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Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 41 WORKING CAPITAL CYCLE AND PERFORMANCE OF LISTED INFORMATION COMMUNICATION TECHNOLOGY FIRMS IN NIGERIA *UZOAMAKA, ERNEST CHINYERUGO, MSc Anambra State University, Igbariam Campus +2348065480208 [email protected] PROF. NWEZE, AUSTIN UCHECHUKWU, PhD Rector, Institute of Management and Technology (IMT), Enugu [email protected] OFFOR, NKECHI T., PhD Anambra State University, Igbariam Campus [email protected] IKEDIASHI, ONYEKA VINCENT, MSc, MBA, FCA Ikediashi & Co Chartered Accountants, Asaba [email protected] *CORRESPONDING AUTHOR Abstract The trade-off between liquidity and profitability made issues relating to working capital management and corporate performance to be widely explored by researchers. The voluminous empirical literature is inconclusive about the effect of working capital cycle on corporate performance. Therefore, this study examined the effect of working capital cycle on corporate performance. 7 out of 9 Information Communication Technology [ICT] companies listed on the Nigeria Stock Exchange (NSE) between 2012 and 2015, whose annual reports were available were purposively sampled. Multiple regression model evaluated with OLS estimation technique was used. Secondary data sourced from annual reports of the selected firms from which measures of performance and working capital cycle were extracted. Cash conversion cycle (CCC), account collection period (ACP), inventory conversion period (ICP), account payment period (APP), working capital ratio were used as the independent variables while firm performance measures used are: return on assets (ROA), return on equity (ROE) and earnings per share (EPS), and firm size was introduced as the control variable. The results indicate that all the working capital component used including firm size does not significantly affects any of the performance indicators used. It is recommended that ICT firms in Nigeria should not consider working capital management while making decision on corporate financial performance (ROE, ROA, and EPS) but should manage the working capital in such a way that it will not affect the firm’s solvency and bankruptcy level. Keyword: working capital cycle, corporate performance, information technology firms, working capital ratio, Nigeria. I. INTRODUCTION Background to the Study Working capital management (WCM) involves planning and controlling liquidity in a way that eliminates the possibility of inability to meet due short term obligations, and avoid too much investment in liquid assets(Soku, 2011). A general measure of working capital management is the cash conversion cycle or working capital cycle, that is, the time taken from the date of the purchases of inventory to the collection of sales of finished goods. Zahra and Azam (2012) pointed out that, management of working capital cycle is very significant for every entity that has large investment in stocks, payables and receivables. The www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 42 profitability and liquidity of business entity depends on the ability of the firm to manage its working capital. (Raheman and Nasr, 2007). Melita, Maria and Petros(2010), provides that reducing the length of cash conversion cycle and optimum use of working capital is very significant for entities in the struggle to survive the recent economic volatility and the increasing difficulty of accessing funds. The management of working capital is a necessity to the financial health of all businesses. Vijayakumar (2011) for instance, revealed that the higher the time lag, the higher the investment in working capital. Many companies in Nigeria operate at a profit but they still run into serious financial difficulties and inadequate capital to provide the cash needed to pay creditors as at when due because of overtrading, (Kolapo, Oke and Ajayi, 2015). To manage accounts receivable, many firms in Nigeria used aggressive collections approach and restrict credit sales to customers to manage cashflow. But this aggressive collection policies and restriction on sales credits has led to lost sales which ultimately reduce profits (Osundina and Osundina2014). Statement of Problems Many studies have been carried out on working capital management, working capital cycle/cash conversion cycle or cash management and corporate performance both with accounting performance and market performance measurements. Based on the available studies carried out in Nigeria and outside (both developing and developed nations), there had been conflicting result on the effect of working capital management and its effect on performance. Three main results