Effects of working capital management on
profitability of the oil and gas companies:
Empirical evidence from emerging markets
MSc in International Financial Management
Master Thesis
Name: Emin Ahmadzada Supervisor: Dr. W. (Wim) Westerman
Student number: s2197081 Co-assessor: Dr. A. (Adri) de Ridder
Email: e.ahmadzada@student.rug.nl
- Abstract -
In this paper we investigate the relationship of working capital management and corporate profitability of the oil and gas companies from the emerging markets. We use Cash Conversion Cycle (CCC) as a measure of working capital management efficiency. Our sample includes 121 publicly listed companies. Results reveal a significant negative relationship between CCC and corporate profitability. The study further finds that a longer receivables collection period will also lead to negative firm profitability. Together with this, we also use cultural uncertainty avoidance as a moderating variable to the main relationship. However, our findings do not provide evidence to this moderating role.
Key Words: working capital, cash conversion cycle, payables, receivables, inventory,
1. Introduction
Working capital management is one of the most fundamental measures of corporate financial strength, because it is considered to be an important component in the process of financing and investment decision-making in the short-term periods. However, it has not received enough attention from researchers, who prefer to direct the major focus to studies embedding long-term financial decisions such as decision-making from the corporate finance perspectives. Working capital management is about managing company’s current assets and liabilities, which is important as it affects firms’ risks (Eljelly, 2004). It is worth to pay required attention to working capital management, because profits of the companies are affected by any change to working capital components (Sagner, 2011).
Significant differences existing among the industries and countries make it less advisable to compare the working capital efficiency across the companies (Platt, 2010). With the focus on researching the current relationship, to come across with studies that investigate a single industry (see, e.g., Nijam, 2016; Mawutor, 2014; Wasiuzzaman, 2015) is not hard. However very few of the researches have been focussing solely on the oil & gas industry. In the study of working capital management, it is interesting to steer the attention to the oil and gas industry. Faced with the pressure from the shareholders and tight regulations imposed by the governments, given the oil price volatility, the companies operating in this sector have started to focus thoroughly on cash and working capital management to improve profitability (Ernst & Young, 2014). There are also limited studies investigating the relationship between working capital management efficiency and corporate profitability in emerging markets. Moreover, the consideration of more instable business and economic environment makes it more interesting to observe trends in the emerging markets. Preve & Sarria-Allende (2010) state that companies from emerging markets face more liquidity risk because of the smaller sized markets in comparison to the developed world. Therefore, liquidity management might also be considered as an integral part of businesses operating in emerging markets. In addition, the high dependence on the oil and gas revenues for the emerging markets is the main reason why it is of great interest to conduct the aforementioned study.
Overall, there are various studies investigating the current relationship between working capital management and firm profitability, however, there is limited research solely concentrating on the oil and gas extracting companies representing emerging markets. Moreover, one of the contributions of this research is the integration of cultural uncertainty avoidance, which is used as a country level variable that seems to moderate this relationship. Hence, with the perspectives aimed at investigating the relationship under the chosen strategy, the aim of this research is to find an answer to the following questions: “How does efficiency of working capital
management affect the profitability of emerging market companies operating in oil & gas industry and how does country-level cultural uncertainty avoidance moderate this relationship?
database for the period of 2010-2016. In line with many previous studies (see, e.g., Garcia-Teruel & Martinez-Solano, 2007; Shin and Soenen, 1998; Lazaridis and Tryfonidis, 2006; Raheman & Nasr, 2007; Enqvist et al., 2014; Wasiuzzaman, 2015; Chang 2017) and the finance literature (Sagner, 2011), our finding confirms the negative relationship between the cash conversion cycle and firm profitability. Moreover, our results also reveal a negative relationship between the receivables
collection period and firm profitability. This is in line with the findings of many other
researchers such as Deloof (2003), Lazaridis and Tryfonidis (2006), Raheman & Nasr (2007), Garcia-Teruel & Martinez-Solano (2007), Gill et al. (2010) and Wasiuzzaman (2015). Furthermore, as opposed to our expectations, we find that uncertainty avoidance makes the negative relationship between the ICP and ROA weaker. However, robustness test illustrated a change in the direction of the coefficient of the ICP. The discussion on this finding constructs the reasoning that for companies to make profits from speculation, economic effect should be more prevelant than cultural effect.
This paper is commenced with an industry overview and literature review, which leads to the formulation of hypotheses. Followed with the presentation of data description and methodology and finishing with the results, discussion, conclusion, limitations and managerial implications.
2. Literature Review
2.1. Oil & Gas Industry
downstream sector includes oil refineries, petroleum products distributors, petrochemical plants, retail outlets and natural gas distribution companies.
Recently, much of the oil and gas industry faced a tough period with lower oil prices and weak demand. The largest decline of the oil price in almost 25 years has been observed in the 2014-2015 period led by non-OPEC production (BP, 2017). On the other hand, the weak increase in global consumption of natural gas relative to previous 10-year average had led to the weakest growth in gas output for the last 34 years (BP, 2017). Today, there are some other trends that are reshaping the industry. For, example, the growing consumer interest in electric vehicles is negatively affecting the demand for oil (World Economic Forum, 2017b).
The combination of strong demand and weak supply, however, could somehow move the oil market back into balance. Brent crude oil price increased by roughly 90 percent in 2016 to over USD$50 per barrel (PWC, 2017), which illustrates a strong recovery since the oil price collapse of 2014-2015. Today, the oil and gas industry is performing with renewed confidence as the oil price is hitting a three-year maximum of USD$70 per barrel. According to Oussov (KPMG, 2018), this is the merit of the demand-supply rebalancing and the future demand expectations.
2.2. Working Capital Management
The traditional definition of working capital management reveals how much of cash is available to make payments on cash requirements in a short-term. The working capital components are accounts receivables, accounts payables and inventory accounts. Working capital is considered as a short-term concept, because based on accounting standards, current assets and current liabilities are short-term items as they can be converted into cash or become due respectively within one-year (Preve & Sarria-Allende, 2010). With its short-term nature, if managed efficiently, working capital seems to add value to corporate performance. Indeed, working capital management is important, because the profits of the companies are affected by any change to working capital components (Sagner, 2011). Thus, an efficient management of working capital components, which plays a crucial role in the process of financing and investment decision-making in the short-term periods, can lead to value increasing activities. Especially, the importance of working capital is vividly expressed in emerging markets because the companies operating in these markets face more liquidity risk, have short supply of capital, and the market size is small, which makes the capital market be of low-quality (Preve & Sarria-Allende, 2010).
