The Effect of Efficient Working Capital Management on Firm
Profitability in the Electronics Manufacturing Industry
Author: Joo Mi Park 10418318
Supervisor: dhr. Francisco Gomez Martinez MSc
Bachelor Thesis
Bachelor Economics and Finance
Faculty of Economics and Business
University of Amsterdam
Abstract
The main purpose of this paper is to examine in which extent the efficient working capital management affects a firm profitability especially in electronic manufacturing companies. This paper provides insights into the importance of cooperation of procurement team and finance team, and the implication of working capital management. In addition to the balanced inventory approach, a cash flow strategy of working capital
management is also examined specifically using the free cash flow measure. This study is conducted using the ordinary least squared method to test the sample of financial performance data, working capital, and cash flow measure from the 11 selected electronic manufacturing firms. The analysis has shown an efficient working capital management is positively related to the firm profitability, though the degree of impact and appropriate verifiable metrics differed by two different regions: Europe and US versus Asia. The result suggests that European and American firms are more plausible to use Cash Conversion Cycle (CCC) and Days of Inventory Outstanding (DIO) for the working capital metrics, while the Asian firms are better to use Net Trade Cycle (NTC), Days of Receivables Outstanding (DRO) and Days of Payable Outstanding (DPO) to analyze the efficiency of working capital management. The outcome presents that both reductions in CCC and days of holding inventories in the European and American firms relate to improvement in firm financial performance, which persisted for the past several years. Moreover, an increase in free cash flow raised the firm profitability for the European and American firms. Meanwhile the effect of cash flow strategies in the Asian firms was inconclusive, however, the result has shown significance in working capital metrics. The reduction in NTC and longer days of payable to the suppliers creditably leads to an improved firm profitability for the Asian firms. Remaining consistent with the earlier research, the detailed findings in this study differed and the following suggestions regarding to the strategic operational activities also differed by its result.
1. Introduction
The debate of therelationship between business strategic plan and financial performance within a firm has been rising for the previous decade. The general idea of the positive effectiveness of internal operational activities on financial performance has been evaluated throughout several research papers (Ou, et al., 2010). For instance, cash flow strategies, costing down on managerial or operational activities and a balanced inventory approach strategized by a procurement team eventually improves the firm performance. However, there has been alack of explicit evidence on how the operational strategic plans either indirectly or directly affect the financial performance. Therefore, this paper reviews the electronic firms’ strategic plans associated mainly with balanced inventory approach and cash flow strategies to observe the impact on firm financial performance.
Working capital management is one of the most important issues in corporate strategy since the capital is strictly related to the value of shareholders. It is not only important from a point of corporate strategic view, but also for the supply chain managers who are heavily involved in optimization of working capital. However, the major issue arises when it comes to balancing the efficiency of working capital management: the profitability liquidity tradeoff (Kargar & Bluementhal, 1994). The greater the liquid assets are held in a firm, the less risky to be out of control in their cash flow strategies, as well as reverse holds. Accordingly, if the supply chain managers intend to increase the working capital, they are tempted to increase the cash amount. However, the controversy over cash strategies generates between the finance team and strategic team. According to the free cash flow hypothesis from a finance theory, holding more free cash harms the stockholders’ value, which accrues disputable decision between the two functional teams. Free cash flow negatively affects financial performance, indicating there is also a trade-off between working capital management and financial performance in some extent. Still finance team cannot disregard strategic cash flow
decisions, since achieving an effective financial management composes the three major areas: capital structure, capital budgeting, and working capital management (Attari & Raza, 2012). Therefore, both balancing inventories and free cash flows at the same time is increasingly being an important matter within a firm.
The objective of this paper is to see the impact of the working capital management and free cash flow strategies on the financial performance, which also stresses the importance of balancing the focal point of procurement and finance team. The procurement team is defined as obtaining goods or services for the operational activities by running a strategic supply management and risk management, as well as planning the purchasing plan for the firm. Meanwhile, the finance team book keeps the purchases and sales by creating financial statements both for internal and external purposes. The difference in functions of these two departments still embraces a link, which explains the necessity of collaboration and will be examined throughout this paper. Even though the finance team generically manages cash payment and collection, influence on the cash flow is predominantly affected by the strategic decisions, the procurement team.
In the case of the company Philips, the procurement team is currently focusing on excavating potential savings from spend and savings pattern of the stakeholders. Moreover, the procurement team is boosting the supplier optimization process to stabilize their supply base management, eventually to stabilize the payment from suppliers and minimize the costs of negotiation. The strategies are academically supported by the Aberdeen Group’s research that working capital and cash flows which both are the financial statement’s elements, can be improved either by extending Days of Payable Outstanding or reducing Days of Sales Outstanding (2008). Several research on supply chain finance also evidenced that improved transparency of spend and reduced operational costs, enables better predictions on cash flow strategies which also lowers working capitals, as well as reduces the costs related with collection and payment processes (Belin, et al., 2011). Therefore, this paper will investigate whether thisresearch is applicable to Philips as well as in the industry wise, using the measures of gross operating profitability and the working capital metrics. This focus was chosen since the electric or electronic companies are manufacturing as well as selling products to the customers, thus they are situated in between of suppliers and customers.
The remainder of this paper follows firstly by discussing the theoretical framework of the working capital management. As well as the working capital efficiency on liquidity and profitability and its empirical literature will be reviewed. Additionally, the free cash flow hypothesis will be addressed in order to view the impact of extra cash generated from the working capital management on firm performance. The next section will present the research methodology and descriptive
analysis with a setup of possible hypotheses. Finally, findings of the empirical study based on the Ordinary Least Squared regression will be discussed and conclude the result of the hypotheses. The conclusion section will summarize the study by answering the research question and providing recommendations for the electronic firms based on the findings of this paper.
2. Literature review
2.1 Financial impact of operational planning
In prior to the discussion on working capital management and free cash flow hypothesis, it is important to view the importance ofthe cooperative involvement of all the internal and external
parties related to a firm. The stock prices, which represent the shareholders’ value, are positively associated with the activities of well-informed parties within a company (Piotroski and Roulstone, 2004). This finding has been verified by considerable researchsuch as a study of the positive correlation between internal operational performance and financial performance or customer satisfaction. Also the external operational performance especially, the supply chain management has been observed to be positively related to an improved operational performance and its
competitiveness (Li et al., 2006). Therefore, the supply chain management has been considered to be a strong tool for firms to increase their business performance, operational efficiency and its growth. In the supply chain management, the interaction of the buyers and strategic managers are regarded to be a key to improve the quality of management plan (Out et al., 2010). The results of their empirical study using Pearson correlations and model validity statistics shown that indeed operational performance effects positively and significantly both on financial performance and customer satisfaction (a coefficient of 0.397 at the 0.01 significance level). Moreover, the supplier management brings an improvement of operational performance significantly as a coefficient of 0.382 at the significance level of 0.01 as well. Therefore, theseveral empirical evidence brings a strong reason for supply managers to achieve its efficient management within a cooperation of strategic, process management from a procurement team. Eventually, enabling to aim for a better financial performance and its profitability, which is the goal of every firm and for the whole internal operators.
2.2 Working Capital Management
Efficient working capital management (WCM) is a crucial element of the overall corporate strategy and supply chain management to promote firm’s profitability as well as to create an improved shareholder value. Smith (1980) has first founded that acknowledging the trade-off between liquidity and profitability arising from the working capital management is vital. Both maintaining liquidity of a firm and maximizing profit is important and those are the main ambition of any firm. Since the two factors are contradicting, balancing the both objectives are challenging for most of the firms. However, a well-managed working capital enhances a company’s position in the market in terms of liquidity and growth of shareholders value (Jeng-Ren et al., 2006). The liquidity term which will be constantly used, refers to the factor from the formula of working capital. Working capital is
calculated as current asset minus current liability. Current assets are in the form of inventories and cash, whereas current liability can be interpreted as financing the current assets. The higher the relative fraction of liquid current assets, the lower the risk of obtaining cash as all the other factors are assumed to be equal. This framework is referred to the financial concept that riskier investment needs to be compensated by generating a higher return. Applying this to the WCM, higher liquidity means the risk is lower but has to bear lower profitability.
The WCM composes the most two important parts: Inventory management and Cash management. Each of them includes individual components of the financial statement, such as cash, convertible securities, account receivables and payables. In order to examine the efficiency of
working capital management, it is essential to have a proper measure of each inventory management and cash management. As predominantly used in most of the researches, the Cash Conversion Cycle (CCC) can measure the cash management; meanwhile, the Net Trade Cycle (NTC) can measure the inventory management.
