Working Capital Efficiency and Firm Profitability - A Quantitative Study of Listed Swedish Firms

Transcription

1 Working Capital Efficiency and Firm Profitability - A Quantitative Study of Listed Swedish Firms Master s Thesis 30 credits Department of Business Studies Uppsala University Spring Semester of 2017 Date of Submission: Oscar Gustén Tobias Pahkamaa Supervisor: Jan Lindvall

2 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 , 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 II

3 Table of Contents Abstract... II List of tables... IV List of figures... IV List of abbreviations... V 1. Introduction Literature review Working capital management Strategies of working capital management Efficiency of working capital management Cash conversion cycle Inventory, accounts receivables and accounts payables Working capital management and economic fluctuations The original study: Frame, results, discussion and implications Hypotheses formulation Methodology Replication The original study: Methodological review Thesis frame Operationalization Variable selection Dependent variable Independent variables Dummy variables Control variables Regression models Data Population and sample Statistical tools Descriptive statistics Correlation matrix Data testing Results Regression analysis: Return on assets Regression analysis: Gross operating income Hypothesis testing Discussion Working capital efficiency and firm profitability Descriptive statistics and correlations Contributions to research Limitations of this thesis Replication as a scientific method Conclusion Suggestions for future research References Appendices Appendix A: Previous research Appendix B: Variable summarization and formulas Appendix C: Descriptive statistics Appendix D: Statistical testing III

4 List of tables Table 1: Previous research on CCC and firm profitability Table 2: Previous research on the individual components of CCC and firm profitability Table 3: Hypotheses group Table 4: Hypotheses group Table 5: Classification of economic downturns and economic booms Table 6: Descriptive statistics full sample Table 7: Pearson s correlation matrix Table 8: Regression analysis ROA Table 9: Regression analysis GOI Table 10: Hypothesis testing group Table 11: Hypothesis testing group List of figures Figure 1: Annual GDP growth (%) of Sweden Figure 2: Number of observations per year IV

5 List of abbreviations AR AP CCC CR DEBT GDP GOI INV OLS ROA SIZE SMEs Days of accounts receivables Days of accounts payables Cash conversion cycle Current ratio Firm debt ratio Gross domestic product Gross operating income Days of inventory Ordinary least squares Return on assets Firm size Small and medium-sized enterprises V

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 , 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 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 6

7 Nordic region as a whole. By drawing upon data from listed Swedish firms for the years , 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 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 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. 7

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 8

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) 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 9

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. 10

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). 11

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 , 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 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) Significant positive relationship with firm profitability Lyroudi & Lazaridis (2000) Sharma & Kumar (2011) 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 12

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). 13

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 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 14

15 the link between external financing and growth over business cycles for the years 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 , they found a negative relationship between CCC and 15

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. 16

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: 17

18 Table 3: Hypotheses group 1 Hypothesis Prediction Hypothesis 1 There is a negative relationship between CCC and firm profitability Hypothesis 2 Hypothesis 3 Hypothesis 4 There is a negative relationship between INV and firm profitability There is a negative relationship between AR and firm profitability 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 Hypothesis 6a Hypothesis 6b Hypothesis 7a Hypothesis 7b Hypothesis 8a Hypothesis 8b The significance of the relationship between CCC and firm profitability decreases during economic booms The significance of the relationship between INV and firm profitability increases during economic downturns The significance of the relationship between INV and firm profitability decreases during economic booms The significance of the relationship between AR and firm profitability increases during economic downturns The significance of the relationship between AR and firm profitability decreases during economic booms The significance of the relationship between AP and firm profitability increases during economic downturns The significance of the relationship between AP and firm profitability decreases during economic booms 18

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 19

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 but that there was a slow down during the years 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 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 nonfinancial 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 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 20

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) 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. 21

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 22

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 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) 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 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 GOI 1 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. 23

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 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) 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 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 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. 24

25 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 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 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 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 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. 25

26 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 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 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: 26

27 Firm profitability = β0 + β1 CCC ti + β2 D1 + β3 D2 + β4 (D1*CCC ti ) + β5 (D2*CCC ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Firm profitability = β0 + β1 INV ti + β2 D1 + β3 D2 + β4 (D1*INV ti ) + β5 (D2*INV ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Firm profitability = β0 + β1 AR ti + β2 D1 + β3 D2 + β4 (D1*AR ti ) + β5 (D2*AR ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Firm profitability = β0 + β1 AP ti + β2 D1 + β3 D2 + β4 (D1*AP ti ) + β5 (D2*AP ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti 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 ). 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 27

28 screened and cleaned from missing values and extreme values. Real GDP data was collected from Statistics Sweden for the years The time period of this thesis is the 16-year period between The reason behind the choice of these years is three folded. Firstly, the years covers three economic downturns with significant impact on the Swedish annual GDP growth. Secondly, the years 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 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 As such, the data is an unbalanced panel over the 16-year period between Figure 1: Annual GDP growth (%) of Sweden Source: Statistics Sweden (2017) The annual GDP growth for the years 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 28

