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\title{Financial Slack and Firm Performance in Africa: The Mediating Effects of the Banking Sector and the Stock Market Development}
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             \author[1]{Demis Hailegebreal  Hailu}

             \affil[1]{  Dongbei University of Finance and Economics}

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\date{\small \em Received: 6 December 2017 Accepted: 31 December 2017 Published: 15 January 2018}

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\begin{abstract}
        


Organizational theory claimed favorable effect of slack on performance and the agency theory; supported an adverse influence of slack on performance. This study explored the association between the financial slack and the financial performance of firms in Africa. This study further rexamined the mediating effect of the banking sector and the stock market development on the slack-performance nexus. While the firm performance is measured as the return on assets (ROA), and return on sales (ROS), the slack components are categorized as available, potential and recoverable slacks. We used firm-level data from 457 firms in 13 African countries from 2006 to 2015. The finding of this study supported the organizational theory that favors a positive effect of slack on performance. The result of mediation analysis shows that both the banking sector and the stock market development have no strong mediating effect on the slack-performance relationship of firms in Africa. This study finally offers micro and macro level policy implications.

\end{abstract}


\keywords{Africa, Banking sector development, Stock market development, Financial slack, Firm performance, Structural Equation Modelling.}

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\section[{Introduction}]{Introduction}\par
rganizational resources and firm performance are the most significant focuses in the strategic management literature. While the firm soundness is the leading issue investigated, the slack resources has received important consideration in the field \hyperref[b13]{(Daniel et al., 2004}\hyperref[b1]{, Alessandri et al., 2014}\hyperref[b53]{, Mousa et al., 2013)}. The resource-based theory \hyperref[b58]{(Penrose, 2009)} suggested that organizational slack is considered a benefit as it can safeguard organizations at the times of environmental turmoil, declines the conflict among employees and promote innovation. Moreover previous empirical studies widely investigated the impact of financial slack on firms' performance, supported the resource-based and behavioral theories which found the positive influence of financial slack on firms' financial performance  {\ref (Shahzad et al., 2016, Bradley et}    {\ref 1997)}.However, the agency theory \hyperref[b32]{(Jensen and Meckling, 1976)} argued that organizational slack is inefficient and accrues because of poor management which is supported by several empirical studies. Consistent with the agency and pecking order theories, studies investigated the relationship between the financial slack and firm performance documented an inverse relationship between these variables \hyperref[b75]{(Voss et al., 2008}\hyperref[b35]{, Latham and Braun, 2009}\hyperref[b2]{, Altaf and Shah, 2017)}. Furthermore, other studies documented that there exists a curvilinear relationship between slack and firm performance, having an inverse U-shape suggesting too little or/ and too much slack being awkward, firms need to uphold the optimal level of slack resources \hyperref[b71]{(Tan and Peng, 2003}\hyperref[b27]{, George, 2005}\hyperref[b77]{, Wiersma, 2017b)}.\par
Though previous studies extensively examined the relationship between slack and firm performance in developed nations \hyperref[b13]{(Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b3]{, Argilés-Bosch et al., 2016}\hyperref[b77]{, Wiersma, 2017b}\hyperref[b69]{, Stan et al., 2014)}and in emerging nations such as China \hyperref[b40]{(Liu et al., 2014}\hyperref[b78]{, Yang and Chen, 2017}\hyperref[b10]{, Chen and Miller, 2007}\hyperref[b57]{, Peng et al., 2010)}and India \hyperref[b2]{(Altaf and Shah, 2017}), this has not been extensively investigated in Africa. These studies documented the direct association of financial slack and firm performance. Also, researchers seldom explored the association of the banking sector and the stock market development and firm performance \hyperref[b21]{(Fafchamps and Schündeln, 2013}\hyperref[b59]{, Pera, 2014}\hyperref[b51]{, Masoud and Hardaker, 2014)} and the link between financial slack and the banking and the stock market development (Agarwal and Mohtadi, 2004)(Etudaiye-Muhtar and Ahmad, 2014). Despite, the mediating effects of the banking sector and the stock market development on slack-performance linkage were ignored.\par