2.2.1 Cash Conversion Cycle
The cash management of a firm is more dependent on the operating cash flow from holding assets rather than the liquidation value of the asset itself (Shin & Soenen, 1998). Therefore, the Cash
Conversion Cycle (CCC) brings high value to the measurement of WCM as it indicates the
correspondence of the flow of cash and working capital. The scales effectively represent the time lag between the cash collection from the debtors and the expenditure or payment to the suppliers for purchases (Padachi, 2006). Gitman (1974) initially developed the formula of CCC length in days as follows:
CCC days = Days of Receivables + Days of Inventory – Days of Payables
According to Gitman (1974), the total days of this cycle calculates from the time when firm pays for purchases of the raw material till the time when the firm eventually collects cash from the sale of the final goods. Also, one of the classic papers that is written by Richards and Laughlin (1980) phrased the CCC as the net time interval of the cash flow between the purchases and the moment of recovery from the final goods sales. Thus, this ongoing cash flow refers to the liquidity. The following Figure 1 is an illustration of the both concepts:
Figure 1: The cash conversion cycle
As the definition of CCC explains, CCC could be positive or negative depending on the both sides of account receivables and payables. Specifically, a positive CCC means that there is a time lag as days between collecting cash from account receivables and paying account payables, which must align this lagging period with additional funds or any liquid assets. On thecontrary, a negative CCC indicates that the firm collected cash from the debtors earlier than before the firm needs to payout to their creditors (Uyer, 2009). In other words, the firm is prosperous to receive the cash collection as fast as possible, meanwhile delay the cash payments as late as possible. Therefore, a company mostly prefers to have their CCC as minimum and even preferably to be in negative, and considers the change in CCC as a dynamic measure of the firm’s liquidity (Attari & Raza, 2012). Additionally, Attai and Raza (2012) proved that this holds finely well throughout the industries and regardless of the firm size and its profitability.
Each of the measures of this WCM metric, however, has different extent of the impact on CCC.
Days of Receivables Outstanding (DRO) is defined as the average time demanded to receive payments from the point when thesale occurs (Kroes & Manikas, 2013). As the average time actualize quicker to receive payments, the more other activities engagement for the firm is possible
with the earlier collected revenue (Bauer, 2007). Therefore, shorter DRO reduces the CCC days in which improves the cash flow and its financial performance (Churchill & Mullins, 2001; Farris & Hutchison, 2002).
Days of Inventory Outstanding (DIO) is defined as the average time lag between the period of holding in inventories and the moment when the inventories are sold (Kroes & Manikas, 2013). Despite to the widely examined in the literature, defining the relationship between inventory and financial performance is not straightforward due to the complexity over the characteristics of holding inventories (Shah & Shin, 2007). Inventory is still an asset and necessary to hold a considerable amount of inventory in order to provide goods on time correspondingly to the demand. However, in general, lots of studies describe as shorter inventory holding periods accordingly means better liquidity and less CCC days as well as better in the financial performance (Capkun et al., 2009).
Days of Payables Outstanding (DPO) is defined as the average time taking to pay its creditors (Kroes & Manikas, 2013). As DIO, designating the exact relationship between DPO and financial performance is not simple. Longer payment term grants the firms to hold cash longer, which improves the liquidity (Bauer, 2007). However, this may harm the relationship with the suppliers or harm the quality of the materials (Fawcett el al., 2010). Reversely, Farris & Hutchison (2002) shown in their case, higher-performing firms have longer DPO and Kroes & Manikas (2013) also proposed that DPO does not have significant impact relatively on CCC through an empirical research on
manufacturing firms in US. Finally, the formula of CCC represents that principally longer DPO induces shorter CCC days and better firm financial performance.
2.2.2 Net Trade Cycle
The efficiency of inventory management can be measured with the Net Trade Cycle (NTC), which basically shares the same concept as CCC but displaying all the components as a percentage of sales (Shin & Soenen, 1998). The NTC is more closely related to the shareholders value, and the firm value since it indicates the necessary days of sales to finance the firm’s working capital. Therefore, as in align with the CCC, the shorter the NTC is preferred as it generates the higher the present value of the cash flow, and the higher the financial performance (Shin & Soenen, 1998). In conclusion, both CCC and NTC predict an inverse interrelation between the profitability and the WCM metrics. Even though, Soenen (1993) investigated that opposed to the result of CCC, the negative relationship of NTC differs across industries, this outcome generally applies to the most of the firms.
2.3 Free Cash Flow hypothesis
As the free cash could be more generated from the WCM, the impact of extra free cash flows on the firm financial performance should be also considered. The effect of WCM can be directly transformed into cash form as it directly reduces the inventory costs or any other management costs. This can be recognized from the definition of free cash flow as operating cash flows minus capital expenditure, inventory cost, and dividend payment, according to Jensen (1986). Meanwhile, the indirect effect of WCM is the spillover budget that can be used in future activities, which also affects the financial performance in the end.
The prominent and traditional free cash flow hypothesis explains that free cash flow negatively affects the financial performance (Jensen, 1986). In the same paper, he stated that the management is prone to invest on the negatively expected or unnecessary Net Present Value (NPV) projects merely for the sake of their own interest, in the case when the firm possesses too much free cash; namely, agency theory. Furthermore, the investors are reluctant to the fact that the firm holds excessive cash instead of investing more on positive NPV projects. However, both statements are
failed to define exactly in which extent the excessive cash flow has the negative impact on agency costs and the investors’ value (Wang, 2010).
Opposed to the original free cash flow hypothesis, recent research papers have empirically found that FCF variables have lack of evidence to fully support the FCF hypothesis. As Gregory (2005) found in his empirical research on UK firms that FCF is actually positively associated with the firm value, operation performance and stock return measures such as Return on asset and equity. Moreover, Zararee & Azzawi (2012) argued that FCF to Equity have significant positive relationship with the stock value of a firm. These findings are also consistent with empirically examined of Taiwan firms in Wang (2010), which FCF could be a result of efficient cost management so that FCF is
contrarily related to the expense ratios. Thus, operating efficiency in management might generate an inverse relationship between FCF and agency costs. Latest papers have suggested that still the impact of FCF on firm performance should be examined across the industries, which in this paper will focus on the electronic industry over the world but still limiting the geographic scope as Asian electronic industry to European and American electronic industry.
3. Research Methodology
3.1 Data and Research design
The sample data is based on the top-10 average revenue electronic firms in the world, and the company Philips for the years of 2005 to 2014. Philips is preparing its sector split due January 2016 and it is worthwhile to examine the effect of WCM critically comparing to the competitors.
Eventually, the analysis adds value to Philips for the strategic preparation of being two stand-alone companies. The financial data for this study is collected yearly base from 110 annual reports from the balance sheet, profit and loss account, and cash flow statement. The data is all book values since most of the companies do not provide the market values of each balance sheet item publicly. The company Philips will be examined separately, to see if this company’s observations are in a compliance with the rest of the industry and its competitiveness within the industry. To have an extensive analysis on this specific firm, unlikely to the other firms, Philips’ financial data is collected quarterly over the same period for a better statistical significance. For the purpose of this research, industries are separated into Asian electronic industry and European and American electronic industry. The analysis results shown different characteristics of WCM on regional difference. Also, Asian electronic firms ranked higher on their average revenue than European or American electronic firms. Therefore, this study is not necessarily inspecting only on the regional difference, but possible to acknowledge the difference in effect of WCM between higher revenue yielded firms and less profitable firms at the same time.
This research is firstly conducted by a descriptive analysis and a Pearson correlation analysis of the model’s variables in the industry level to provide an insight of the relationship between each variable. The result of the descriptive analysis will support the idea of constructing three main hypotheses as well as four sub-hypotheses for each main hypothesis to evaluate the research question of this paper. The hypotheses will be tested further in depth based on the ordinary least regression analysis.