2.3. Cash Conversion Cycle and Corporate Profitability
Investigation of the effects of working capital management upon corporate performance has been a focus of various researchers for a long period of time and under numerous environments. The finance literature reveals that managing working capital efficiently can positively influence the firm’s profitability (Preve & Sarria-Allende, 2010). It is necessary for the companies to efficiently manage their working capital in order to be able to survive and grow sustainably (Banos-Caballero et al. 2010). Various studies investigate the relationship between the working capital management and the performance of the firms (see, e.g., Shin & Soenen, 1998; Deloof, 2003; Lazaridis and Tryfonidis, 2006; Raheman & Nasr, 2007; Garcia-Teruel & Martinez-Solano, 2007; Gill et al. 2010; Sharma and Kumar, 2011; Sagner, 2011; Banos-Caballero et al., 2012; Yazdanfar & Ohman, 2014; Enqvist et al., 2014; Aktas et al. 2015; Wasiuzzaman, 2015; Chang, 2017). Some of these studies reveal a concave relationship between working capital efficiency and profitability. For example, Baños-Caballero et al. (2012) use a sample comprising of 1008 Spanish SMEs over the period of 2002-2007 and find a concave relationship between the working capital level and firm profitability. They state that there is an optimal working capital level for small and medium sized enterprises which balances costs and benefits and maximizes their profitability. Consistent with this finding, Aktas et al. (2015) also demonstrate that there is an optimal level of working capital policy and that firms improve their operating performance by converging to the required level. The study made a use of 15,541 listed firms that were obtained from the WRDS merged CRSP/COMPUSTAT files capturing the period from 1982 to 2011. Concluding from this, it is shown that a movement away from the optimal level would decrease the profitability.
shareholders can be created by reducing the inventory accounts, the receivables collection period, and payables deferral period. The study used a sample comprised of large Belgian non-financial companies for four years in a period between 1992 and 1996. The cash conversion cycle itself in the study did not reveal a significant outcome, even though the coefficient was negative as well. Unlike Deloof (2003), the research by Lazaridis and Tryfonidis (2006), Raheman & Nasr (2007), Enqvist et al. (2014) and Wasiuzzaman (2015) found a significant negative relationship between corporate profitability and the cash conversion cycle. Moreover, Garcia-Teruel & Martinez-Solano (2007) in their research undertaken on Spain’s SMEs, with the use of 38,464 firm-year observations, also found a negative relationship between CCC and corporate profitability. This finding is consistent with the outcome of Yazdanfar & Ohman (2014), where they used a sample of Swedish small and medium size enterprises from 2008-2011. Notwithstanding, opposite to all these studies, Sharma and Kumar (2011) and Gill et al. (2010) concluded that companies can create a value and increase profitability by increasing the cash conversion cycle.
Based on the literature above, it is expected that there is a negative relationship between the firm profitability and cash conversion cycle. A shorter cash conversion cycles reveal that a firm’s working capital management is efficient and that it has a superior liquidity. With efficient working capital management, firms are able to excellently perform in the daily business operations and confidently approach the potential growth opportunities. Also taking into the account a claim by Platt (2010), companies usually become more profitable when they shorten their CCC. Following this allows us to construct our first hypothesis as following:
H1: There is a negative relationship between cash conversion cycle and firm
profitability.
2.4. Receivables Collection Period (RCP) and firm profitability
important to be able to collect the receivables to finance its operations and also pay debts to stay profitable. However, companies usually make sales on credit terms, rather than with immediate cash payments, which plays a role in the form of short-term loans to clients. Basically, the offer of trade credit by companies can be seen as a substitute to banks’ and other financial institutions’ financing operation. Firms provide such commercial credits to increase sales or the market share mostly when they face profitability problems (Molina and Preve, 2009). Nonetheless, the provision of commercial credit is not always beneficial to the companies. Molina and Preve (2009) find that during the times of financial distress firms might become vulnerable and a significant drop in the corporate performance might lead to situations where they are unable to invest in commercial credits to customers. Based on this, seemingly during the time of instability the outstanding risky credits might lead to bankruptcy. Indeed, many previous studies, such as by Deloof (2003), Lazaridis and Tryfonidis (2006), Raheman & Nasr (2007), Garcia-Teruel & Martinez-Solano (2007), Gill et al. (2010) and Wasiuzzaman (2015) are revealing that shorter days of receivables collection period will improve firm profitability, whereas Sharma and Kumar (2011) concluded oppositely.
Overall, it is important to convert receivables into cash fast to eschew the possible losses on potential investment in revenue generating activities. Moreover, a lower CCC can be accomplished by reducing the receivables collection period. Consistent with this and also with the previous studies, we construct the conjecture that the firms will gain more profits when the receivables are collected faster. We support our conjecture with the fact that in case of having a shorter time for collecting the account receivables, the financial risks such as the bad debt risk will be reduced which in turn will lead to higher profitability. Thus, we hypothesize that:
H2: There is a negative relationship between receivables collection period and firm
profitability.
2.5. Inventory Conversion Period (ICP) and firm profitability
to market demand which will efficiently increase profitability. However, there are also some costs involved with the inventory, for instance, carrying costs. Keeping the inventory might damage companies’ financial performance. Especially, it may bring a colossal loss to the oil and gas extraction companies, which find it very costly and technically impossible to deal with storage. The reason behind it is the oil and gas commodities for which the unit value is lower than the unit cost of storage. Together with this, keeping the inventory might also carry speculative purposes when companies expect the future prices to be different than the current prices (Jaffe and Soligo, 2002). Thus, the aim of efficient inventory management in oil and gas industry is to make sure that the company is efficient in its operations as well as in managing the production and carrying costs.
Deloof (2003), Raheman & Nasr (2007), Garcia-Teruel & Martinez-Solano (2007), Enqvist et al. (2014) and Wasiuzzaman (2015) all found a negative relationship between the inventories collection period and firm profitability. Generally, reducing the time during which the oil and gas are held in inventory can efficiently reduce the CCC of companies operating in the industry. Especially, considering the costs of the oil and gas storage, it can be said that a lower period of inventory conversion indicates a better performance. Thus, the oil and gas companies can promptly sell the inventory and by doing so increase the sales and reduce carrying costs. Considering this, consistent with the findings of other authors, we formulate the third hypothesis as follows:
H3: There is a negative relationship between inventory collection period and firm
profitability.