found among these studies on working capital cycle are: significant positive effect, negative effect, and no significant effect. In Nigeria for instance, Iyewumi, Remy and Omotayo (2015), Onodje (2014), Osundina and Osundina(2014), Uremadu, Egbide and Enyi (2012), Angahar and Alematu (2014), found positive effect; Ogundipe, Idowu and Ogundipe (2012), Kajola, Nwaobia and Adedeji(2014), Onyemaobi (2014), Uremadu, Egbide and Enyi (2012), Muhammad, Abdul and Zahid (2015), Uwuigbe, Uwalomwa and Egbide (2012), Takon (2013), Takon and Atseye (2015), Kolapo, Oke and Ajayi, (2015), found significant negative effect while Adeleke and Mukolu (2013) Owolabi and Alu (2012), Emeni and Uruakpa (2013) found insignificant effect. Even then, most of the studies were carried out in manufacturing firms; very few studies were carried out outside manufacturing sector. There is still paucity or scanty literature on the quoted ICT sector in Nigeria. Based on our extensive literature reviewed, the only available studies on IT firms is the study of Justyna (2013) that investigate the liquidity impact on profitability in polish listed IT companies. All his liquidity variables (including CCC, APP, ACP and ICP) are positive and significantly affect corporate performance. The importance of working capital management on business organization, cannot be over emphasized, hence the argument on the effect of working capital will continue to be an important issues/topic among researchers. Therefore, this study contributes to the debate of the effect of working capital cycle on corporate performance using quoted ICT firms that covers period after IFRS adoption (2012-2015) as a point of reference. Objectives of the Study The specific of objectives of the study are to examine: 1. 2. 3. Whether cash conversion cycle, Account Collection Period (ACP), Account Payment Period (APP), Inventory Conversion Period (ICP), and working capital ratio affects corporate performance measured as earnings per share The effect of cash conversion cycle, Account Collection Period (ACP), Account Payment Period (APP), Inventory Conversion Period (ICP), and working capital ratio affects corporate performance measured as return on assets Whether cash conversion cycle, Account Collection Period (ACP), Account Payment Period (APP), Inventory Conversion Period (ICP), and working capital ratio affects corporate performance measured as return on equity Statement of Hypotheses For this study, the following null research hypotheses were formulated: 1. 2. 3. Cash conversion cycle, account collection period, account payment period, inventory conversion period and working capital ratio does not significantly affects corporate performance measured as earnings per share. There is no significant effect of Cash conversion cycle, account collection period, account payment period, inventory conversion period and working capital ratio on corporate performance measured as return on assets. Cash conversion cycle, account collection period, account payment period, inventory conversion period and working capital ratio have no significant effect corporate performance measured as return on equity. II. REVIEW OF RELATED LITERATURE www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 43 Mohammad and Mahum (2014), explores working capital management and corporate performance using 2007-2011 annual reports of cement, chemical and engineering sectors of Pakistan. The results of Pooled ordinary least squares method indicate that average payment period negatively related to performance while cash conversion cycle has a positive significant association with return on equity. However average collection period, operating cycle and age of inventory have no significant association with return on equity. Syed, Mawih and Faris (2015) explore the effect of working capital (WC) management efficiency on the operating performance of industrial companies listed in the Muscat Securities Exchange (MSM) in Sultanate of Oman. The regression results revealed that the first model is significant and there are only cash conversion cycle and NWCR have impact on the NOP, but the second model is insignificant. Justyna (2013) investigate the liquidity impact on profitability in polish listed IT companies using Pearson correlation to analyze 10 listed IT firms for the period 2003-2011. Accounts payables conversion period, receivable conversion period, inventory conversion period are positively related to profitability of polish listed IT companies’. The liquidity impact on profitability in polish listed IT companies proved the existence of statistically significant relationship between liquidity and profitability. Rizwan (2016) examined the impact