3.2 Measurement of Variables
Measures for the working capital management (WCM) and profitability were adopted and modified from Makori & Jagongo (2013), Deloof (2003), Shin & Soenen (1998), in order to remain consistent with previous research. Return on Asset (ROA) is widely used measure of a firm profitability in the finance field. Thus, ROA is the dependent variable of the models tested throughout the paper. Referring to the literature, WCM could be sensitive to the free cash flow as it is a critical proxy for estimating whether internal sourcing for reinvestment or external sourcing as cash is better for the firm. Free cash flow is a factor of the cash flow management and included as an independent variable of the models. As explained in the previous section, the WCM metrics, which are the DSO, DIO, DPO, CCC, and NTC, are the critical independent variables. The variable CCC and NTC are comprehensive variables as they incorporate the variables of DSO, DIO and DPO in a different form. So it is possible to investigate either the consolidated effect or separated effect of each component variable. The gross operational profitability is also included as an independent variable. Since the effect of growth opportunities on working capital can be the result of business operational efficiency and causes positive impact on the profitability as well. Besides, firm specific variables related to the firm’s financial activity would be excluded from the research. The level of financial investment should be fixed to merely capture the effect of WCM on profitability. Therefore, the financial debt ratio and debt ratio enacts as the control variables. The dummy variable represents the region as 1 denotes EU and US, while 0 indicates Asia. Table 1 explains how each variable is measured:
Table 1: Measurement of Variables
3.3 Model Specification
To determine the efficiency of WCM in the electronic industry, eleven explanatory variables as mentioned above, will be included in the model depending on the hypothesis. The basic form of the model presents as follow:
𝑅𝑅𝑅𝑅𝐴𝐴𝑖𝑖 = 𝛽𝛽0+ 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖+ 𝛽𝛽2𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖+ 𝛽𝛽3𝑁𝑁𝑁𝑁𝐹𝐹𝑖𝑖+ 𝛽𝛽4𝐺𝐺𝑅𝑅𝐺𝐺 + 𝛽𝛽5𝐹𝐹𝐹𝐹𝑅𝑅 + 𝛽𝛽6𝐹𝐹𝑅𝑅 + 𝜀𝜀
The consolidated variable CCC can be individualized into each component variable to specify the effect on profitability while the other conditions remain equal. Moreover, CCC and NTC are
exchangeable variables to explain the effect of WCM by their significance level in the model, thus a better appropriate WCM variable can be decided based on the model’s result. The extensive form of the model represents as follow:
𝑅𝑅𝑅𝑅𝐴𝐴𝑖𝑖 = 𝛽𝛽0+ 𝛽𝛽1𝐹𝐹𝐹𝐹𝐹𝐹𝑖𝑖+ 𝛽𝛽2𝐹𝐹𝑅𝑅𝑅𝑅𝑖𝑖+ 𝛽𝛽3𝐹𝐹𝐺𝐺𝑅𝑅𝑖𝑖+ 𝛽𝛽4𝐹𝐹𝐷𝐷𝑅𝑅𝑖𝑖+ 𝛽𝛽5𝑁𝑁𝑁𝑁𝐹𝐹 + 𝛽𝛽6𝐺𝐺𝑅𝑅𝐺𝐺 + 𝛽𝛽7𝐹𝐹𝐹𝐹𝑅𝑅 + 𝛽𝛽8𝐹𝐹𝑅𝑅 + 𝜀𝜀
Variable Abbreviation Measurement
Return on Asset ROA
Free Cash Flow FCF (Operating CF-Income Tax-Interest-Dividend)/Net Sales Days Receivables Outstanding DRO Account Receivables/Sales*365
Days Payables Outstanding DPO Account Payables/Sales*365 Days Inventory Outstanding DIO Inventory/Cost of Goods Sold*365 Cash Convention Cycle CCC DRO+DIO-DPO
Net Trade Cycle NTC (Net Sales/Net Working Capital)/365
Gross Operational Profitability GOP (Sales-Cost of Good Sold+Depreciation)/Sales Financial Debt Ratio FDR Financial Assets/Total Assets
Debt Ratio DR Liabilities/Assets
region_dum region_dum Dummy=1 European& American firms Dummy=0 Asian firms
4. Data Analysis and Results
4.1 Descriptive Analysis
Descriptive analysis presents the mean, standard deviation, and minimum and maximum values of the collected variables. The output is sourced from STATA.
Table 2: Descriptive Statistics of Variables for Electronic Firms Globally (2005-2014)
Table 2 provides an overview of the performance of the selected 11 firms in this research. The average return on asset is 2.59% with a standard deviation of 4.38%. The mean free cash flow ratio is 54.41% which means that approximately 54.41% of operating cash flow generates from sales per currency unit. Also, the mean cash conversion cycle is 75 days in this industry, approximately two and a half month takes to convert the raw materials into the final cash collection of the sales of
production. However, the deviation range is remarkably large, as the minimum measure is negative 46 days, while the maximum value is 181 days. Therefore, the cash conversion cycle should be
carefully viewed throughout the study. On average, the mean gross operational profitability is 29.89% indicating that every sales in currency unit proceeds 29.89% of gross profit. All of the measures
represent the industry globally, and the observations may differ by region as this paper classified.
Table 3: Descriptive Statistics of Variables for EU & US Electronic Firms (2005-2014)
Among the top-10 average revenue of electronic firms in the world, three of them are originated from EU and US and the statistic overview is presented in Table 3. The company Philips is separately included in this observation. The mean return on asset is 5.40% with a standard deviation of 3.31%. This figure is almost twice higher than the global average return on asset that European and American high profitable electronic firms are generating relatively better profit out of the investment. Comparing to the global financial debt ratio, they are less debt-financing their assets as the figure is 6.70% while the global figure is almost 10%. The mean gross operational profitability is
Mean Std.Dev. Min Max Observations Return on Equity .0522332 .1662832 -.5902115 .3934367 N = 110 Return on Asset .0259091 .0438103 -.1292069 .1398291 N = 110
Free Cash Flow .5440806 2.09728 -2.03738 15.366 N = 110
Cash Conversion Cycle 75.88426 50.54771 -45.97409 180.6639 N = 110 Net Trade Cycle .0076675 .0254767 -.1053183 .204602 N = 110 Gross Operational Profitability .2989098 .1073231 .0381639 .5368664 N = 110 Financial Debt Ratio .0995047 .1148266 .0044948 .5164442 N = 110
Debt Ratio .6537893 .136412 .2705233 .8911449 N = 110
Variable
Obs Mean Std. Dev. Min Max
ROE 40 .1473826 .0872445 -.1063755 .3934367 ROA 40 .0539497 .0331039 -.043919 .1398291 FCF 40 .0992152 .0749735 -.022798 .2763141 CCC 40 109.2714 49.41029 20.79537 180.6639 NTC 40 .0091174 .0110017 .0032606 .072885 GOP 40 .2751966 .0449061 .2099771 .3958733 FDR 40 .0669869 .0690564 .0085411 .2866197 DR 40 .6201818 .0997027 .3992259 .7951817 Variable
slightly lower than the global figure as around 2.5% difference. Coincidently, the cash conversion cycle is comparably high as 109 days compared to the global industry level as 75 days, which they are operating less efficiently in working capital compared to the rest of the industry. Furthermore, they are generating less free cash flow over sales ratio as 9.92% meanwhile globally it measured as 54.41%. The ability of a firm to achieve extra cash after paying obligatory payment for instance the operating expenses, is relatively less than contrasting to the rest of the world. The findings cannot define the cause and effect relationship from this descriptive statistics, which can be verified in the correlation analysis.
Table 4: Descriptive Statistics of Variables for Asian Electronic Firms (2005-2014)
Majority of the top-10 average revenue of electronic firms in the world, which are seven firms from the collected samples are based on Asia. Table 4 shows the descriptive statistics of those profitable electronic firms. Regardless to their high revenue, the return on asset is relatively low as 1% while the European and American firms shown it as 5.40%. Moreover, the assets are debt-financing more with an 11.81% which is about 5% higher than the EU and US located firms. However, the gross operating profitability is around 4% higher than the European and American firms which are
benefitting from their efficient management. The most notable fact of this statistics is the mean value of free cash flow which is approximately 80% compared to the ratio in EU and US of 9.92%. Realization of extra free cash from their sales is extremely high which should be thoroughly investigate the behind relationship of the variables since the deviation range is greatly large.
4.2 Correlation Analysis
Correlation analysis provides an insight of relationships among the explanatory variables. The analysis empowers the basic idea of structuring hypotheses that are relevant to answer the research question. Contrarily to the previous descriptive analysis, this section delivers a detailed analysis which is separated to Philips, EU and American, and Asian firms. These outputs are also sourced from STATA.
Obs Mean Std. Dev. Min Max
ROE 70 -.0021378 .1764357 -.5902115 .1642659 ROA 70 .0098859 .0411866 -.1292069 .0777346 FCF 70 .7982895 2.600966 -2.03738 15.366 CCC 70 56.80587 40.50799 -45.97409 147.1043 NTC 70 .0068389 .0309031 -.1053183 .204602 GOP 70 .3124602 .1286213 .0381639 .5368664 FDR 70 .1180864 .1310344 .0044948 .5164442 DR 70 .6729935 .1508247 .2705233 .8911449 Variable
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
ROA 1.0000 FCF 0.5181* 1.0000 DRO -0.0102 0.0084 1.0000 DPO -0.1062 -0.0368 0.6763* 1.0000 DIO 0.0610 0.0552 0.6184* 0.6998* 1.0000 CCC -0.1055 -0.0389 0.8298* 0.9592* 0.6237* 1.0000 NTC 0.2265 0.0995 -0.1252 0.0115 0.0986 -0.0590 1.0000 GOP -0.4086* -0.1386 0.2194 0.6258* 0.2380 0.5619* 0.1976 1.0000 FDR 0.4989* 0.1976 -0.3132* -0.5060* -0.2945* -0.4862* -0.0491 -0.7606* 1.0000 DR -0.4176* -0.3190* 0.3981* 0.3562* 0.1971 0.4133* -0.0234 0.3713* -0.4117* 1.0000
Table 5: Pearson Correlation Analysis for Philips (2005Q1-2015Q1, N=41)
Table 5 shows that there is a negative relationship between ROA and the measures CCC, GOP and DR. Consistent with the most of the previous research (Deloof, 2003), ROA is negatively related to CCC in this case as well. The shorter time lag between sales and the collection of payment is hence
increasing profitability. Meanwhile, NTC which is a similar measure to CCC shows a positive
relationship with ROA. Both measures are not significant enough within 10%, this relationship cannot be justified yet. The positive relationship between ROA and FCF is incompatible to the original and traditional FCF hypothesis. However, this findings support the recent studies regarding to the opposed empirical result to the FCF hypothesis.
