goods sold )
Days of accounts receivables AR 365 (
Accounts receivables
Revenue )
Days of accounts payables AP 365 (
Accounts payables
Cost of goods sold )
Dummy
Economic downturns D1 Categorization of economic downturns
Economic booms D2 Categorization of economic booms
Control
Current ratio CR Current assets
Current liabilities
Firm size SIZE ln(Sales)
Firm debt ratio DEBT Short term loans + Long term loans
Total assets
Year YEAR Control for firm and year effects
Industry INDUSTRY Control for industry effects
63
Appendix C: Descriptive statistics
The descriptive statistics of the different economic states used in this thesis are here
presented. Economic downturns and economic booms are categorized as the five years with
the lowest respectively the highest annual GDP growth during the 16-year period of the study.
To understand the features and characteristics of the variables of the sample in economic
downturns respectively economic booms, the descriptive statistics present the mean, standard
deviation and minimum and maximum values. Table D and table E below present the
descriptive statistics for economic downturn and economic booms respectively.
Table D: Descriptive statistics economic downturns
Variable Mean SD Min Max
CCC 88.617 78.817 -270.756 596.011
INV 71.870 66.680 0 414.079
AR 71.038 41.346 1.234 467.742
AP 54.291 48.317 0 383.468
ROA 0.024 0.1485 -1.143 0.731
GOI 0.318 0.294 -0.869 3.100
CR 1.963 1.781 0.234 19.810
SIZE 14.458 2.095 7.454 19.531
DEBT 0.477 0.198 0.007 1.408 Notes: Descriptive statistics for the sampled variables for the years 2001, 2008, 2009, 2012 and 2013, for a total of number of
847 observations.
Table E: Descriptive statistics economic booms
Variable Mean SD Min Max
CCC 82.806 65.999 -299.582 429.482
INV 65.908 58.930 0 411.897
AR 71.213 37.753 1.456 345.452
AP 54.315 48.158 0 393.451
ROA 0.036 0.167 -1.180 0.511
GOI 0.332 0.349 -0.715 4.662
CR 2.054 1.999 0.143 26.633
SIZE 15.477 2.135 7.147 19.560
DEBT 0.461 0.186 0.025 1.021 Notes: Descriptive statistics for the sampled variables for the years 2000, 2004, 2006, 2010 and 2015 for a total of number of
779 observations.
64
Appendix D: Statistical testing
D.1 Extreme values and outliers
The original sample contained a number of extreme values and missing values. Those values
interrupt and make the assumption testing in the OLS regression misleading. Therefore, the
data has been screened and cleaned from these values. The original sample contained 5,200
firm-year observations Firstly, missing values were removed leaving a sample of 2,667 firm-
year observations. Secondly, in order to identify these outliers and extreme values the data has
been checked manually using descriptive statistics and boxplots to identify which values are
categorized as extreme values and outliers in the data (Pevalin & Robson 2009 p. 289).
Extreme values and outliers have been trimmed and deleted manually for both dependent and
independent variables. 77 observations were removed from the sample. The final sample
represents 49.8% of the total original sample. The figure below illustrates boxplots of the
variables after they have been trimmed. Notably, some extreme values and outliers were kept
in the sample since these are plausible from a theoretical point of view.
-20
24
6
GO
I
0
100
200
300
400
500
AR
0
100
200
300
400
AP
-400
-200
0
200
400
600
CC
C
0
100
200
300
400
INV
65
D.2 Linearity test
OLS regression requires that the association between the dependent variable and the
independent variables are linear (Pevalin & Robson 2009 p. 329-330). The figures below
illustrate that there is a linear relationship between the dependent variable and independent
variables.
66
D.3 Normality test
OLS regression requires that the residuals are normally distributed (Pevalin & Robson 2009 p.
288-295). The residuals are the differences between the predicted and the actual values for
each case. In all regression models, the residuals are normally distributed with some kurtosis.
The first four histogram shows the residuals for the regression models using ROA and the
four remaining represent the residuals for the regression models of using GOI. In addition, the
standardized normal probability (P-P) plots illustrates that there is some variation for the
residuals. The variation and kurtosis tendency indicates that there are some violations in the
normality distribution for the residuals, this because there are outliers in the sample (Pevalin
& Robson 2009 p. 299).