of the liquidity management on the performance of the 64 Pakistani non-financial companies constituting Karachi Stock Exchange (KSE) 100 Index for the period of 2006-2011. The results of descriptive statistical, correlation and multivariate regression analyses indicate that liquidity variables current ratio and the cash conversion cycle have significant positive impact on profitability (ROA). Kaveh (2015) investigate the effect of Cash Conversion Cycle on Return on Asset. The study used Cash conversion cycle index and other control variables such as financial leverage and GDP index for the accepted companies in Tehran Stock Exchange. The results of the study show that cash conversion period has a negative effect on the ROA. But by separation of firms in different industries, the effect is different. Also, the control variables, GDP index have a significant effect on the ROA Paul et al (2013) study analyzed the effects of working capital management on the profitability of 9 manufacturing firms listed on the Nairobi Securities Exchange. The result of multiple regression and correlation analyses revealed that gross operating profit was positively correlated with average collection period and average payment period but negatively correlated with cash conversion cycle. The relationship between inventory turnover in days and gross operating profit was insignificant. Mohammad and Tayebeh (2014) determined the effect of Cash Conversion Cycle (CCC) and the deviation from optimal level of CCC on profitability of the firms in the Iranian capital market. A sample consisted of 97 listed companies on Tehran Stock Exchange (TSE) for the period 2005 to 2011 was subjected to the study. The results do not indicate any significant relationship between profitability, measured as the ratio of Net Operating Income (NOI) to sales, and Cash Conversion Cycle (CCC). Deviation from the optimal CCC level has an inverse and significant effect on profitability. The relationship between the cash conversion cycle and profitability of companies listed in Tehran Stock Exchange (with emphasis on the type of industry) was investigated Mahmoud, Amir and Ali (2015). The study has one hypothesis in which the relationships between the cash conversion cycle and profitability were tested in three industries separately. Multivariate linear regression analysis was used to test the hypothesis. Overall, the results indicated a significant inverse relationship between the cash conversion cycle and profitability in the automotive and cement industries, but a significant relationship was not observed between cash conversion cycle and profitability in the pharmaceutical industry. Muhammad et al (2014), study the relationship between cash conversion cycle (CCC) and performance of 16 cement industry of Pakistan for the period 2007 to 2012. The findings of the study show negative relationship between firm’s cash conversion cycle and profitability. In the Nigerian context, Uremadu et al (2012) presents empirical evidence of the effect of working capital management and liquidity on corporate profits using a cross-sectional time series data for the period 2005-2006. The result of descriptive statistics and OLS methodology revealed positive effect of inventory conversion period (ICP), debtors’ collection period (DCP); and a negative effect of cash conversion period (CCP), creditor’s payment period (CPP), on return on assets. Owolabi, and Obida, (2012) measures the relationship between liquidity management and corporate profitability using data from selected manufacturing companies quoted on the floor of the Nigerian Stock Exchange. The result of descriptive analysis show that liquidity management measured in terms of the company’s Credit Policies, Cash Flow Management and Cash Conversion Cycle has significant impact on corporate profitability Iyewumi et al (2015) investigated the relationship between working capital management and firm’s profitability in the oil and gas sector in Nigeria. The study is based on secondary data collected from sample of two listed oil firms in Nigeria for the period 1995 to 2011. The results of correlation analysis and OLS estimation technique provide that firm’s profitability in Nigeria is affected by; the working capital management components which are; cash conversion cycle, average days’ receivables, average days payables, average days inventories. Second, size of the firm is equally another important variable found to affect the profitability of firms. Onodje (2014), determines whether the internal financial activity of working capital management affects the performance of 75 Nigerian manufacturing companies for the period 2002 -2012 They found that working