* indicate a significance level of 10% (STATA Output)
Table 6: Pearson Correlation Analysis for EU & US Electronic Firms (2005-2014, N=40)
* indicate a significance level of 10% (STATA Output)
Table 7: Pearson Correlation Analysis for Asian Electronics Firms (2005-2014, N=70)
Table 6 imposes a compatible result with Philips result in table 5, even though Philips is also a European company. One notable difference from table 5 is that DIO is significant for negatively related to ROA and CCC as it is a component of the formula. As discussed in the literature review, DIO adds up to the calculation of CCC, in which shorter is preferred to achieve shorter CCC. Thus, taking the components of CCC into account may have indication to the hypothesis analysis on industry level. Table 7 provides a contradicting result as ROA is negatively related to FCF, NTC, and FDR. The free cash flow hypothesis states obtaining more free cash actually harms the business since it opposed to the investors’ interest. Based on the sample of Asian electronic firms, it also explains that GOP is positively related to ROA within a significance level while it does not apply to European and American electronic firms. Thus, this proves that efficient operational activities improve the firm’s profitability. In contrast to Philips and the other European and American firms, Asian firms are observed that CCC
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
ROA 1.0000 FCF 0.1113 1.0000 DRO -0.0079 -0.0009 1.0000 DPO -0.0342 -0.2999* 0.5917* 1.0000 DIO -0.4014* -0.1745 0.4263* 0.1839 1.0000 CCC -0.1450 0.0374 0.8902* 0.2777* 0.7073* 1.0000 NTC 0.0072 0.3371* 0.0147 -0.2225 0.0524 0.1097 1.0000 GOP -0.1453 -0.4180* 0.0599 0.3381* 0.1476 -0.0054 -0.0412 1.0000 FDR 0.2799* -0.1857 -0.2007 -0.4158* 0.2976* 0.0660 0.0188 -2.578* 1.0000 DR -0.2953* 0.5652* -0.3020* -0.6809* -0.0364 -0.0617 0.2829* -0.3569* -0.0160 1.0000
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
ROA 1.0000 FCF -0.0903 1.0000 DRO -0.0042 -0.2439* 1.0000 DPO -0.0000 -0.0320 0.2763* 1.0000 DIO 0.0249 -0.3340* 0.6976* 0.1784 1.0000 CCC 0.0060 -0.2790* 0.8258* -0.2532* 0.7692* 1.0000 NTC -0.1229 -0.0510 -0.0396 -0.0955 0.0185 0.0275 1.0000 GOP 0.3996* -0.4594* -0.0548 -0.2889* 0.1745 0.1728 0.0985 1.0000 FDR -0.1247 0.5749* -0.2575* 0.2243* -0.3108* -0.4124* -0.0819 -0.7681* 1.0000 DR -0.5778* 0.1100 0.2903* 0.3968* 0.2758* 0.1031 -0.0525 -0.5371* 0.2824* 1.0000
is positively related to ROA though the power of relativeness is not high. Both WC metrics are not significant, thus the effect of Asian firms should be also viewed in a detailed model. Nevertheless, each component of CCC shows that the findings associate with the theory of WCM. DRO and DIO have a positive relationship with CCC, and DPO is negatively related to CCC with a high significance level. Meaning that the high revenue companies in the world relatively manage their inventory fairly well that the result fits into the theory.
4.3 Hypotheses construction
Efficient working capital management under the control of the procurement team is assumed to bring a positive impact on financial figures in the electronic firms. Moreover, the effect could be also related to the free cash flow and expected an impact of free cash on its profitability. Thus, the liquidity-profitability tradeoff of the WCM should be estimated. Finally, the aim is to show abundant arguments why procurement team and finance team within a firm should be closely related.
Based on the general findings from the descriptive analysis and the assumptions, hypotheses can be formulated as follows:
H1: WCM has no relationship with the profitability in the case of Philips H1.1: FCF has no relationship with the profitability in the case of Philips H2: WCM has no relationship with the profitability in EU&US electronic firms H2.1: FCF has no relationship with the profitability in EU&US electronic firms H3: WCM has no relationship with the profitability in Asian electronic firms H3.1: FCF has no relationship with the profitability in Asian electronic firms
Due to the significance level, the specific variables of WCM metrics should be specified by the hypothesis. Sufficient sample data collected for Philips case compared to other companies and it enabled both variables, CCC and NTC, to be significant enough for testing the hypotheses. For the EU&US electronic firms, only CCC was comparable for the measurement than NTC. The Asian
electronic firms, on the contrary, shown a stronger significance on NTC. Therefore, it is meaningful to evaluate the impact of the components of CCC on ROA for both samples since the appropriate WCM variable for each model is not consistent. The sub-hypotheses are listed as follows:
H2.2: DRO has no relationship with the profitability in EU&US electronic firms H2.3: DPO has no relationship with the profitability in EU&US electronic firms H2.4: DIO has no relationship with the profitability in EU&US electronic firms H3.2: DRO has no relationship with the profitability in Asian electronic firms H3.3: DPO has no relationship with the profitability in Asian electronic firms H3.4: DIO has no relationship with the profitability in Asian electronic firms
Figure 2: Structure of Models and Hypotheses
4.4 Regression Analysis
Regress on ROA
Model 1 Model 2 Model 2.1 Model 3 Model 3.1 Parameter N = 41 N = 40 N = 40 N = 70 N = 70 FCF .0011823*** (.0004292) .2202525** (.0842162) .1694155** (.0719095) (.0018644) -.0007318 (.0018086) .0005048 DRO .0002036 (.0001204) .0002731* (.000207) DPO -.000079 (.0004733) .0004087** (.0002055) DIO -.0011944*** (.0002953) (.0004008) .0002261 CCC .0000684** (.0000318) -.0001535* (.0000932) (.000111) .0001459 NTC 1.945291** (8.409043) (.4519183) .0878279 .0443854 (.38126) (.1281474) -.217044* -.1869769* (.1210691) GOP -.1562023* (.1014481) (.1229228) -.047125 (.1089885) .0836665 .1363243** (.0569063) .1491272*** (.0593035) FDR .0729855 (.0464725) .1729181** (.07309) .2926927** (.0887466) (.0559325) .129248** .1270745** (.0548861) DR -.0248835 (.0165916) -.204752*** (.0568399) -.143463** (.0729755) -.1320444*** (.0324047) -.1673842*** (.034081) regionDum 1 1 0 0 _cons .0458374 (.0420687) .1764344** (.0549788) (.0773254) .163523** (.0386955) .0346744 (.0393662) -.0064689 F( 6, 34) = 7.13 F( 6, 33) = 3.39 F( 8, 31) = 5.58 F( 6, 63) = 7,71 F( 8, 61) = 7,79 Prob > F 0.0001 0.0103 0.0002 0.0000 0.0000 R-squared 0.5571 0.3812 0.5900 0.423 0.5054
*, **, and *** indicates a significance level at 10%, 5%, 1% levels respectively (STATA Output)
Model 1 tests the two hypotheses of Philips. An efficient WCM means lower CCC and NTC is
preferable as discussed earlier in the literature review. Thus, negative coefficients of the WC metrics denote that one is efficiently managing the working capital. However, the regression result indicates that both CCC and NTC of Philips have a positive relationship with ROA. Thus, the hypothesis 1 is rejected with a significance level of 5%. Though the coefficient of CCC is almost zero of .0000684 (within 5%), which has not a considerable impact on ROA, it still has a positive impact on ROA. Thus, the profitability and liquidity tradeoff issue is less substantial for Philips; in other words, the
profitability is less sensitive to the liquidity of asset or cash. The result opposes to the initial result of descriptive analysis in table 5 as it shown a negative relationship between CCC and ROA, since the correlation was insignificant and Pearson correlation analysis does not identify the causality. Even though the finding is not matching with the WCM theory, still it is consistent with the paper of Attari and Raza (2012). The implication is that longer the CCC turnover in days, lesser capital needs to be engaged in current assets instead in fixed assets. If the company has more capital investment, it induces higher profit in the company. Therefore, Philips is more beneficial to use money on the fixed assets such as machineries, land or on the capital expenditures rather than to cover the business operating expenses. The result supports the way that Philips is aiming to lower the internal operating expenses. The capital investment may also include the financial investment, but not necessarily. Since the financial asset ratio is not notable in model 1, it is uncertain whether increasing financial investment is effective to the capital investment.