2.6. Payables Deferral Period (PDP) and firm profitability
with the research of Deloof (2003), Raheman & Nasr (2007), Enqvist et al. (2014) and Wasiuzzaman (2015). Moreover, Preve & Sarria-Allende (2010) state that the buyers need to be careful with their suppliers because they are dependent on the suppliers’ financing, especially during the times of financial distress when it is difficult and nearly impossible to access the financing from financial institutions. The reason why many previous researchers have found a negative relationship might also be that earlier payments are motivated by trade discounts (Deloof 2003; Enqvist et al. 2014). Envist et al. (2014) also states that more profitable firms opt for trade discounts rather than using financing by suppliers. On the other hand, as part of the CCC, the payables deferral period should be well optimized to keep the CCC low. Thus, paying suppliers late increases the payables deferral period, that in turn makes working capital more efficient (Enqvist, 2014). Therefore, despite of many previous findings, we construct the conjecture that prolonging the payments to suppliers will positively influence the firm profitability. Our conjecture is in line with the study by Lazaridis and Tryfonidis (2006) who found a positive relationship between PDP and CCC. As a result, we hypothesize as following:
H4: There is a positive relationship between payables deferral period and firm
profitability.
Table 1: Summary of the similar researches
Authors Country, years & observation Profitability measure WCM measure Results of WCM on profitability Results of RCP on profitability Results of ICP on profitability Results of PDP on profitability
Shin & Soenen, 1998 USA, 1975-1994, 58,985 firm-years IA (operating income + depreciation), IS (operating income + depreciation)/ net sales NTC (Net Trade Cycle) Negative relationship Not individually measured Not individually measured Not individually measured
Lazaridis & Tryfonidis, (2006) Greece, 2001-2004 524 firm-years GOP (Gross Operating Profit) ratio CCC Negative relationship Negative relationship No significant relationship Positive relationship Raheman & Nasr, 2007 Pakistan, 1999-2004 470 firm-years NOP (Net Operating Profitability) CCC Negative relationship Negative relationship Negative relationship Negative relationship Garcia-Teruel & Martinez-Solano, 2007 Spain’s SMEs, 38,464 observation s ROA (Return on Assets) CCC Negative relationship Negative relationship Negative relationship No significance after controlling for possible endogeneity Sharma and Kumar (2011) India, 2000-2008 2,367 firm-years ROA (Return on Assets) CCC Positive relationship Positive relationship Negative relationship Negative relationship Baños-Caballero et al. (2012) Spain, 2002-2007 5862 firm-years ROA (Return on Assets) CCC Concave Not individually measured Not individually measured Not individually measured Yazdanfar & Ohman, (2014) Sweden, 2008-2011 23000 SMEs ROA (Return on Assets) CCC Negative relationship Not individually measured Not individually measured Not individually measured Enqvist et al. (2014) Finland, 1990-2008, 1,136 firm-years ROA (Return on Assets), GOI (Gross Operating Income) CCC Negative relationship No significant relationship Negative relationship Negative relationship Wasiuzzaman 2015 Malaysia, 960 firm-years ROA (Return on Assets) NWC* Negative relationship Negative relationship Negative relationship Negative relationship Chang, 2017 46 countries, 1994-2011 266,547 firm-years ROA (Return on Assets) CCC Negative Relationship NA NA NA
2.7. Cultural Uncertainty Avoidance, Cash Conversion Cycle and Corporate Profitability
Whilst many studies concentrate on the relationship between corporate profitability and working capital management efficiency, not much research has been undertaken to account for external factors which influence the current relationship. One of the researches considering the moderating effect is the study of Enqvist et al. (2014), which accounts for state of the economy, where they find that the impact of cash conversion cycle on corporate profitability increases during the recession. In this study, however, the national culture is used as a country level moderating factor to the relationship between cash conversion cycle and corporate profitability.
and the current study, it is specifically interesting to investigate whether or not culture influences the relationship between cash conversion cycle and the profitability.
Hofstede (2001) identified five dimensions of national culture. One of these dimensions is labeled as uncertainty avoidance, which deals with society’s tolerance to unknown and uncertain factors. The nations with a culture of high uncertainty avoidance often prefer clearness, predictability and transparency in the operations and structures of organizations. Having clear-cut rules and structures to follow, companies from the countries scoring high on uncertainty avoidance will opt for well-defined settings and rules in the operations. This also means that these firms will usually avoid any uncertainty about the processes in their operations. Working towards reducing the unknown will usually create an environment with strong bureaucracy, laws and rules that may illustrate a sound governance regime. Gonenc and Seifert (2016) found that sound governance regimes reduce uncertainty, which consequently leads to companies holding less cash. Where uncertainty is remaining, companies from countries with high uncertainty avoidance will hold more cash to deal with uncertainty about cash flows and thereby reduce risks. This train of thoughts are in line with the precautionary motive of holding cash, which explains that the company will hold some cash to be able to respond to uncertain and unexpected (Preve & Sarria-Allende, 2010). Drawing parallels with the cash conversion cycle shows that a quick conversion of products into cash through sales will tie the capital less in the business process and make sure that with the capital on hand the company is able to back-up any risks associated with uncertainty. Indeed, cash conversion cycle is inversely related to cash holding (Anjum and Malik, 2013). With higher cash holding, firm are able to invest in growth promising opportunities and increase corporate performance. Firms will reduce the CCC by focusing on better planning and control systems to be able to efficiently manage the accounts receivables to avoid potential
default on credits provided to the customers. The oil and gas companies from
companies going bankrupt. Thereby, with prolonged payments to suppliers, companies will observe an increase in their firm profitability. With this motivation, we construct the conjecture that in cultures with high uncertainty avoidance, where there are clearly set rules, sound structures and regimes, companies efficiently manage their working capital and with lower cash conversion cycles make profits. The construction of hypotheses is presented below.
The oil and gas companies from countries with higher uncertainty avoidance will carry more routines and tasks to control the account receivable and by reducing the risk of defaults they will achieve higher profitability.
H5: Higher uncertainty avoidance will strengthen the negative relationship between
accounts receivable collection period and firm profitability.
Figure 1: Moderation effect of Cultural Uncertainty Avoidance (UAI) to the relationship between the Receivable Collection Period (RCP) and firm profitability (PRFT).
Companies from countries with higher uncertainty avoidance will carry more routines and tasks to eschew possible overage inventory level and they will achieve higher profitability by reducing storage costs.
H6: Higher uncertainty avoidance will strengthen the negative relationship between
Figure 2: Moderation effect of Cultural Uncertainty Avoidance (UAI) to the relationship between the Inventory Collection Period (ICP) and firm profitability (PRFT).
Companies representing the aforementioned industry, from countries with higher uncertainty avoidance, will pay suppliers as late as possible to guarantee order quality and time. Thus, by reducing uncertainty about the future external financing of their working capital, companies will reduce risks and achieve higher profitability.
H7: Higher uncertainty avoidance will strengthen the positive relationship between
payables deferral period and firm profitability.