capital management is a determinant of manufacturing performance in Nigeria. Receivable conversion period and inventory conversion period are positively related to manufacturing performance. On the other hand, payable deferral period, cash conversion cycle and the debt-equity ratio period are negatively related to manufacturing performance. Ogundipe et al (2012), study examines the impact of working capital management on firms’ performance and market value of 54 non-financial firms in Nigeria for the period 1995-2009. The result shows there is a significant negative relationship between cash conversion cycle and market valuation and firm’s performance. It also shows that debt ratio is positively related to market valuation and negatively related firm’s performance. www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 44 Osundina and Osundina (2014) examine the effect of working capital management on market value of quoted food and beverages manufacturing firms in Nigeria. Survey research design was employed using primary data. The result of Pearson Product Moment Correlation and multiple regression analysis indicate that working capital management had significant positive effect on market value of food and beverages manufacturing firms in Nigeria. Also, the outcome of this study shows that Cash conversion cycle, Account Collection Period, Inventory Conversion Period, Account Payment Period, had significant effect on market value of food and beverages manufacturing firms in Nigeria. Kajola et al (2014) examines the impact of working capital management on firms’ financial performance of thirty manufacturing firms listed on the Nigerian Stock Exchange for the seven-year period 2004-2010. Results reveal that working capital management (Cash Conversion Cycle) is negatively and significantly related with firm’s financial performance (ROA). The Impact of Cash Management on Firms’ Financial Performance of Some Selected Manufacturing firms was undertaken by Onyemaobi (2014). The results of correlation and linear regression of 17firms (2000-2010) confirms the theoretical negative relationship between cash conversion cycle and cash conversion efficiency of manufacturing firms. Sabo et al (2015) examines the impact of working capital management on corporate profitability of 7 listed firms in Nigeria for the periods of 2008 to 2012. The results of descriptive statistics and GLS regression analysis confirm a positive relationship among Average Collection Period (ACP), Current Ratio (CR) and the size of the firm (LOGSIZE) with Profitability and a negative relationship with Inventory Turnover Period (ITP) and Average Payment Period (APP). Olubukunola et al (2012) investigates empirically, the relationship between cash management and profitability of 15 listed manufacturing companies in Nigeria from 2005-2009. The result of Pearson’s correlation and regression analysis show that there is a strong negative relationship between cash conversion cycle and profitability of the firms. Takon (2013), investigate the impact of Cash Conversion Cycle on performance of Nigerian quoted firms for the period, 20002009. Multiple regression analyses indicate that cash conversion cycle had a significant negative relationship with profitability Takon and Atseye (2015) investigated the effect of working capital management on profitability of Nigerian listed companies during the period 2000-2009. The panel data, pooled OLS regression and fixed effects results showed that there is a strong negative relationship between working capital management and profitability. Liquidity had a positive and strong significant relationship with return on assets. Age also had a positive relationship with profitability while accounts receivable had a negative significant relationship with return on assets as to increase firms’ profits. Duru and Okpe (2015), examines the effect of cash conversion cycle on the performance of 3Health Care Companies in Nigeria. Generalized least square multiple regressions test show that both cash conversion cycle and debt ratio had negative and significant effect on the profitability of Health Care Companies in Nigeria. Adeleke and Mukolu (2013) empirically assessed the impact of working capital on organization performance using ten listed companies across different industries in Nigeria. Multiple regression analysis discovered that while six of the companies used showed a negative relationship between working capital and organization performance, four showed a positive relationship. It was also discovered that working capital of all the ten firms do not have significant impact on their performances during the period under review. Owolabi, and Alu, (2012) adopted