Free cash flow is positively related to ROA with .0011823 at a high significance level at 1%. Therefore, the hypothesis 1.1 is also rejected. Holding more cash is profitable for Philips, which enhances the importance of cash management. As Gregory (2005), Wang (2010), Zararee & Azzawi (2012) already found that the efficient operating cash flow has a positive effect on the stock market, and the conclusion holds in this study for Philips as well. The interpretation is that the investors of Philips are relatively less concerned to agency costs that may occur from extra free cash, as the conflicting interest of investors and managers is a major issue in the free cash flow hypothesis. Instead of focusing on investment of high NPV projects, the focus should remain on efficient cash flow management. Also, relating to the result of hypothesis 1, it is desirable to emphasize the efficient cash flow management and earn or hold more cash attained from the efficient WCM. The overall model 1 is statistically significant, as the F-value displays 7.13 with significance of .0001. The measure of R-squared indicates that approximately 55.71% of the deviation in the profitability in terms of ROA can be identified by the model 1.
Model 2 emphasizes the importance of the two WC metrics in profitability of European and American firms. Hypothesis 2 is rejected in terms of CCC but not rejected using NTC. The CCC has a negative relationship with ROA as a coefficient of -.0001535 at a significance level of 10%. However, the NTC is not significant enough to conclude the hypothesis. Examining the separate components of CCC is required in this case, to have an accurate test on the model representing the European and American firms. Contrary to Philips, the electronic firms generally in Europe and America, prefers shorter CCC to achieve a higher profit. The result is consistent with the papers of Shin & Soenen (1998), Padachi (2006), Richards and Laughlin (1980), which are the prominent papers for WCM. Matching the timing of cash expenditure and final collection of payments is important to be less risky in financing their operations. Also, the liquidation of assets value is important as it implies the net time interval should better to be short.
Model 2.1 uses alternative specifications, DRO, DPO, and DIO, to have better accurate measures of WCM for European and American electronic firms. The variables DRO and DPO did not show significantly important to explain in which extent the days of receivables and payables
outstanding on average had direct impact on ROA. Though DIO has shown a strong direct impact on ROA with a negative relationship. Following the theory of WCM, the performance of European and American firms benefits strongly by managing their days of holding inventories. These firms are prone to the liquidity problem and maintaining a shorter day of holding inventories is critical to the profitability, means that a firm can profit easily by converting its inventories to cash. According to the regression analysis, decreasing 1% of DIO increases 0.12% of ROA. Therefore, the hypothesis H2.4 is rejected at a 1% level. Meanwhile, the hypotheses H2.2 and H2.3 cannot be concluded which
indicates that the result cannot verify the exact effect of DRO and DPO on ROA specifically in EU & US. Free cash flow is positively related to ROA in the both models, thus the hypothesis H2.1 is rejected both at the 5%. Generating more cash from their operational efficiency is important for the firms. Kroes and Manikas (2013) verified DIO and DSO are the effective measures for cash flow management for the manufacturing firms. The findings of this paper’s case can also be linked that operating efficient inventory management generates extra free cash or more liquidity, which both DIO and FCF variables are positively associated with ROA. The overall model 2.1 is also statistically plausible with a F-value of 5.58 (at p> .0002).
Model 3 examines the case of Asian electronic firms, and covers most of the top 10 high revenue firms in the world. The regression analysis implies that hypothesis 3 is rejected in terms of the NTC variable with a coefficient of -.217044 at 10% level. The net trade cycle is negatively related to the ROA as the theory explains; shorter NTC implies less day sales required financing its working capital and the findings of model 3 supports it. Therefore, the firms with shorter NTC also require less external financing and intensifies their financial performance. The examination of model 3 backs the explanation since the financial asset ratio is positively associated with its profitability (a coefficient .129248 at a 5% level), while the leverage level has a strong negative relationship with the
profitability (a coefficient -.1320444 at a 1% level). According to the definition of NTC explained in the earlier section, the evidence from model 3 denotes that higher sales are important to decrease NTC and especially in Asian electronic companies. Though NTC is also a measure of WCM, it has more emphasis on sales under the WC and on cash management than the amount of inventory itself. Thus, the prediction of DIO effect on ROA is assumed to be insignificant in Asian electronics firm. The alternative model is imposed to specify the effect of CCC into its components.
Model 3.1 extensively explains the influence of DRO and DPO on profitability at a significance level 10% and 5% respectively. On the contrary, DIO does not clearly provide a relationship with ROA. Generally speaking, a firm will benefit by reducing the DRO and increasing DPO. The findings of model 3.1 show mixed results as both measures impact a firm positively. This implies the Asian electronic firms are more influential to days of paying to their suppliers than days of receiving from their customers. The findings hold that more profitable firms, which are Asian electronic firms in the sample, are able to wait longer paying their bills. Longer payment cycle certainly improves firm liquidity and the Asian electronic firms empirically supports by its evidence. The coefficient measures of NTC, FDR, and DR are still consistent with the result from model 3. Therefore, hypotheses 3.2 and 3.3 are rejected but hypothesis 3.4 is inconclusive and cannot be rejected. Additionally, hypothesis 3.1 regarding to the free cash flow cannot be rejected as well by the given result. The effect of free cash flow on the firm’s valuation or on the investors’ interest is vague to conclude and discriminative explanatory variables are required for a thorough examination.
5. Conclusion
5.1 Conclusion and Implications
This paper researched whether an efficient working capital management of electronic firms is
positively related to its financial profitability in a scope of geography as European and American firms versus Asian firms, covering the period of 2005 to 2014. This study found that efficiently managed working capital generally impacts positively on its profitability in terms of ROA, though an
appropriate measure differs by the result. Thus the profitability-liquidity tradeoff issue is important in any firm. The case of Philips shown that both WCM metrics are appropriate to use and the sample size is reasonable enough to explain it. The European and American firms are better measuring the WCM efficiency by CCC and additionally, only DIO can be specified its effect on ROA. Meanwhile, the Asian firms are better evaluating the WCM with NTC with respectively DRO and DPO are applicable as measurement. The results imply different extent of efficient WCM affects a firm’s profitability and their focus of strategic management should be different accordingly. However, the impact of free cash flow that could be generated from WCM on its profitability cannot be generalize as it is only significant in European and American electronic firms but not in Asian electronic firms.
The findings conclude in general, performance of a procurement team actually adds value to the firm’s profitability either directly or indirectly. Therefore, the result supports the rationale of the two different operational team’s cooperation. However, the detailed ways of collaboration should be considered accordingly to the market situation and the uniqueness of the firm’s situation in the world market. The analysis of Philips shows that the CCC is not a critical value for their profitability and actually shown that it is better to have a longer cash cycle. This phenomenon can be potentially explained as the suppliers of Philips have been stably supplied the company for very long time and already have a strong connection between the parties. Therefore, rigorously negotiating or cutting off the suppliers might be even harder for Philips compared to the other manufacturing firms. Otherwise, Philips may harm the relationship between the suppliers and eventually may cause worsen terms of supply management. Nevertheless, balancing the level of inventories is important for Philips as well, however, receiving or paying cash from the suppliers is relatively less influential to its profitability. As shown in the result, holding more free cash is significantly profitable for Philips and this can be achieved from its capital investment following from the reviewed literatures. The implication of Philips result is that finance team should recognize the hindered reasons of hardship in the supplier management of procurement team while they can improve the profitability by
increasing capital management, increasing the amount of cash, and this may influence the decision of procurement management.
Even though the fact that Philips is also based in Europe, this study delivers a different result for other European and American firms that they should especially focus on matching the overall CCC and efficiently holding their inventories. Balancing and maintaining accurate and timely collection period of cash from both suppliers and customers is essential to perform better in the financial aspect. The result provides an implication of shortening days of holding inventories is positively related to the firm profitability. Thus, the function of the procurement team’s supplier and inventory management is relevant to the performance of the finance team. Similar to Philips result, holding more cash for their operational activities is also important to the European and American companies. The finance team of a firm will be assured of their performance while it helps the organization to smoothly operate with sufficient cash in the future.
The Asian firms in this study are recommended to focus on increasing sales and managing the days of collecting bills from their customers. Increasing sales can cause the reduction in NTC, which explains why the Asian firms which are achieving the highest profits in the world, are stringent to increasing sales and profit. In contrary to European and American firms, they are more sensitive to the days of receivables and clearly bring a positive effect on firm profitability when it reduces the DRO. Therefore, this finding also supports that the procurement team performance helps the finance team to realize a better result and indeed the efficient working capital management improves the firm financial performance.