Figure 3: Moderation effect of Cultural Uncertainty Avoidance (UAI) to the relationship between the Payables Deferral Period (PDP) and firm profitability (PRFT).
Finally, structuring the accounts receivables, inventories and payables in a clear and systematic manner will help companies to achieve reduced cash conversion cycle, which is hypothesized to increase the profitability. Companies from countries with higher uncertainty avoidance will carry more routines and tasks to manage working capital efficiently, which will strengthen the negative relationship between cash conversion cycle and profitability.
H8: Higher uncertainty avoidance will strengthen the negative relationship between
Figure 4: Moderation effect of Cultural Uncertainty Avoidance (UAI) to the relationship between the Cash Conversion Cycle (CCC) and firm profitability (PRFT).
3. Data and Methodology 3.1. Data Collection
During the times of financial and economic instabilities there might be potential biases of the data. To prevent this, the research’s focus is embracing the period after the global financial crisis (GFC) of 2007-2009, focusing on the period from 2010 to 2016. One-year period is allowed to avoid any possible inaccuracies in the financial data of companies. The research makes use of two databases, which are Orbis and DataStream. Orbis is used to create the list of companies. In the collection process of the list of companies, the specific selection criteria were used. First, the research focuses on the countries using a market classification developed by MSCI (Morgan Stanley Capital International), which recognizes 24 countries from Asia, America, Europe, Middle East and Africa as emerging market countries. The final sample, however, includes only companies from 16 emerging market countries (Appendix I). Second, a US SIC code 13: oil and gas extraction’s component with US SIC code 131: crude petroleum and natural gas, was used to narrow down the number of companies from emerging markets only to the ones representing the oil and gas extraction sector. Thirdly, ISIN codes were used as company identification indices and to ensure the richness of the data, the selection criterion in the Orbis database was configured to limit the list to only publicly traded companies listed in different stock exchanges. At the end, after controlling for selection criteria of companies, we
obtained 153 firms operating in the oil and gas industry of the emerging market
necessary financial data on chosen variables were collected for a time period of 7 years. There were companies with unavailable data for some of the accounts and those companies were taken out of our sample to prevent problems associated with the calculation of the ratios and CCC. This also made the panel dataset balanced, meaning that it has data for all the companies and years. Moreover, a 99.7% rule was used to make sure that all the values from our sample lie within 3 standard deviations of the mean, which makes our statistical dataset normally distributed. In total, deletion of companies with insufficient data and application of the 99.7 empirical rule to our data set has reduced the number of emerging markets oil and gas extracting companies from 153 to 121, which led to 847 firm-year observations.
Besides that, the research also makes a use of the data with the country scores about the uncertainty avoidance dimension of Hofstede’s national culture obtained from the website (www.geert-hofstede.com). This score is used as the measurement tool of the country level moderating variable- cultural uncertainty avoidance.
3.2. Descriptive Statistics
The initial data-set suffered from an “outlier” problem and to cope with this, the “winsorization” function on STATA was used, which cut 5% from each tail. This has led to the data being free of outliers. The descriptive statistics is outlined in table 2 below. The total number of observations is 847. The mean value of return on assets is 0.0495, whereas the cash conversion cycle average is around 122 days. Moreover, the receivables collection period mean is around 135 days, whereas the mean of accounts payable deferral period is about 88 days. In regard to ICP, on average for oil and gas companies from our sample, it takes 67 days to sell inventory. The maximum time taken by companies is 228 days, whereas the minimum time is almost 2 days.
Table 2: Descriptive Statistics
Source: Eviews output
Notes: ROA- return on assets; ROE- return on equity (used as a robustness in the study); CCC- cash conversion cycle; RCP- receivables collection
period; ICP- inventory collection period; PDP- payables deferral period; LVRG- leverage (Debt ratio); LIQD- liquidity (Current Ratio); Size.
Moreover, according to Preve & Sarria-Allende (2010), the oil and gas
industry has the longest average days of trade payables among all other industries, which is on average about 148.94 days. Our sample average is about 88 days, with the maximum of 280 days and minimum of 3 days. Preve & Sarria-Allende (2010) also reveal that the trade receivables over assets is below the average of all industries from the Fama and French industry classification. Our sample, however, reveal that, on average, the oil and gas companies are paying to their suppliers faster than they are receiving payments from their customers. The sample characteristics are similar to that by Lazaridis and Tryfonidis (2006), where their sample shows the mean number of RCP at 148.25 and of PDP at 96.10. However, their sample is not composed of only oil and gas companies.
3.3. Methodology
The primary aim of this study is to investigate the influence of WCM on corporate profitability of the oil and gas firms from the emerging markets. Next to this, the research also accounts for cultural uncertainty avoidance as a country level moderating variable. Table 3 below outlines the statistical method of OLS regressions
Mean Median Minimum Maximum
Standard
Deviation Observations
ROA 0.04945 0.043404 -0.04874 0.15762 0.061493 847
ROE 0.101601 0.092344 -0.06485 0.29106 0.108192 847
CCC 122 days 71 days -46 days 427 days 142 days 847
RCP 135 days 95 days 22 days 378 days 113 days 847
ICP 67 days 42 days 2 days 228 days 70 days 847
PDP 88 days 51 days 3 days 280 days 87 days 847
LVRG (DR) 0.204153 0.175627 0 0.53695 0.186477 847
LIQD (CR) 2.141303 1.461875 0.493278 6.12269 1.75735 847
with panel data that are used to examine the impact of working capital management efficiency on the profitability of firms.
All the models are estimated by using period fixed effect regressions, which will allow us control for time invariant omitted variables. Period fixed effect models the omitted variables for each cross-sectional unit with a use of dummy variable (Baltagi, 2005 and Greene, 2003). Moreover, the analysis will be repeated based on the different measurement of firm profitability (ROE) to see if the main findings are robust. Together with this, we repeat the analysis on the original model with a dependent variable ROA by excluding the two-year period (2014 and 2015) to see if the results reveal a better outcome without the years of instabilities in oil and gas industry.