an Ex-post - facto research involving trend analysis of five years’ financial statements of five manufacturing companies to study working capital and firm performance. Multivariate analyses test indicated that each working capital component affected the company’s level of profitability at varying rates, but, these effects when pooled together are not significant. Emeni and Uruakpa (2013) ascertain the nature of the relationship between working capital management and profitability of 142 firms in Nigeria for the period 2009-2011. Ordinary Least Squares (OLS) regression technique result shows that all the explanatory variables (LQD, DBT, CCC) except CCC were positively associated with profitability of firms in Nigeria. It was also found that only LQD is significantly associated with profitability. Solabomi and Oboh (2013) provided evidence on the interaction between working capital management and corporate profitability of 35 manufacturing companies listed on the Nigerian stock exchange for a two-year period (2011 - 2012). Panel exploration and Factorial-ANOVA estimation results suggested a positive significant relationship between debt structure and profitability but no significant relationship between firm’s working capital composition and profitability. Angahar and Alematu (2014) examined the impact of working capital management on profitability of Nigerian 4 Cement Industry for a period of eight (8) years (2002-2009). The result of multiple regression analysis revealed insignificant negative relationship between the profitability (measured by ROA) and the number of days’ accounts receivable are outstanding (DAR); significant negative relationship between the profitability and the number of days’ inventory are held (DINV); and significant positive relationship between the profitability and the cash conversion cycle (CCC). Kolapo et al (2015) evaluates the effect of working capital management (WCM) on corporate performance of selected listed firms in Nigeria from 2001-2010. The findings reveal that WCM affects ROA and GWC; however, none of the measures of WCM has significant influence on GWC while APP and CCC exert significance on ROA. Oladipupo and Okafor (2013), examines firm’s working capital management practice and profitability of twelve manufacturing companies on the Nigeria Stock Exchange over five years’ period (2002 to 2006). The result of OLS regression technique indicates that shorter net trade cycle and debt ratio promotes high corporate profitability. While the level of leverage has negative significant impact on corporate profitability Salman et al (2014) investigated the relationship between working capital management on profitability of 20 manufacturing companies quoted in Nigerian Stock Exchange from 2005-2013. Multiple regressions analyses result showed that working capital has negative and significant relationship with the Return on Assets (ROA) and Return on Equity (ROE) at 5% level. Summary of Literature Reviewed www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 45 We have reviewed the related literature on working capital components and corporate performance. Our literature review covers current literature on empirical studies .The relationship between working capital and firm performance has been a subject of several empirical investigations for several decades. At least three main deductions can be drawn from our review. First, the relationship between working capital cycle and firm performance has received a fair amount of empirical attention. Second, the findings of the studies are somewhat mixed: slightly more 45% of them found significant negative relationship, while over 35% found positive significant relationship, while the remaining less than twenty percent found no significant effect of working capital cycle on firm performance/value. Third, the analysis of the relationship between working capital components and firm performance in developing countries in general, were centered on manufacturing firms, little attention was made to study non-manufacturing sectors, therefore non-manufacturing sectors received little or limited empirical attention. In Nigeria, empirical studies carried out so far on working capital cycle and firm’s performance provide mix results and most of the studies in Nigeria were carried out in the manufacturing firms. Even then most studies carried out on manufacturing sectors used only one performance measure. Save the study of Justyna (2013) that investigated the liquidity impact on profitability of polish listed IT companies, no literature exists on ICT/IT quoted companies on subject of this study. This study seeks to add to the stock of knowledge on the phenomenon of