5.2 Limitations and Future Research
This research is limited in which examining only the top 10 average revenue electronic firms. Also, the study examined the effect of FCF on ROA but not whether the WCM has a direct effect on FCF. The result might bring a clear picture of effects of FCF as it failed showing in the Asian firms. The further extension of this study might examine on more firms or develop the scope of the research to the other industries. Also, the supplier base management can take into account for a thorough association of efficient working capital management, which even allows a detailed analysis on the relationship of procurement management and firm profitability. Therefore, the investigation will clarify the relationship of both cash flow and inventory management with the profitability to play successful operations within the firm.
References
Attari, M.A., & Raza, K. (2012). The Optimal Relationship of Cash Conversion Cycle with Firm Size and Profitability. International Journal of Academic Research in Business and Social Sciences, Vol. 2, No. 4. Bauer, D. (2007). Working capital management: driving additional value within AP. Financial
Executive, Vol. 23, No. 8, pp. 60-63.
Capkun, V., Hameri, A.P., & Weiss. L.A. (2009). On the relationship between inventory and financial performance in manufacturing companies. International Journal of Operational Production
Management, Vol. 29, No. 8, pp. 789-806.
Churchill, N.C., & Mullins, J.W. (2001). How fast can your company afford to grow? Harvard Business Review, Vol. 79, No. 5, pp. 135-142.
Deloof, M. (2003). Does Working Capital Management Affect Profitability of Belgian Firms? Journal of Business Finance and Accounting, Vol. 30, No. 3&4, pp. 573-587.
Farris, M.T., & Hutchison, P.D. (2002). Cash-to-cash: the new supply chain management metric. International Journal of Physical Distribution & Logistics Management, Vol. 32, No. 3-4, pp. 288-298. Fawcett, S.E., Waller, M.A., & Fawcett, A.M. (2010). Elaborating a dynamic systems theory to understand collaborative inventory successes and failures. International Journal of Logistics Management, Vol. 21, No. 3, pp. 510-537.
Garcia-Teruel, P.J., & Martinez-Solano, P. (2007). Effects of working capital management on SME profitability. International Journal of Managerial Finance, Vol. 3, No. 2, pp. 164-177.
Gitman, L.J. (1974). Estimating Corporate Liquidity Requirements: A Simplified Approach. The Financial Review, Vol. 9, No. 1, pp. 79-88.
Gregory, A. (2005). The Long Run Abnormal Performance of UK Acquirers and the Free Cash Flow Hypothesis, Journal of Business Finance & Accounting, Vol. 32, No. 5&6, pp. 777-814.
Jeng-Ren, C., Li, C., & Hann-Wen, W. (2006). The determinants of working capital management. Journal of American Academy of Business, Vol. 10, No. 1, pp. 149-155.
Kargar, J., & Blumenthal, R.A. (1994). Leverage impact of working capital in small businesses. TMA Journal, Vol. 14, No. 6, pp. 46-53.
Kroes, J,R., & Manikas, A.S. (2014). Cash flow management and manufacturing firm financial
performance: A longitudinal perspective. International Journal of Production Economics, Vol. 148, pp. 37-50.
Makori, D.M., & Jagongo, A. (2013). Working Capital Management and Firm Profitability: Empirical Evidence from Manufacturing and Construction Firms Listed on Nairobi Securities Exchange, Kenya. International Journal of Accounting and Taxation, Vol. 1, No. 1, pp. 1-14.
Ou, C.S., Liu, F.C., Hung, Y.C., & Yen, D.C. (2010). A structural model of supply chain management on firm performance. International Journal of Operations & Production Management, Vol. 17, No. 6, pp. 596-610.
Padachi, K. (2006). Trends in Working Capital Management and its Impact on Firms’ Performance: An Anlaysis of Mauritian Small Manufacturing Firms. International Review of Business Research Papers, Vol. 2, No. 2, pp. 45-58.
Piotroski, J.D., & Roulstone, D.T. (2004). The Influence of Analysts, Institutional Investors, and Insiders on the Incorporation of Market, Industry, and Firm-Specific Information into Stock Prices. The Accounting Review, Vol. 79, No. 4, pp. 1119-1151.
Richards, V.D., & Eugene J.L. (1980). A Cash Conversion Cycle Approach to Liquidity Analysis. Financial Management, Vol. 9, No. 1, pp. 32-38.
Shin, H.H., & Soenen, L. (1998). Efficiency of Working Capital and Corporate Profitability. Financial Practice and Education, Vol. 8, No. 2, pp. 37-45.
Smith, K. (1980). Profitability Versus Liquidity Tradeoffs in Working Capital Management. Readings on The Management of Working Capital, West Publishing Company, pp. 549-562.
Uyar, A. (2009). The Relationship of Cash Conversion Cycle with Firm Size and Profitability: An Empirical Investigation in Turkey. International Research Journal of Finance and Economics, ISSN 1450-2887, Issue 24.
Wang, G.Y. (2010). The Impacts of Free Cash Flows and Agency Costs on Firm Performance. Journal of Service Science & Management, Vol. 3, pp. 408-418.
Zararee, A.N.A., & Azzawi, A.A. (2012). The impact of free cash flow on market value of firm. Wroclaw University of Economics, Vol. 1, No. 271, pp. 13-21.
Appendix A: Tables
Table 9: Philips Data per quarter from 2005 Q1 till 2015 Q1
1) Comma is substituted from dot.
2) Days are rounded to two decimals, and the other variables are rounded to five decimals.
ROA FCF DRO DIO DPO CCC NTC GOP FDR DR
2015Q1 0,00333 -2,79137 349,48 443,48 199,15 593,82 0,00015 0,44934 0,03652 0,63841 2014Q4 0,00476 4,93210 273,64 314,17 139,56 448,25 0,00012 0,44706 0,03926 0,61315 2014Q3 -0,00384 -2,93407 342,76 390,20 201,94 531,02 -0,00011 0,38633 0,03087 0,60598 2014Q2 0,00941 0,52410 326,52 426,56 194,95 558,13 0,00040 0,46817 0,03157 0,58073 2014Q1 0,00522 -2,26744 370,29 450,89 211,13 610,05 0,00016 0,46036 0,02899 0,57554 2013Q4 0,01481 0,89902 258,87 301,99 132,17 428,69 0,00065 0,48331 0,03076 0,57728 2013Q3 0,00991 0,17457 309,32 391,19 184,45 516,06 0,00049 0,47828 0,02968 0,61538 2013Q2 0,01125 -0,48915 288,53 413,01 176,02 525,52 0,00069 0,47479 0,03218 0,60898 2013Q1 0,00560 -1,30492 310,16 419,54 201,59 528,11 0,00037 0,45721 0,03098 0,57321 2012Q4 -0,01437 -5,37500 257,10 306,06 153,31 409,86 -0,00019 0,43986 0,02968 0,61538 2012Q3 0,00358 1,90157 293,20 416,46 187,92 521,74 0,00022 0,44649 0,02998 0,58991 2012Q2 0,00564 -1,13609 256,47 398,28 168,31 486,43 0,00030 0,44026 0,03256 0,58311 2012Q1 0,00621 0,36070 332,95 422,53 228,82 526,66 0,00025 0,44168 0,03025 0,62660 2011Q4 -0,00565 4,05725 246,99 307,63 181,96 372,67 0,00023 0,42998 0,02686 0,57229 2011Q3 0,00277 -0,42491 285,29 446,82 216,06 516,04 0,00021 