29 compared to the results for the whole time period 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 (%) Source of annual GDP growth (%): Statistics Sweden (2017) 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. 29

30 Figure 2: Number of observations per year 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 Descriptive statistics Table 6: Descriptive statistics full sample Variable Mean SD Min Max CCC INV AR AP ROA GOI CR SIZE DEBT Notes: Descriptive statistics for the sampled variables for the years , for a total of number of 2,589 observations. 30

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 , 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 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 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 31

32 capital management and firm profitability. For a more detailed review of previous research, see appendix A Correlation matrix Table 7: Pearson s correlation matrix ROA GOI CCC INV AR AP CR SIZE DEBT ROA GOI 0.383*** CCC *** *** INV ** 0.079*** 0.707*** AR *** *** 0.421*** AP *** 0.152*** *** 0.281*** 0.255*** CR 0.034** *** 0.242*** 0.159*** 0.167*** SIZE 0.308*** 0.137*** ** *** *** *** DEBT *** ** *** *** *** *** 0.327*** ROA 0.387*** *** *** *** ** GOI 0.319*** *** 0.082** *** 0.120*** *** CCC ** *** 0.679*** 0.285*** *** 0.116*** *** INV *** 0.037* 0.713*** *** 0.118*** 0.073** *** AR ** *** 0.462*** 0.085** 0.330*** *** AP *** 0.122*** *** 0.290*** 0.219*** *** CR ** 0.330*** 0.227*** 0.245*** *** *** SIZE 0.281*** 0.113*** * *** *** *** 0.329*** DEBT *** *** *** ** *** 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. 32

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 Data testing In order to conduct OLS regression, the sample needs to fulfill certain criteria (Pevalin & Robson 2009 p ). 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 33

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. 34

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 CCC ti + β2 D1 + β3 D2 + β4 (D1*CCC ti ) + β5 (D2*CCC ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (INV): ROA = β0 + β1 INV ti + β2 D1 + β3 D2 + β4 (D1*INV ti ) + β5 (D2*INV ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (AR): ROA = β0 + β1 AR ti + β2 D1 + β3 D2 + β4 (D1*AR ti ) + β5 (D2*AR ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (AP): ROA = β0 + β1 AP ti + β2 D1 + β3 D2 + β4 (D1*AP ti ) + β5 (D2*AP ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti 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. 35

36 Table 8: Regression analysis ROA Dependent: ROA Coefficient estimator Expected Sign Model (CCC) Model (INV) Model (AR) Model (AP) Constant *** ( ) *** ( ) *** (0.0353) *** ( ) D * ( ) ( ) * ( ) (0.0162) D * ( ) * ( ) ( ) * ( ) CCC *** ( ) (D1*CCC) ( ) (D2*CCC) ( ) INV * (0.0002) (D1*INV) ( ) (D2*INV) ( ) AR *** ( ) (D1*AR) ( ) (D2*AR) ( ) AP * ( ) (D1*AP) ( ) (D2*AP) (0.0003) CR *** ( ) ** ( ) ** ( ) * (0.0024) SIZE *** *** *** *** ( ) ( ) ( ) DEBT *** *** *** ( ) ( ) ( ) R-square F-value N 2,589 2,589 2,589 2,589 Year Included Included Included Included Industry ID Included Included Included Included 36 ( ) *** ( ) Clustered around Yes Yes Yes Yes Firm ID 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. 37

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 CCC ti + β2 D1 + β3 D2 + β4 (D1*CCC ti ) + β5 (D2*CCC ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (INV): GOI = β0 + β1 INV ti + β2 D1 + β3 D2 + β4 (D1*INV ti ) + β5 (D2*INV ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (AR): GOI = β0 + β1 AR ti + β2 D1 + β3 D2 + β4 (D1*AR ti ) + β5 (D2*AR ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti Model (AP): GOI = β0 + β1 AP ti + β2 D1 + β3 D2 + β4 (D1*AP ti ) + β5 (D2*AP ti ) + β6 CR ti + β7 SIZE ti + β8 DEBT ti + β9 YEAR t + β10 INDUSTRY i + ε ti 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. 38

39 Table 9: Regression analysis GOI Dependent: GOI Coefficient estimator Expected Sign Model (CCC) Model (INV) Model (AR) Model (AP) Constant *** ( ) ** ( ) *** ( ) ( ) D ( ) ( ) ( ) ( ) D * ( ) * ( ) ( ) * ( ) CCC ** ( ) (D1*CCC) ( ) (D2*CCC) ( ) INV ( ) (D1*INV) ( ) (D2*INV) ( ) AR *** ( ) (D1*AR) ( ) (D2*AR) ( ) AP ** ( ) (D1*AP) ( ) (D2*AP) ( ) CR ( ) (0.0033) ( ) ( ) SIZE *** ( ) *** ( ) ** ( ) *** ( ) DEBT ( ) ( ) ( ) ( ) R-square F-value 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, 39