interest using Information Communication Technology [ICT] sector that has little empirical attention in prior studies. Therefore, this study investigates the effect of working capital cycle on corporate performance (measured using three accounting performance ROA, ROE and EPS) using Nigerian listed ICT sector as the point of reference. III. METHODOLOGY For this study, the quantitative research was adopted, since the researcher want to be objective by using secondary data rather than opinion. The type of quantitative design used is correlational research design; this is because of the need to determine the correlation (effect and relationship) between the dependent variables and independent variables. In the determination of sample size, purposive sampling technique was adopted because of the nature of the research. 7 out of 9 listed ICT firms were selected based on data availability (listed ICT firms that is in operation for the period of the study, 2012 to 2015 and has its complete annual reports and account available to the public). The main sources of data used for this study is the annual reports and accounts of the 7 listed ICT firms for the period 2012 to 2015. Model Specification and Justification The multi-variant equation technique of Ordinary Least Square was employed for this study. This has become expedient because of its theoretical plausibility, explanatory ability, accuracy of the parameter estimate, simplicity and forecasting ability (Gujarity, 2004). From the literature review, it was observed that, there is a causal link between working capital cycle and corporate performance. In the study, we control the effect of firm’s size by introducing natural logarithm of firm’s size in the model as a control variable. Performance (proxy as ROA; ROE; & EPS) = f(CCC, ACP, APP, ICP, WCR and NLFSIZ) Model I EPSii= β0+β1CCCit+β2ACPit+β3APPit+β4ICPit+β5WCRit+β6NLFSIZit + Ut……………. (1) Model II ROAit= β0+β1CCCit+β2ACPit+β3APPit+β4ICPit+β5WCRit+β6NLFSIZit + Ut……………. (2) Model III ROEit = β0+β1CCCit+β2ACPit+β3APPit+β4ICPit+β5WCRit+β6NLFSIZit + Ut……………. (3) Where, Dependent Variables ROAit = Return on Assets for the firm i in the year t www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 46 ROAit = Return on Equity for the firm i in the year t EPSit = Earnings Per Share for the firm i in the year t Independent Variables CCCit = Cash Conversion Cycle for the firm i in the year t ACPit =Account Collection Period for the firm i in the year t APPit = Account Payment Period for the firm i in the year t ICPit = Inventory Conversion Period for the firm i in the year t WCRit = Working Capital Ratio for the firm i in the year t LNFSIZit = Natural Logarithm of Firms Size for the firm i in the year t --------- Control Variable The parameter β1, β2, β3, β4………….Β6 which are coefficient of the variables represents the degree of change of the dependent variables (ROA, ROE, and EPS) because of a unit change of other independent variables (CCC, ACP, APP, ICP, and WCR). The error term (Ut) is used to capture the effect of other variables that are not included in the model. Table 1 Variables Measurement Variables Measurement Dependent variables 1 ROA Net operating Profit (after adjustment for interest and tax) divided by average total assets. 2 ROE Net operating income after tax divided by total equity 3 EPS Net income after tax divided by number of share issued Independent variables 1 ACP Average Account Receivable X 365 divided by Sales 2 APP Average Account Payables X365 divided by Cost of Sales 3 ICP Average Inventory X 365 divided by Cost of sale (or Purchases if available) 4 CCC ACP + ICP – APP 5 WCR Current assets divided by current liabilities Control variable 1 NLFSIZ Natural logarithm of total revenue Sources: Researchers Variable Measurement, 2016 Method of Data Analyses The Ordinary Least Square Multiple Regression model was used for data analyses. This statistical tool also includes correlation matrix, descriptive statistics, t-test (for individual significance), F-test (for overall significance of the model), R2 (for goodness of fit) multi-collinearity and Durbin Watson test IV. PRESENTATION AND DISCUSSION OF RESULTS 4.1 Presentation of Results www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 4.1.1 47 Preliminary Test The descriptive statistics of all the variables in the study is presented on table 2 while the correlation test is shown on table 3 Table 2. Descriptive Statistics N Minimu Maximu m m Mean Std. Deviation EPS 28 -986.00 212.00 -25.9379 193.51518 ROA 28 -37.89 23.51 3.0543 9.89196 ROE 28 -101.52 15.52 -2.9279 27.96381 CCC 28 -2999.03 780.16 -98.4625 846.48329 ACP 28 32.24 1123.67 251.3489 261.06024 ICP 28 16.46 1249.46 