Table 3: Regression Models
(1) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐂𝐂𝐂𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ ∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔+e𝒊𝒕 (2) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐂𝐂𝐂𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ 𝛽G𝐔𝐈𝐀𝒋 +𝛽K𝐔𝐀𝐈𝒋∗ 𝐂𝐂𝐂𝒊𝒕+ ∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (3) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐈𝐂𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ ∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (4) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐈𝐂𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ 𝛽G𝐔𝐈𝐀𝒋 +𝛽K𝐔𝐀𝐈𝒋∗ 𝐈𝐂𝐏𝒊𝒕+∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (5) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐑𝐂𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ ∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (6) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐑𝐂𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ 𝛽G𝐔𝐈𝐀𝒋 +𝛽K𝐔𝐀𝐈𝒋∗ 𝐑𝐂𝐏𝒊𝒕+∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (7) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐏𝐃𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ ∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕 (8) 𝑹𝑶𝑨𝒊𝒕= 𝑐 + 𝛽+𝐏𝐃𝐏𝒊𝒕+ 𝛽-𝐋𝐕𝐑𝐆𝒊𝒕+ 𝛽2𝐒𝐈𝐙𝐄𝒊𝒕+ 𝛽7𝐋𝐐𝐃𝐓𝒊𝒕+ 𝛽G𝐔𝐈𝐀𝒋 +𝛽K𝐔𝐀𝐈𝒋∗ 𝐏𝐃𝐏𝒊𝒕+∑𝑻𝒊𝒎𝒆 𝑭𝒊𝒙𝒆𝒅 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 + e𝒊𝒕
Notes: Table 4 below provides more detailed information on each variable illustrated as components of regression models here in table 3.
3.4. Variables
In the models, the firm level variables are denoted by subscript i and t forfirm
and time respectively. The main dependent firm level variable is firm profitability
(𝐑𝐎𝐀𝒊𝒕), whereas cash conversion cycle (𝐂𝐂𝐂𝒊𝒕) is used as the main firm-level
independent variable. The models also account for a country level moderating
variable for country j- cultural uncertainty avoidance score (𝐔𝐀𝐈𝒋𝒕) from Hofstede
(2001). With the use of country scores for the uncertainty avoidance dimension, each country from our sample is manually assigned to associated scores revealing an estimate of uncertainty avoidance levels. The primary estimate of uncertainty avoidance based on the original data for the current sample ranged from 30 to 112, low and high uncertainty avoidance scores respectively. The score for Greece falls outside the normal uncertainty avoidance range developed by Hofstede. Considering this, in this study, we follow the modified data from the same source and the new overview of our data reveals a minimum score of 30 (China) and a maximum score of 100 (Greece reduced from 112 to 100). Major focus is also directed towards the interaction variable between cultural uncertainty avoidance and cash conversion cycle (𝐔𝐀𝐈𝒋∗ 𝐂𝐂𝐂𝒊𝒕).
I follow Sharma and Kumar (2000), Lazaridis and Tryfonidis (2006), Enqvist et al. (2014) and Deloof (2003) and employ additional control variables such as:
leverage (𝐋𝐕𝐑𝐆𝒊𝒕), size (𝐒𝐈𝐙𝐄𝒊𝒕) and liquidity (𝐋𝐐𝐃𝐓𝒊𝒕). These control variables are
Table 4: The description and source of variables
Variable name Definition Source
Profitability (PRFT1)
𝐑𝐎𝐀 = 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
DataStream codes:
Net Income (WC07250) and Total Assets (WC02999).
Profitability (PRFT2) 𝐑𝐎𝐄 =
𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑒𝑟_𝑠 𝐸𝑞𝑢𝑖𝑡𝑦
DataStream codes:
Net Income (WC07250) and Total shareholders’ Equity (WC03995).
Cash Conversion Cycle
(CCC) 𝑪𝑪𝑪 = 𝑅𝐶𝑃 + 𝐼𝐶𝑃 − 𝑃𝐷𝑃 𝑹𝑪𝑷 = l𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒 𝑆𝑎𝑙𝑒 o ∗ 365 𝑰𝑪𝑷 = l𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑖𝑒𝑠 𝐶𝑂𝐺𝑆 o ∗ 365 𝑷𝑫𝑷 = l𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑃𝑎𝑦𝑎𝑏𝑙𝑒 𝐶𝑂𝐺𝑆 o ∗ 365 DataStream codes: Accounts Receivable (WC02051), Sales (WC01001), Inventories (WC02101), COGS (WC01051) and Accounts Payable (WC03040).
Uncertainty Avoidance (UAI) Ranging from 0-100 www.geert-hofstede.com
Leverage (LVRG)
𝐃𝐞𝐛𝐭 𝐫𝐚𝐭𝐢𝐨 = 𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
DataStream codes:
Total Debt (WC03255) and Total Assets (WC02999)
Size (SIZE) Natural logarithm of total assets DataStream codes:
Total Assets (WC02999)
Liquidity (LQDT)
Current Ratio = 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
DataStream codes:
Current Assets (WC02201) and Current Liabilities (WC03101)
Notes: This table illustrates detailed information on used variables, description of their proxies and data sources
RCP stands for Receivables Collection Period; ICP stands for Inventory Collection Period; PDP stands for Payables Deferral Period
3.5. Correlation Analysis
RCP; ROA and ICP; ROA and PDP; ROA and LVRG (Leverage). On the other hand, LIQD (Liquidity), SIZE and UAI (Uncertainty Avoidance) are positively related to ROA.
Table 5: Correlation Matrix
ROA CCC RCP ICP PDP LVRG LIQD SIZE UAI
ROA 1 CCC -0.02762 1 RCP -0.17437 0.656522 1 ICP -0.07477 0.467681 0.230119 1 PDP -0.232 -0.11653 0.357804 0.302361 1 LVRG -0.34998 -0.19318 -0.17032 0.05825 0.100564 1 LIQD 0.235624 0.273935 0.192912 0.06404 -0.06317 -0.52579 1 SIZE 0.083146 -0.19332 -0.282 -0.10072 -0.1317 0.181549 -0.18783 1 UAI 0.117106 -0.09329 -0.18001 -0.03907 -0.11861 -0.07451 0.005775 0.286552 1
Notes: The table contains correlation coefficients of all the variables used in the study: ROA (Return of assets), CCC (Cash Conversion Cycle),
RCP (Receivables Collection Period), ICP (Inventory Collection Period), PDP (Payables Deferral Period), LVRG (Debt Ratio used for the leverage), LIQD
4. Results and discussion
4.1. Main Analysis
Table 6: Regression analyses on Dependent Variable ROA Time-Fixed:
YES I II III IV V VI VII VIII
CCC -0.03*** (0.001) -0.0001*** (0.0032) RCP -0.011*** (0.000) -0.010** (0.021) ICP (0.244) -0.003 -0.015** (0.047) PDP -0.012*** (0.001) (0.552) -0.003 UAI -0.0019 (0.861) (0.515) 0.009 (0.958) 0.0006 0.0002** (0.049) CCC*UAI 0.0001*** (0.002) RCP*UAI (0.822) 0.000 ICP*UAI 0.0002* (0.094) PDP*UAI (0.234) 0.000 Leverage -10.701*** (0.000) -11.061*** (0.000) -10.377*** (0.000) -9.944** (0.013) -10.43*** (0.000) -10.95*** (0.000) -10.07*** (0.000) -9.930*** (0.000) Liquidity 0.402*** (0.002) 0.420*** (0.001) 0.360*** (0.007) 0.320** (0.013) 0.380*** (0.004) 0.420*** (0.001) 0.352*** (0.007) 0.306** (0.017) Size 0.360*** (0.000) 0.261*** (0.000) 0.374*** (0.000) 0.321*** (0.002) 0.305*** (0.000) 0.246*** (0.001) 0.325*** (0.000) 0.291*** (0.000) Observations 847 847 847 847 847 847 847 847 R-squared 0.183 0.215 0.178 0.204 0.190 0.216 0.183 0.208 Adjusted R-squared 0.173 0.206 0.168 0.194 0.178 0.204 0.171 0.196
Notes: The table contains regression coefficients of all the variables used in the study: ROA (Return of assets), CCC (Cash Conversion Cycle),
RCP (Receivables Collection Period), ICP (Inventory Collection Period), PDP (Payables Deferral Period), LVRG (Debt Ratio as a proxy for leverage), LIQD and SIZE. Moreover, it also reveals the interaction effects CCC*UIA, RCP*UAI, ICP*UAI, PDP*UAI. In the table, *, **, *** refers to 10%, 5% and 1% significance levels (e.g.: *** p<0.01, ** p<0.05, * p<0.1).