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. 40

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 Hypothesis 3 Hypothesis 4 There is a negative relationship between INV and firm profitability There is a negative relationship between AR and firm profitability There is a positive relationship between AP and firm profitability Support Reject Inconclusive Support Support Support Support Support Support Reject Reject Reject 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. 41

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 Hypothesis 6a Hypothesis 6b Hypothesis 7a Hypothesis 7b Hypothesis 8a Hypothesis 8b The significance of the relationship between CCC and firm profitability decreases during economic booms The significance of the relationship between INV and firm profitability increases during economic downturns The significance of the relationship between INV and firm profitability decreases during economic booms The significance of the relationship between AR and firm profitability increases during economic downturns The significance of the relationship between AR and firm profitability decreases during economic booms The significance of the relationship between AP and firm profitability increases during economic downturns The significance of the relationship between AP and firm profitability decreases during economic booms Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Support Support Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Support Support Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject 42

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. 43

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 44

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 45

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 in Finland and in Sweden. Even though the time periods overlap, the trend in interest rates is downwards from the 1990s, accelerating after the financial crisis 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 46

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) 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 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. 47

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 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). 48

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 49

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. 50

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 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 51

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. 52

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58 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 Yasdanfar & Öhman (2014) The impact of cash conversion cycle on firm profitability: An empirical study based on Swedish data Findings Sweden, SMEs ROA CCC has a significant negative relationship with firm profitability Year Enqvist, Graham & Nikkinen (2014) The impact of working capital management on firm profitability in different business cycles: Evidence from Finland Finland, nonfinancial listed firms ROA, GOI CCC has a significant negative relationship with firm profitability 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 Sharma & Kumar (2011) Effect of Working Capital Management on Firm Profitability: Empirical evidence from India India, nonfinancial listed firms ROA CCC is significant positive correlated with firm profitability Mathuva (2010) The Influence of Working Capital Management Components on Corporate Profitability: A Survey on Kenyan Listed Firms Kenya, listed firms on Nairobi Stock exchange Net operating profit CCC has a significant negative relationship with firm profitability Falope & Ajilore (2009) Working Capital Management and Corporate Profitability: Evidence from Panel Data Analysis of Selected Quoted Companies in Nigeria. Nigeria, nonfinancial firms ROA CCC has a significant negative relationship with firm profitability 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 Lazaridis & Tryfonidis (2006) Relationship between working capital management and Greece, listed firms Gross operating profit CCC has a significant negative relationship with firm profitability

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 Deloof (2003) Does Working Capital Management Affect Profitability of Belgian Firms? Belgium, nonfinancial listed firms GOI CCC has a significant negative relationship with firm profitability 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 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, nonfinancial firms Operating income plus depreciation related to total asset Negative relationship between the net trade cycle and firm profitability 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

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, nonfinancial 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 Sharma & Kumar (2011) Effect of Working Capital Management on Firm Profitability: Empirical evidence from India India, nonfinancial listed firms ROA Negative relationship between INV and firm profitability Positive relationship between AR and firm profitability Negative relationship between AP and firm profitability Mathuva (2010) The Influence of Working Capital Management Components on Corporate Profitability: A Survey on Kenyan Listed Firms Kenya, listed firms on Nairobi Stock exchange Net operating profit Positive relationship between INV and firm profitability Positive relationship between AR and firm profitability Positive relationship between AP and firm profitability Falope & Ajilore (2009) Working Capital Management and Corporate Profitability: Evidence from Panel Data Analysis of Selected Quoted Companies in Nigeria. Nigeria, nonfinancial firms ROA Negative relationship between INV and firm profitability Negative relationship between AR and firm profitability Negative relationship between AP and firm profitability Garcia- Teruel & Effects of working Spain, listed ROA Significant negative

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 Deloof (2003) Does Working Capital Management Affect Profitability of Belgian Firms? Belgium, nonfinancial listed firms GOI Significant negative relationship between INV and firm profitability Significant negative relationship between AR and firm profitability

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 inventory Days of accounts receivables Days of accounts payables Dummy INV AR AP Days of accounts payables Inventory 365 ( Cost of goods sold ) Accounts receivables 365 ( ) Revenue Accounts payables 365 ( Cost of goods sold ) 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 62

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 INV AR AP ROA GOI CR SIZE DEBT 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 INV AR AP ROA GOI CR SIZE DEBT Notes: Descriptive statistics for the sampled variables for the years 2000, 2004, 2006, 2010 and 2015 for a total of number of 779 observations. 63

64 AR AP 0 CCC GOI INV 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 firmyear 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. 64

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 ). The figures below illustrate that there is a linear relationship between the dependent variable and independent variables. 65