0,44049 0,02501 0,53307 2011Q2 -0,04710 0,29564 269,41 432,93 180,89 521,45 -0,00073 0,45936 0,02461 0,51673 2011Q1 0,00619 -1,82704 282,19 413,13 167,60 527,72 0,00023 0,45453 0,02255 0,52874 2010Q4 0,01977 1,66623 219,39 304,04 182,25 341,17 0,00054 0,42384 0,05323 -0,00322 2010Q3 0,01570 0,02317 281,84 468,63 221,74 528,72 0,00041 0,46905 0,02745 0,51214 2010Q2 0,00792 0,23762 257,76 366,68 204,11 420,33 0,00041 0,42481 0,03351 0,53963 2010Q1 0,00918 -0,28021 266,57 344,45 182,60 428,42 0,00034 0,44319 0,03571 0,53998 2009Q4 0,01260 1,09910 200,37 233,42 144,23 289,56 0,00039 0,43192 0,10622 -0,26728 2009Q3 0,00601 0,16953 279,09 330,05 197,66 411,48 0,00167 0,41594 0,03813 0,54570 2009Q2 0,00147 -31,5000 270,50 351,79 178,66 443,64 0,00001 0,40554 0,04088 0,54141 2009Q1 -0,00299 1,24731 280,42 367,54 164,34 483,62 -0,00016 0,38660 0,03744 0,52346 2008Q4 -0,04414 -7,61765 207,76 19,95 143,26 84,45 -0,00014 0,37177 0,04888 0,50840 2008Q3 0,00172 0,37594 402,57 337,76 182,73 557,60 -0,00010 0,36075 0,06827 0,46384 2008Q2 0,02002 -2,10769 277,69 326,84 168,18 436,35 0,00018 0,37630 0,09290 0,44195 2008Q1 0,00590 -3,58857 297,14 339,86 179,84 457,16 0,00010 0,37301 0,12473 0,46691 2007Q4 0,03899 1,38603 207,44 127,06 147,13 187,36 0,00027 0,38027 0,13970 0,40357 2007Q3 0,00919 0,45974 264,97 315,63 179,93 400,67 0,00021 0,36665 0,20430 0,00000 2007Q2 0,04245 -2,38279 278,91 311,34 173,03 417,21 0,00016 0,37991 0,21589 0,39562 2007Q1 0,02302 -0,95833 277,54 288,00 169,57 395,96 0,00015 0,37015 0,25590 0,41163 2006Q4 0,01742 0,98195 223,95 193,41 154,93 262,43 0,00031 0,36208 0,29009 0,40573 2006Q3 0,11220 25,36000 285,39 273,75 191,43 367,70 0,00001 0,30841 0,26999 0,00000 2006Q2 0,00808 -1,05722 247,88 280,16 165,62 362,42 0,00036 0,37192 0,28463 0,44890 2006Q1 0,00443 -4,52489 258,18 280,51 169,18 369,51 0,00022 0,36561 0,29180 0,45341 2005Q4 0,00995 1,17358 216,17 178,45 154,05 240,57 0,00047 0,32890 0,17909 0,50845 2005Q3 0,04479 0,43626 282,33 275,91 182,41 375,83 0,00019 0,33344 0,18706 0,49584 2005Q2 0,03188 -2,69620 260,72 308,07 170,20 398,60 0,00010 0,38169 0,22208 0,49943 2005Q1 0,00383 -2,04831 258,85 293,52 166,98 385,38 0,00017 0,38001 0,22668 0,52192
Table 10: Samsung Electronics Data per year from 2005 till 2014
Table 11: Siemens Data per year from 2005 till 2014
Table 12: Hitachi Data per year from 2005 till 2014
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,05263 0,40667 49,98 62,29 49,27 36,96 0,00338 0,43507 0,06924 0,27052 2013 0,07773 0,48168 44,49 49,54 50,72 45,68 0,00419 0,53627 0,03609 0,29924 2012 0,07078 0,42915 38,62 76,87 51,15 12,89 0,00506 0,53687 0,03583 0,32910 2011 0,04877 0,32965 46,01 78,61 51,15 18,55 0,00459 0,44485 0,02490 0,34972 2010 0,06551 0,36933 50,30 60,87 47,51 36,94 0,00663 0,53227 0,03127 0,33465 2009 0,06235 0,36374 53,00 56,58 37,97 34,39 0,00571 0,52320 0,03203 0,34886 2008 0,02965 0,33892 70,94 46,26 38,60 63,27 0,00515 0,39532 0,07147 0,40244 2007 0,03894 0,42246 79,62 57,90 41,04 62,76 0,01683 0,38687 0,10543 0,40057 2006 0,05086 0,52974 78,85 57,83 41,32 62,35 0,00746 0,40123 0,10223 0,41081 2005 0,05514 0,52599 81,11 55,76 38,74 64,09 0,00819 0,42167 0,11345 0,44122
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,05326 0,10812 103,63 54,17 107,72 157,17 0,00495 0,22088 0,21979 0,69952 2013 0,04195 0,14968 101,70 52,03 106,54 156,20 0,00608 0,22509 0,18611 0,71913 2012 0,04319 0,02346 99,04 52,29 102,03 148,77 0,00641 0,23040 0,16707 0,71092 2011 0,06105 0,06581 106,16 54,89 108,28 159,55 0,00678 0,23662 0,14611 0,69153 2010 0,04114 0,26184 100,58 52,94 100,44 148,07 0,00655 0,25057 0,13763 0,71704 2009 0,02637 0,12414 94,28 49,54 92,19 136,92 0,00794 0,24841 0,13802 0,71254 2008 0,06328 0,22663 102,37 57,46 94,09 139,00 0,07289 0,25678 0,11701 0,71015 2007 0,04251 0,14069 103,47 59,32 91,51 135,66 0,01416 0,26899 0,09487 0,67640 2006 0,03851 0,11226 112,59 62,75 95,06 144,90 0,00778 0,23418 0,09149 0,71217 2005 0,02713 0,02165 116,81 69,39 87,41 134,83 0,00863 0,29429 0,09692 0,68544
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,03496 0,05346 144,18 69,58 72,50 147,10 0,00491 0,27124 0,01687 0,65031 2013 0,02473 0,16438 131,07 66,83 77,80 142,03 0,00513 0,27679 0,01339 0,67589 2012 0,04438 0,09421 117,51 66,48 70,87 121,90 0,00622 0,30436 0,00914 0,70547 2011 0,03343 0,27258 109,85 66,05 70,49 114,30 0,00800 0,31202 0,01998 0,73422 2010 -0,00920 0,28268 119,50 66,89 65,13 117,73 0,00688 0,29905 0,02168 0,74666 2009 -0,07977 -0,01298 99,62 55,04 68,00 112,59 0,01349 0,30213 0,01811 0,76825 2008 -0,00549 0,17621 105,20 69,35 59,92 95,77 0,01034 0,29786 0,01293 0,79388 2007 -0,00317 0,16922 112,64 75,37 65,45 102,71 0,00772 0,26132 0,01395 0,77051 2006 0,00378 0,21625 120,09 73,37 62,37 109,09 0,00156 0,26613 0,04508 0,74975 2005 0,00533 0,16208 115,24 68,65 62,86 109,45 0,00167 0,26943 0,05410 0,76297
Table 13: Sony Data per year from 2005 till 2014
Table 14: Panasonic Data per year from 2005 till 2014
Table 15: Toshiba Data per year from 2005 till 2014
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 -0,04528 15,36600 55,28 55,48 34,64 34,44 -0,00016 0,04541 0,51644 0,81850 2013 0,00739 1,21676 55,19 92,85 39,50 1,83 -0,00094 0,05760 0,51506 0,81127 2012 -0,03041 2,86263 59,12 103,85 40,08 -4,64 -0,00019 0,04233 0,47530 0,81271 2011 -0,01709 0,76389 54,75 94,25 36,73 -2,76 -0,00175 0,05897 0,45640 0,77255 2010 0,00104 13,73005 50,84 92,90 32,94 -9,12 0,00234 0,05524 0,41189 0,74464 2009 -0,00805 -2,03738 44,35 73,51 37,41 8,25 0,00292 0,03816 0,39942 0,73225 2008 0,03044 1,35167 50,84 78,07 43,88 16,65 0,00104 0,08826 0,34539 0,72396 2007 0,01132 6,33882 66,15 95,35 41,76 12,55 0,00020 0,07171 0,33191 0,71231 2006 0,01230 0,74879 53,87 83,59 40,32 10,60 0,00109 0,08436 0,33182 0,69797 2005 0,01763 5,12116 57,66 80,43 32,71 9,94 0,00042 0,08033 0,28905 0,69783
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,02293 0,21223 65,21 60,65 48,59 53,15 0,02659 0,46590 0,05214 0,69568 2013 -0,12921 -0,04972 63,26 53,32 52,99 62,93 -0,04910 0,41175 0,05131 0,75837 2012 -0,11317 -0,05481 62,84 52,97 51,67 61,55 0,20460 0,35143 0,06846 0,70042 2011 0,01058 0,13257 60,50 57,20 51,21 54,52 0,00982 0,34140 0,07282 0,62337 2010 -0,03518 0,15132 80,98 73,22 62,44 70,20 0,00575 0,28418 0,07619 0,55973 2009 -0,06155 -0,02653 74,42 71,32 61,08 64,19 0,00444 0,29576 0,04870 0,89114 2008 0,01571 0,19294 87,74 83,50 61,49 65,73 0,00457 0,28246 0,04609 0,81706 2007 -0,02199 -0,12801 83,17 83,22 63,23 63,19 0,00577 0,27600 0,06759 0,84170 2006 -0,08649 -0,13156 76,80 75,75 55,18 56,23 0,00328 0,26829 0,07471 0,81303 2005 -0,06542 -0,27151 121,90 92,19 65,93 95,64 0,00086 0,12838 0,09651 0,88916