This let us make an inference that reducing the cash conversion cycle by 1 day will result in an increased firm profitability by 0.03 percent. Our study succeeds in finding a consistence with the finance literature (Sagner, 2011) and also with the findings of other authors (see, e.g., Shin and Soenen, 1998; Lazaridis and Tryfonidis, 2006; Raheman & Nasr, 2007; Enqvist et al., 2014; Wasiuzzaman, 2015; Chang 2017). In general, results indicate that the oil and gas companies can focus more on the efficient management of working capital to positively influence their profitability. They can optimize a capital lockup and process costs by shortening the cash conversion cycle, which will lead to higher corporate profitability.
In the finance literature it is generally known that the lesser the number of accounts receivables, the company will more benefit from it in terms of increased profitability (Sagner, 2011). Consistent with this, the analysis of the model (II) provides support of hypothesis 2. It reveals a negative relationship between the receivables collection period and firm profitability. Specifically, the analysis reports that an increase in the number of days of accounts receivables by 1 day is associated with a decrease in ROA, which is used as a measure of firm profitability, by 0.011 percent. This result may indicate that it is important for the firms to collect their credits provided to customers as fast as possible to be able to use money on financing new value increasing and growth projects. Financing new investments in the oil and gas industry requires even more attention. Because, before the actual production of oil, companies spend much of their time, energy and cost on field discovery, evaluation and development. These processes require big finances and collection of receivables can somehow reduce the burden of the companies, because in this case they use less of the costly external financing. The importance of this is even more pronounced for the oil and gas companies from emerging markets, where the capital markets are low-quality and supply of capital is short. Moreover, another reason behind the outcome of the analysis is that fast collection of accounts receivables will decrease the risk of bad debts and defaults on commercial credits provided to customers. This will reduce the risks of bankruptcy of oil and gas companies. Overall, our finding is also in line with those of Deloof (2003), Lazaridis and Tryfonidis (2006), Raheman & Nasr (2007), Garcia-Teruel & Martinez-Solano (2007), Gill et al. (2010) and Wasiuzzaman (2015).
reducing cost it will be possible for a company operating in oil and gas industry to gain profitability. In line with this reasoning, the regression analysis reveals a negative relationship. However, we do not find a significance to the negative coefficient, which does not let us make any inference by rejecting a null hypothesis. Therefore, we do not find a support to our hypothesis 3. Gill et al. (2010) in their study also could not find significant results. However, many previous researchers such as Deloof (2003), Raheman & Nasr (2007), Enqvist et al. (2014) and Wasiuzzaman (2015) found significant negative relationship between the inventory collection period and firm profitability
With regards to the payables deferral period (PDP), analysis of model (IV) reveals a significant negative coefficient of 0.012, which does not support our hypothesis 4 stating that longer payables deferral period increases firm profitability. In contrary to our hypothesis, the result infers that an increase in the accounts payable period by 1 day will decrease firm profitability by 0.012 percent. This finding is in line with the statement that less profitable firms wait longer to pay their credits. The correlation analysis also revealed a negative relationship between the variables. The outcome is also in line with the findings of Deloof (2003), Raheman & Nasr (2007), Enqvist et al. (2014) and Wasiuzzaman (2015) who also found negative relationships between these variables. The reason behind this significant negative relationship between the variables might be that the companies need to be careful in the process of accounts payables management as it can lead to poor relationships with suppliers. Especially, this importance is more pronounced for companies from emerging markets where, according to Preve & Sarria-Allende (2010) the market size is small and the supply of capital is short. With a low-quality capital market and also during the times of financial distress, the companies from emerging markets are mostly dependent on commercial credit provisions by suppliers. Thus, by building strong relationships with suppliers, companies are able to access financing, cut costs, and maintain healthy sales margins, which will lead to better performance.
the negative relationship between cash conversion cycle and firm profitability is weaker in countries with higher uncertainty avoidance. Companies from countries with high uncertainty avoidance will reduce uncertainties, however it does not seem to manage working capital efficiently. The interaction coefficient is 0.0001 at a 99% confidence level. Moreover, as CCC length is dependent on its components, the analysis of the interaction effects on the components also were crucial. However, we also do not find support to any of them. Models (VI) and (VIII) do not reveal any significance, wheras model (VII) illustrate significance. Nontheless, model (VII), depicts a positive coefficient as opposed to hypothesized negative one. Our expectation was that oil and gas companies from countries with higher uncertainty avoidance will carry more routines and task to reduce inventory level and achieve higher profitability with cost-cutting behavior. This would mean that the negative relationship between turning inventories into sales and firm profitability would be stronger for companies from high uncertainty avoidance countries. However, results reveal that the relationship gets weaker with the moderator involvement. A possible explanation to this is described further below (see Section 4.2.2).
4.2. Robustness Analyses
4.2.1. Robustness test 1
Table 7: Robustness test with the dependent variable “ROE”
Notes: The table contains regression coefficients of all the variables used in the study: ROE (Return of Equity), CCC (Cash Conversion Cycle),
RCP (Receivables Collection Period), ICP (Inventory Collection Period), PDP (Payables Deferral Period), LVRG (Debt Ratio as a proxy for leverage), LIQD and SIZE. Moreover, it also reveals the interaction effects CCC*UIA, RCP*UAI, ICP*UAI, PDP*UAI. In the table, *, **, *** refers to 10%, 5% and 1% significance levels (e.g.: *** p<0.01, ** p<0.05, * p<0.1).