212.9846 294.34176 APP 28 9.28 3252.40 562.7961 797.53681 WCR 28 74.75 416.13 163.4646 69.07570 NLFSIZ 28 14.33 16.43 15.4832 .61578 Source: researchers’ computation 2017 using SPSS 20 Table 2 above shows the descriptive statistics of the variables used for the study. Cash conversion cycle (CCC) has the highest standard deviation while account payment period has the highest mean score; log of firm size has the lowest standard deviation. The table also shows the mean, the lowest, highest, and the standard deviation (chance of occurrence) for each of the variables used in this study. Table 3 Correlations Results EPS EPS ROA ROE CCC WCR NLFSIZ ACP ICP APP 1 ROA .208 (Sig) (.289) ROE .615** .841** (Sig) (.001) (.000) CCC -.021 -.089 -.070 (Sig) (.916) (.653) (.723) WCR .242 .205 .366 .038 (Sig) (.214) (.295) (.055) (.849) -.043 -.274 -.196 .420* .001 (.828) (.158) (.318) (.026) (.998) ACP .104 * -.234 .313 .104 .607** (Sig) (.598) (.043) (.230) (.104) (.599) (.001) .166 -.231 -.043 .270 .351 .356 .868** (.398) (.237) (.830) (.164) (.067) (.063) (.000) * .260 .303 .488** .438* (.181) (.116) (.008) (.020) NLFSIZ (Sig) ICP (Sig) 1 -.386 1 1 APP .034 -.272 -.159 -.422 (Sig) (.865) (.162) (.418) (.025) 1 1 1 1 1 **Correlation is significant at the 0.01 level (2-tailed), *.Correlation is significant at the 0.05 level (2-tailed). Source: researchers’ computation 2017 using SPSS 20 www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 48 Table 3 shows the correlation between the variable of the study. From the table, ROA is correlated with EPS, ACP; ROE is correlated with ROA; APP and firm size are related with CCC; ACP is related to firm size; ICP and APP are correlated with ACP; APP is correlated with ICP other variables has no significant relationship. The correlation matrix shows that there is no multi correlation among the variables of the study. 4.1.2. Regression Results The results of the estimation of model 1-3 are presented on tables 4-6 below. Table 4: Summary of Regression Model Result for Model One Dependent variable: Earnings per Share Method: Ordinary Least Square. Period of study: 2012 – 2015 Included Observations: 28 Variable Coefficient Std. Error t-statistic P. value Constant 17.962 1446.183 .012 .990 CCC -.048 .081 -.588 .563 ACP 70.070 129.273 .542 .594 ICP -7.173 93.877 -.076 .940 APP -30.304 43.915 -.690 .498 WCR .832 .742 1.121 .275 NLFSIZ -22.315 104.667 -.213 .833 R-Square: .100; F-statistic: (6, 21) .387, Prob(F-statistic): .879; Adjusted R-squared: -.158; Durbin-Watson Stat: 2.601 Source: researchers’ computation 2017 using SPSS 20 Since the F-ratio and t-test P-value for the variables are greater than 0.05, we retain the null hypothesis (H0) and conclude that: “CCC, ACP, ICP, APP and WCR does not significantly affect EPS” Table 5: Summary of Regression Model Result for Model Two Dependent variable: Return on Assets Method: Ordinary Least Square. Period of study: 2012 – 2015 Included Observations: 28 Variable Coefficient Std. Error t-statistic P. value Constant -5.797 67.133 -.086 .932 CCC -.003 .004 -.681 .504 ACP -4.540 6.001 -.757 .458 ICP 1.980 4.358 .454 .654 APP -2.111 2.039 -1.035 .312 WCR 0.039 .034 1.125 .273 NLFSIZ 1.766 4.859 .363 .720 R-Square: .257; F-statistic: (6, 21) 1.214, Prob(F-statistic): .338; Adjusted R-squared: .045; Durbin-Watson Stat: 1.725 Source: researchers’ computation 2017 using SPSS 20 Since the F-ratio and t-test P-value for the variables are greater than 0.05, we accept the null hypothesis (H 0) and conclude that: “CCC, ACP, ICP, APP and WCR does not significantly affect ROA” Table 6: Summary of Regression Model Result for Model Three Dependent variable: Return on Equity Method: Ordinary Least Square. Period of study: 2012 – 2015 Included Observations: 28 www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 Variable Coefficient 49 Std. Error t-statistic P. value Constant -91.765 187.394 -.490 .629 CCC -.010 .011 -.918 .369 ACP -12.133 16.751 -.724 .477 ICP 9.052 12.164 .744 .465 APP -6.663 5.690 -1.171 .255 WCR .159 .096 1.651 .114 NLFSIZ 7.548 13.563 .557 .584 R-Square: .276; F-statistic: (6, 21) 1.335, Prob(F-statistic): .286; Adjusted R-squared: ..069; Durbin-Watson Stat: 2.178 Source: researchers’ computation 2017 using SPSS 20 Since the F-ratio and t-test P-value for the variables are greater than 0.05, we retain the null hypothesis (H 0) and conclude that: “CCC, ACP, ICP, APP and WCR does not significantly affect ROE” 4.3 Discussion of Findings From the analyses of data, no significant evidence was found to reject any of the three hypotheses raised for the study, hence the acceptance of all null hypotheses. Our independent and control variables does not have significant