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,00824 0,07137 114,50 127,89 70,23 56,84 0,00732 0,29393 0,10649 0,73527 2013 0,01308 -0,00157 117,54 137,49 84,87 64,92 0,00915 0,26749 0,11577 0,76786 2012 0,01327 0,13719 104,55 132,95 69,66 41,26 0,01174 0,29901 0,12238 0,78425 2011 0,02545 0,18883 85,05 117,35 64,42 32,13 0,01365 0,32728 0,12276 0,78071 2010 -0,00362 0,24490 89,32 116,26 59,00 32,06 0,01557 0,33971 0,11426 0,79314 2009 -0,06033 -0,10542 75,00 93,19 51,58 33,39 -0,01156 0,33267 0,09808 0,86076 2008 0,02147 0,02184 84,50 77,58 53,96 60,87 -0,10532 0,43064 0,09986 0,82778 2007 0,02579 0,17585 96,35 93,81 55,07 57,61 0,03063 0,38850 0,08599 0,81316 2006 0,01682 0,21611 15,39 113,44 52,08 -45,97 0,01941 0,40996 0,10318 0,78800 2005 0,01019 0,13688 97,48 106,64 55,22 46,06 0,02033 0,38958 0,08893 0,82161
Table 16: LG Electronics Data per year from 2005 till 2014
Table 17: Mitsubishi Data per year from 2005 till 2014
Table 18: Honeywell International Data per year from 2005 till 2014
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,01381 0,07104 67,02 78,66 46,02 34,37 0,02178 0,42133 0,00620 0,64954 2013 0,00750 0,10721 64,58 71,34 40,51 33,76 0,02753 0,42293 0,00670 0,64284 2012 -0,01477 0,11945 91,70 124,26 16,83 -15,73 -0,01839 0,24879 0,00631 0,57944 2011 -0,00983 0,04048 84,65 103,73 14,02 -5,05 -0,01926 0,22746 0,01252 0,56631 2010 0,03980 -0,07695 67,37 79,86 49,02 36,54 0,02941 0,40340 0,00922 0,60210 2009 0,06310 0,36488 77,96 84,75 43,26 36,46 0,05229 0,47725 0,01522 0,61310 2008 0,02960 0,29519 56,29 62,92 49,34 42,70 0,02277 0,45435 0,05826 0,65089 2007 0,06093 0,33288 58,24 58,94 50,39 49,69 0,01472 0,46495 0,02645 0,62908 2006 0,00704 0,32098 48,62 66,16 56,13 38,59 -0,01350 0,37142 0,00735 0,67383 2005 0,01921 0,32799 55,43 68,97 61,33 47,79 -0,04984 0,39322 0,00449 0,66139
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,04370 0,28426 123,76 95,04 75,43 104,16 0,00392 0,35327 0,13772 0,57810 2013 0,02044 0,06454 136,93 91,48 82,79 128,24 0,00355 0,32101 0,12443 0,61879 2012 0,03518 -0,06468 132,14 97,22 80,00 114,91 0,00362 0,33645 0,12457 0,64884 2011 0,04067 0,21859 110,36 97,10 73,41 86,67 0,00465 0,33942 0,13772 0,66723 2010 0,01004 0,34132 115,44 92,33 69,09 92,21 0,00352 0,30618 0,14780 0,68238 2009 0,00466 0,11966 104,54 82,42 71,57 93,70 0,00496 0,33918 0,14206 0,72948 2008 0,04554 0,13195 112,65 93,51 64,70 83,83 0,00540 0,35350 0,15736 0,70404 2007 0,03638 0,16178 115,38 95,34 67,07 87,10 0,00539 0,33774 0,17024 0,69318 2006 0,02955 0,23151 114,85 96,76 66,63 84,71 0,00558 0,31536 0,17891 0,71567 2005 0,02229 0,13747 113,97 90,82 63,80 86,95 0,00504 0,30436 0,16040 0,77213
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,09328 0,15041 33,20 67,63 55,52 21,10 0,00419 0,27003 0,05742 0,61152 2013 0,08991 0,11271 32,13 66,58 55,24 20,80 0,00419 0,25707 0,04488 0,61556 2012 0,07166 0,10785 31,23 61,10 54,64 24,77 0,00564 0,24610 0,02996 0,68999 2011 0,04786 0,11903 28,80 60,56 54,50 22,74 0,00566 0,22433 0,02125 0,72855 2010 0,05266 0,27631 30,19 62,00 56,43 24,63 0,00556 0,22773 0,02294 0,71808 2009 0,06023 0,23048 98,77 57,19 54,25 95,83 0,00759 0,24108 0,01608 0,75131 2008 0,08058 0,17694 79,91 49,19 50,17 80,89 0,02408 0,26669 0,01888 0,79518 2007 0,07550 0,21848 88,64 54,99 53,58 87,24 0,01302 0,26996 0,01479 0,72720 2006 0,06588 0,13949 97,34 59,66 60,84 98,52 0,01243 0,34378 0,01235 0,68585 2005 0,05313 0,10672 85,08 48,94 57,67 93,81 0,01096 0,20998 0,01146 0,65151
Table 19: Schneider Data per year from 2005 till 2014
ROA FCF DRO DPO DIO CCC NTC GOP FDR DR
2014 0,05250 0,05783 181,42 96,49 71,13 156,06 0,01167 0,25035 0,01793 0,51040 2013 0,05401 0,06311 162,55 94,59 75,52 143,49 0,00501 0,25629 0,00854 0,52896 2012 0,05354 0,09473 161,31 102,72 75,75 134,34 0,00543 0,27301 0,01239 0,53490 2011 0,05613 0,08752 184,10 107,06 87,58 164,61 0,00576 0,25547 0,01845 0,55217 2010 0,06337 0,17511 174,24 105,78 96,75 165,21 0,00488 0,27320 0,01907 0,51728 2009 0,03434 0,11879 150,32 84,00 82,90 149,21 0,00481 0,26596 0,01654 0,53730 2008 0,07168 0,11020 149,70 77,57 86,70 158,83 0,00872 0,32168 0,01576 0,55452 2007 0,07497 0,07395 157,76 76,25 88,69 170,20 0,00993 0,32633 0,02282 0,55673 2006 0,07358 0,07290 175,81 88,36 93,21 180,66 0,00418 0,32013 0,02658 0,53396 2005 0,06646 0,07865 177,65 90,20 86,29 173,75 0,00480 0,30834 0,03593 0,49816
Appendix B. Regression Analysis
Table 20: Model 1 Philips regression analysis
Table 21: Model 2 European & American firms’ regression analysis
Number of obs = 41 F( 6, 34) = 7.13
Source SS df MS Prob > F = 0.0001
Model .01248387 6 .002080645 R-squared = 0.5571
Residual .009924353 34 .000291893 Adj R-squared = 0.4790
Total .022408223 40 .000560206 Root MSE = .01708
ROA Coef. Std. Err. t P>|t|
FCF .0011823 .0004292 2.75 0.009 .0003101 CCC .0000684 .0000318 2.15 0.039 3.67e-06 NTC 19.45291 8.409.043 2.31 0.027 2.363673 GOP -.1562023 .1014481 -1.54 0.133 -.3623696 FDR .0729855 .0464725 1.57 0.126 -.0214581 DR -.0248835 .0165916 -1.50 0.143 -.0586018 _cons .0458374 .0420687 1.09 0.284 -.0396564 [95% Conf. Interval] .0020546 .0088348 .1313313 .167429 .0499651 3.654.214 .0001331 Number of obs = 40 F( 6, 33) = 3.39 Source SS df MS Prob > F = 0.0103 Model .016293227 6 .002715538 R-squared = 0.3812
Residual .026445572 33 .000801381 Adj R-squared = 0.2687
Total .042738798 39 .001095867 Root MSE = .02831
ROA Coef. Std. Err. t P>|t|
FCF .2202525 .0842162 2.62 0.013 .0489134 CCC -.0001535 .0000932 -1.65 0.109 -.0003431 NTC .0878279 .4519183 0.19 0.847 -.8316067 GOP -.047125 .1229228 -0.38 0.704 -.2972133 FDR .1729181 .07309 2.37 0.024 .0242153 DR -.204752 .0568399 -3.60 0.001 -.3203936 _cons .1764344 .0549788 3.21 0.003 .0645793 [95% Conf. Interval] .3915917 -.0891105 .2882896 .3216209 .2029634 1.007263 .0000361
Table 22: Model 3 Asian firms’ regression analysis
Number of obs = 70 F( 6, 63) = 7.71
Source SS df MS Prob > F = 0.0000
Model .04954595 6 .008257658 R-squared = 0.4233
Residual .06750105 63 .001071445 Adj R-squared = 0.3684
Total .117047 69 .001696333 Root MSE = .03273
ROA Coef. Std. Err. t P>|t|
FCF -.0007318 .0018644 -0.39 0.696 -.0044575 CCC .0001459 .0001116 1.31 0.196 -.0000771 NTC -.217044 .1281474 -1.69 0.095 -.4731261 GOP .1363243 .0569063 2.40 0.020 .0226061 FDR .1292481 .0559325 2.31 0.024 .0174758 DR -.1320444 .0324047 -4.07 0.000 -.1968001 _cons .0346744 .0386955 0.90 0.374 -.0426525 [95% Conf. Interval] .002994 -.0672887 .1120012 .2410203 .2500425 .0390381 .0003688