The first robustness test repeated the analyses with a use of ROE as a dependent variable. It basically reveals the same outcome as before (see table 7 above). Our robustness test has reduced the indicator of the negative effect between the cash conversion cycle and firm profitability from -0.030 to -0.009. However, it has left the high significance unchanged. Therefore, we can still infer that the lower cash conversion cycle results in the better firm profitability. On the other hand, the result on the inventory collection period is also left unchanged, which still does not let us make inference about its effect on profitability. Together with this, the
Time-Fixed:
YES I II III IV V VI VII VIII
performance on the payables deferral period reveals a coefficent of -0.012, with the highest level of significance, which is the same as in the main model. In regards to the receivables collection period, the coefficient is still negative and highly significant, but the negative effect is stronger (-0.020). The interaction effects illustrate the same results as before, also with a little changes in coefficients. Moreover, the coefficients of control variables are reduced, whereas the liquidity does not reveal any significance at all. Together with this, a closer look at the R-squared coefficients of all the models from the regressions with the dependent variable of “ROE” (table 7) will show that they are almost twice as low as in the regression models where explanatory variables are regressed on “ROA”. In general, it seems that ROE is not a right measure of profitability to be used in this study. Indeed, the study by Hong et al. (2011) who investigated the relationship between working capital efficiency and firm profitability in Brazil, found significant results with ROA but failed to reach significance with ROE as a dependent variable.
Overall, the major changes that this robustness test brought was the change of coefficients for some of the variables, lost significance on liquidity variable across all the models and, most importantly, decreased R-squared. Therefore our robustness test does not explain our models better than our actual test. Thus, we consider the results from table 6.
4.2.2. Robustness test 2
Table 8: Robustness test with the sample comprised of 5 years (excluding the period of 2014-2015)
Notes: The analysis excludes the years of instabilities in oil and gas industry (2014-2015). The table contains regression coefficients of all the
variables used in the study: ROA (Return of assets), CCC (Cash Conversion Cycle), RCP (Receivables Collection Period), ICP (Inventory Collection Period), PDP (Payables Deferral Period), LVRG (Debt Ratio as a proxy for leverage), LIQD and SIZE. Moreover, it also reveals the interaction effects CCC*UIA, RCP*UAI, ICP*UAI, PDP*UAI. In the table, *, **, *** refers to 10%, 5% and 1% significance levels (e.g.: *** p<0.01, ** p<0.05, * p<0.1).
On the other hand, instability of 2014-2015 for oil and gas industry paved the way for different results of the moderation effect in our study. When we exclude the last two years to mitigate the effect of 2014-2015, we observe that the sign of the coefficient for the interaction effect (ICP*UAI), in contrast that of the main model, becomes negative which is consistent with our hypothesis. However, we do not have enough statistical evidence to show its significance. This can be due to our sample size or other factors not considered in this study. On the other hand, considering oil and gas are in the list of the most speculated commodities, thus in order to make profit
from speculation, economic effect can be more prevelant than cultural effect. As
Time-Fixed:
YES I II III IV V VI VII VIII
noted previously in the literature, oil and gas companies may also decide to keep inventory for specualtive purposes when they expect future prices to be different than current prices (Jaffe and Soligo, 2002). To elaborate more, companies can be forced to speculate with excess of their inventories because of unexpected changes in oil and gas prices can enforce companies to abide by economic reasoning rather than cultural ones.
5. Conclusion and Further Research
5.1. Summary and Conclusion
will also reduce risks of credit defaults that can jeopardize firm performance. On the other hand, we do not find significant results for the inventory conversion period and the payables deferral period. We also do not find enough evidence to support our hypotheses on the moderating effect of cultural uncertainty avoidance to the relationships under investigation. Nevertheless, our research is engaged in the interesting discussion regarding the interaction effect of the inventory collection period and cultural uncertainty avoidance (ICP*UAI). The main analysis, as opposed to our hypothesis 6, revealed that uncertainty avoidance makes the negative relationship between the ICP and ROA weaker. However, after excluding the years of observed instabilities in the oil and gas industry, the coefficient of the interaction appeared to be negative but insignificant. The insignificance of the coefficient can be explained by the small sample size or by other factors not considered in this study. The reason of a change in the direction of the coefficient of the ICP*UAI might be that for companies to make profits from speculation, economic effect should be more
prevelant than cultural effect. Literature suggests that oil and gas companies may
decide to keep inventory for specualtive purposes when they expect future prices to be different than current prices (Jaffe and Soligo, 2002). Indeed, companies tend to speculate with excess inventories because of unexpected changes in oil and gas prices. This can enforce companies to abide by economic reasoning rather than cultural ones. Moreover, we also perform a robustness test with a different measure of firm profitability (ROE instead of ROA) and reveal the change of coefficients for some of the variables, lost significance on liquidity variable across all the models and, most importantly, decreased R-squared. Therefore we conclude that our initial test with a dependent variable of ROA explains our models better than the robustness test.
with our findings, managers can reduce the length of the cash conversion cycle to enhance corporate profitability. It is of great importance, especially for companies from emerging markets, to concentrate mostly on collecting accounts receivables as fast as possible. The reason behind this is that in contrast to developed world, emerging markets are less stable. Thus, credit control managers should make sure that the collection of account receivables is not delayed. Otherwise, it can jeopardize a firm performance. Moreover, upstream operations of oil and gas companies are very costly. Therefore, collection of account receivables will allow managers to increase cash holdings, which can then be used in financing new growth-promising operations with less costs than it would be with external financing. Secondly, based on the discussion of our results, managers are advised to make optimal decisions by weighting the advantages and disadvantages of keeping inventories as a speculation purpose or selling them fast to reduce costs. The optimal decision making should be driven more by economic rather than cultural reasoning.
5.2. Suggestions for future work
References
Anjum, S., & Malik, Q.A. (2013). Determinants of Corporate Liquidity- An Analysis of Cash Holdings. Journal of Business and Management, 7(2), 94-100.
Almeida, H., Campello, M., & Weisbach, M. S. (2004). The cash flow sensitivity of cash. The Journal of Finance, 59(4), 1777-1804.
Banos-Caballero, S., Garcia-Teruel, P.J. & Martinez-Solano, P. (2010). Working capital management in small and medium-sized enterprises. Accounting and Finance
Journal 50(1), 511-527.