effect on the dependent variables. Both jointly and individually, independent variables (CCC, ACP, ICP, APP and WCR) and control variable have insignificant effect for all the performance indexes used, hence cannot predict our dependent variables (EPS, ROA, and ROE). Cash conversion cycle (CCC) has negative insignificant effect on corporate performance measured as return on assets. However, the study of Justyna (2013) in polish listed IT companies found significant positive effects of working capital components (ACP, APP, ICP, CCC and LQD) on profitability which is inconsistent with the results of our study. It should be noted that the study of Justyna (2013) is the only study carried out on IT/ICT sector (based on the literatures reviewed). The difference in our finding maybe as a result of sample size, area of the study, period of the study, or the accounting system of the companies as our study is based on the post adoption period of IFRS. The results of the study though not consistent with many literature reviewed but are consistent and support the finding of, Faris and Nassem (2013), Kaveh (2015), Mohammad and Tayebeh (2014), Mahmoud et al (2015), Adeleke and Mukolu (2013), Owolabi, and Alu, (2012), Emeni and Uruakpa (2013), Solabomi and Oboh (2013), and Oladipupo and Okafor (2013) that do not support the significant effect of cash conversion cycle on corporate performance hypothesis. Account collection period (ACP) has positive insignificant effect on EPS and negative insignificant effect on ROA and ROE. Our findings confirm the study of Mohammad and Mahum (2014), Syed et al (2015), Adeleke and Mukolu (2013), Owolabi, and Alu, (2012), Solabomi and Oboh (2013), Angahar and Alematu (2014), and Kolapo et al (2015) that founds insignificant effects of account collection periods and corporate performance. Inventory conversion period (ICP) has negative insignificant effect on EPS, and insignificant positive effect on ROA and ROE as a measure of corporate performance. Consistent with are the studies of Syed et al (2015), Paul et al (2013),Adeleke and Mukolu (2013),Owolabi, and Alu, (2012), and Solabomi and Oboh (2013) that found insignificant effect of inventory conversion period on corporate performance. Account payment period (APP) has negative insignificant effect on ROA, ROE, and EPS as a measure of corporate performance. Consistent with are the studies of Sarbapriya (2012), Syed et al (2015), Adeleke and Mukolu (2013), Owolabi, and Alu, (2012), and Solabomi and Oboh (2013) that APP does not affects performance of firms. Working capital ratio has positive insignificant effect on corporate performance measured as EPS, ROA and ROE. However the study of Justyna (2013) in polish listed IT companies found significant positive effects of working capital ratio on profitability which is inconsistent with the results of our study. The result of the study is consistent and in line with the result of: Mohammad and Mahum (2014), Bagchi and Khamrui (2012),Khalaf (2012), Onodje (2014), and Oladipupo and Okafor (2013) that found no evidence to support the theoretical relationship of working capital ratio and performance. Firm’s size measured as natural logarithm of sales as a control variable was also found insignificant on all the performance indicators used in the study. V. SUMMARY OF FINDING CONCLUSION AND RECOMMENDATIONS Cash conversion cycle, account collection period, account payment period, inventory conversion period, working capital ratio and firms, size does not significantly affect return on assets, return on equity, and earnings per share as corporate performance indicators of of listed ICT firms in Nigeria. Our findings fail to support the theoretical relationship of working capital components and performance of firms. Based on the findings, we recommend that, working capital components should not be considered while making decision on the performance (EPS, ROA, and ROE) of listed ICT firms in Nigeria. ICT firms should concentrate more on management of liquidity that will not affect its solvency or bankruptcy level rather than hypothesising that their profitability is dependent on working capital components. This is because our results show that profitability is not a function of working capital components of listed ICT firms in Nigeria. www.scirj.org © 2017, Scientific Research Journal Scientific Research Journal (SCIRJ), Volume V, Issue V, May 2017 ISSN 2201-2796 50 REFERENCES [1] Adeleke, O., &Mukolu, M.O. (2013), Working capital and organization performance in Nigeria. International Journal of Business and Management Invention, 2(6), 26-35. Available @ www.ijbmi.org [2] Angahar, P. 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