01
23
4
Den
sity
-1 -.5 0 .5 1Residuals
01
23
45
Den
sity
-1 -.5 0 .5 1Residuals
01
23
45
Den
sity
-1 -.5 0 .5 1Residuals
01
23
45
Den
sity
-1 -.5 0 .5 1Residuals
67
0.5
11
.52
Den
sity
-1 0 1 2 3 4Residuals
0.5
11
.52
Den
sity
-1 0 1 2 3 4Residuals
0.5
11
.52
Den
sity
-1 0 1 2 3 4Residuals
0.5
11
.52
Den
sity
-1 0 1 2 3 4Residuals
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
68
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
0.0
00
.25
0.5
00
.75
1.0
0
Norm
al F
[(re
s-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
69
D.4 Homoscedasticity
OLS regression requires that the variance of the residuals is constant, that is showing
homoscedasticity. If the variance of the residuals is not constant, that is showing
heteroscedasticity, the results will be impacted (Pevalin & Robson 2009 p. 288-295). To test
for homoscedasticity, scatterplots have been checked for all the regression models. The
scatterplots below illustrate each of the regression models that have been tested. As can be
illustrated, the assumption of homoscedasticity is present in each case.
-1-.
50
.51
Resid
uals
-.4 -.2 0 .2 .4Fitted values
-1-.
50
.51
Resid
uals
-.4 -.2 0 .2 .4Fitted values
-1-.
50
.51
Resid
uals
-.4 -.2 0 .2 .4Fitted values
-1-.
50
.51
Resid
uals
-.4 -.2 0 .2 .4Fitted values
70
-10
12
34
Resid
uals
0 .2 .4 .6Fitted values
-10
12
34
Resid
uals
0 .2 .4 .6Fitted values
-10
12
34
Resid
uals
-.4 -.2 0 .2 .4 .6Fitted values
-10
12
34
Resid
uals
0 .2 .4 .6Fitted values
71
D.5 Multicollinearity
OLS regression requires that the independent variables are not highly correlated (Pevalin &
Robson 2009 p. 288-295). Multicollinearity means that there is correlation between the
independent variables. Multicollinearity is problematic because it increases the variance of the
regression coefficients, thus harming the regression models. Multicollinearity has been tested
for through the VIF-test. In line with, Pevalin and Robson (2009 p. 302) and previous
research (e.g. Lazaridis & Tryfonidis 2006), VIF values above 10 and values under 0.1
indicates multicollinearity. As shown in the tables below, the VIF values for the independent
variables are above 0.1 and under 10.
Table F: Model (CCC): ROA = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +
β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
CCC 2.70
CR 1.56
SIZE 1.24
DEBT 1.51
(D1*CCC) 3.64
(D2*CCC) 3.44
Table G: Model (INV): ROA = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
INV 2.62
CR 1.53
SIZE 1.23
DEBT 1.53
(D1*INV) 3.32
(D2*INV) 3.05
Table H: Model (AR): ROA = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
AR 2.43
CR 1.54
SIZE 1.29
DEBT 1.50
(D1*AR) 5.20
(D2*AR) 5.54
Table I: Model (AP): ROA = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
AP 5.68
CR 1.51
SIZE 1.24
DEBT 1.50
(D1*AP) 3.91
(D2*AP) 3.85
72
Table J: Model (CCC): GOI = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
CCC 2.70
CR 1.56
SIZE 1.24
DEBT 1.51
(D1*CCC) 3.64
(D2*CCC) 3.44
Table K: Model (INV): GOI = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
INV 2.62
CR 1.53
SIZE 1.25
DEBT 1.53
(D1*INV) 3.32
(D2*INV) 3.05
Table L: Model (AR): GOI = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
AR 2.43
CR 1.54
SIZE 1.29
DEBT 1.50
(D1*AR) 3.20
(D2*AR) 3.34
Table M: Model (AP): GOI = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7
SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti
Variable VIF
AP 5.68
CR 1.51
SIZE 1.24
DEBT 1.30
(D1*AP) 3.91
(D2*AP) 3.85