Banos-Caballero S., Garcia-Teruel J.P. & Martinez-Solano, P. (2012). How does working capital management affect the profitability of Spanish SMEs? Small Business
Economics, 39(2), 517-529.
Banos-Caballero, S., Garcia-Teruel, P.J. & Martinez-Solano, P. (2014). Working capital management, corporate performance and financial constraints. Journal of
Business Research 67(3), 332-338.
Baltagi, H. B. (2005). Econometric analysis of panel data (3rd ed.). Chichester: John
Wiley and Sons.
BP (2017). BP Statistical Review of World Energy.
Chang, C. C. (2017). Cash conversion cycle and corporate performance. Global evidence. International Review of Economics & Finance 56(1), 1-14.
Chui, A., Lloyd, A. & Kwok, C. (2002). The Determination of Capital Structure: Is National Culture a Missing Piece to the Puzzle? Journal of International Business
Studies 33(1), 99-127.
Ching, Hong & Novazzi Ayrton & Gerab, Fabio. (2011). Relationship between working capital management and profitability in Brazilian listed companies. Journal
of Global Business and Economics 3(1), 74-86
Deloof M. (2003). “Does working capital management affect profitability of Belgian firms?” Journal of Business Finance and Accounting 30(3), 573-588.
Enqvist, J., Graham, M. & Nikkinen, J. (2014). The impact of working capital management on firm profitability in different business cycles: Evidence from Finland.
Research in International Business and Finance, 32(3), 36-49.
Ernst and Young, (2014). Cash in the barrel. Working capital management in the oil and gas industry.
Eljelly, A.M.A. (2004). Liquidity - profitability tradeoff: an empirical investigation in an emerging market. International Journal of Commerce and Management 14(2), 48-61.
Garcia-Teruel J.P., & Martinez-Solano, P. (2007). Effects of working capital management on SME profitability. International Journal of Managerial Finance,
3(2), 164-177.
Gill, A., Biger, N. & Mathur, N. (2010). The relationship between working capital management and profitability: evidence from the United States. Business and
Economics Journal 10(1), 1-9.
Guiso, L., Sapienza, P. & Zingales, L. (2006). Does Culture Affect Economic Outcomes?. The Journal of Economic Perspectives 20(2), 23-48.
Hofstede, G. (2001). Culture’s Consequences, Comparing Values, Behaviors, Institutions, and Organizations across Nations, Beverly Hills, CA, Sage.
Jaffe, A.M., & Soligo, R. (2002). The role of inventories in oil market stability. The
Quaterly Review of Economics and Finance 42(1), 401-415.
Kwok, C., Tadesse, S. (2006). National culture and financial systems. Journal of
International Business Studies 37(2), 227-247.
Lazaridis, I. & Tryfonidis, D. (2006). Relationship between working capital management and profitability of listed companies in the Athens stock exchange.
Journal of Financial Management and Analysis, 19(1), 26-35.
Mawutor J. K. Mensah (2014). Working Capital Management and Profitability of Firms: A study of listed manufacturing firms in Ghana. Research Journal of Finance
and Accounting 5(22), 1-13
Molina, C., and L.A. Preve (2009). “Trade Receivables Policy of Distressed Firms and Its Effect on the Cost of Financial Distress” Financial Management 38(3), 663– 686.
Nijam H. Mohamed (2016). Cash Conversion Cycle, Its Properties and Profitability: Evidence from listed hotel companies in Sri Lanka. Research Journal of Finance and
Accounting 7(1), 23-32
Oussov A. (2018). Global Head of Oil & Gas and Head of Oil & Gas in Russia and the CIS, KPMG in Russia: Renewed confidence for 2018 in the Oil & Gas Industry.
Retrieved from
https://home.kpmg.com/xx/en/home/insights/2018/01/market-update-oil-gas.html
Preve A. Lorenzo, Sarria-Allende V. (2010). Working Capital Management. Financial Management Association Survey and Synthesis Series. Oxford University Press. Raheman, A. & Nasr, M. (2007). Working capital management and profitability- case of Pakistani firms. International review of business research paper 3(1), 279-300. Sagner. S. James (2011). Essentials of Working Capital Management. John Wiley & Sons, Inc., Hoboken, New Jersey.
Seifert, B., Gonenc, H., (2016). Creditor Rights, Country Governance, and Corporate Cash Holdings. Journal of International Financial Management & Accounting 27(1), 65-90.
Sharma, A.K., & Kumar, S. (2011). Effect of working capital management on firm profitability: Empirical evidence from India. Global Business Review, 12(1), 159-173. Shin, H.H. & Soenen, L. (1998). Efficiency of working capital management and corporate profitability. Financial Practice & Education, 8(2), 39-45.
Wasiuzzaman S. (2015). Working Capital and Profitability in Manufacturing Firms in Malaysia: An Empirical Study. Global Business Review 16(4), 545-556.
World Economic Forum (2017a), Digital Transformation Initiative: Oil and Gas Industry
World Economic Forum (2017b), Mapping Global Transformations: Oil and Gas Industry, Co-curated with James A. Baker III Institute for Public Policy, Rice University. Retrieved from:
https://toplink.weforum.org/knowledge/insight/a1Gb0000000LOnGEAW/explore/su mmary
APPENDICES
Appendix I: Sample composition by country
Appendix II: Overview of Hypotheses
ROA (Base model) Expected sign Resulted sign
H1 CCC-ROA - - *** Supported
H2 RCP-ROA - - *** Supported
H3 ICP-ROA - - No significance Rejected
H4 PDP-ROA + - *** Rejected
H5 RCP*UAI-ROA - 0 No significance Rejected
H6 ICP*UAI-ROA - + * Rejected
H7 PDP*UAI-ROA + 0 No significance Rejected
H8 CCC*UAI-ROA - + *** Rejected
ROE (Robustness test
1) Expected sign Resulted sign
H1 CCC-ROE - - *** Supported
H2 RCP-ROE - - *** Supported
H3 ICP-ROE - - No significance Rejected
H4 PDP-ROE + - *** Rejected
H5 RCP*UAI-ROE - 0 No significance Rejected
H6 ICP*UAI-ROE - + *** Rejected
H7 PDP*UAI-ROE + 0 No significance Rejected
H8 CCC*UAI-ROE - + *** Rejected
ROA (Robustness test 2:
no 2014-2015) Expected sign Resulted sign
H1 CCC-ROA - - *** Supported
H2 RCP-ROA - - *** Supported
H3 ICP-ROA - - No significance Rejected
H4 PDP-ROA + - *** Rejected
H5 RCP*UAI-ROA - - No significance Rejected H6 ICP*UAI-ROA - - No significance Rejected
H7 PDP*UAI-ROA + - * Rejected