The current study, therefore, contributes to the corporate finance literature in the following ways. (1) It explored the direct slack-performance relationship of firms operating in heterogeneous business environments.  {\ref (}2) It investigated the mediating effects of the banking sector and the stock market development on the slack-performance relationship of firms in Africa using Structural Equation Modeling believing that mediation analysis facilitates a better understanding of the relationship between the financial slack and firm performance through banking sector and stock market development. 
\section[{II.}]{II.} 
\section[{The Banking and Stock Market Development in Africa}]{The Banking and Stock Market Development in Africa}\par
Generally, in Africa, the banking sector and the stock markets development has been remained low compared to developed and emerging continents. However, there still exists relative differences in these institutional developments in African countries that influence on the slack-performance nexus. For instance, \hyperref[b59]{Pera (2014)} reviewed the banking sectors in Sub-Saharan Africa and reported that, as the banking sector continues in a higher growth cycle, the need for formal financial services usually increases leading to an increase in bank assets to GDP. Likewise, \hyperref[b55]{Ngare et al. (2014)}explored the association between the stock market development and economic growth, and found a positive influence of the stock market development on investment. This result revealed that the stock market is decisive in providing external finance for investment so that firms can accumulate more slack. We computed the average banking and stock market development using bank deposit to GDP\% and stock market capitalization to GDP\% of Asia, Europe, and Africa from 2006 to 2014 to compare African banking sector and stock market development with the stated continents (see figure 1). We also used the world average bank deposit to GDP\% and Stock market capitalization to GDP\% as a baseline for comparison between the stated continents. Accordingly, the average banking sector development is found to be the highest in Europe, which is 72.228 followed by Asia, which is 56.096 and the world average is 41.931 in the last nine years. The average banking development of Africa is found to be the lowest (30.825), indicating that the banking sector development in Africa is in its infant stage. On average, Asian stock market is found to be relatively better with an average of 59.567 followed by the European stock market with an average of 55.558. Africa is still behind in stock market development compared with Asia and Europe. However, African stock market development, compared with the world's average is found to be better with the average stock market development of 47.392, indicating the stock market development in Africa is in its initial stage, but it is considered to be promising in the future.  
\section[{III. Literature Review and Hypothesis}]{III. Literature Review and Hypothesis}\par
Though \hyperref[b4]{Barnard (1968)}stated the role of slack in his work on the Functions of the Executive, the specific label of slack was not conceived until \hyperref[b49]{March and Simon (1958)}issued their influential book of 'organization 'in 1958.\par
Resources-based \hyperref[b58]{(Penrose, 2009}) and organizational theorists \hyperref[b12]{(Cyert and March, 1963}\hyperref[b60]{, Pfeffer and Salancik, 1978}\hyperref[b73]{, Thompson, 1967)}considered the firm as a unit to an organism that needs survival as the ultimate goal. The slack is defined as a cushion of actual or potential resources that enable a firm to adjust effectively to internal pressures for alteration or to external burdens for change in policy, and to pledge changes in strategy related to the external environment  {\ref (Bourgeois, 1981, Nohria and}\hyperref[b56]{Gulati, 1997)}.\par
According to \hyperref[b12]{Cyert and March (1963)}, \hyperref[b19]{Dimick and Murray (1978)}, and Geiger and Cashen, 2002), slack is defined as the resource available to the organization, which is beyond the minimum requirement to operate a required level of production. The slack resources comprise human, technological and financial slacks. However, our study considered the financial slack resources. Prior literature suggested that there are multiple components of slack \hyperref[b7]{(Bourgeois, 1981}\hyperref[b67]{, Singh, 1986}\hyperref[b25]{, Geiger and Cashen, 2002)}. These components are classified as available, recoverable and potential slacks \hyperref[b7]{(Bourgeois, 1981}\hyperref[b9]{, Bromiley, 1991}\hyperref[b13]{, Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}, absorbed and unabsorbed slack \hyperref[b70]{(Tan, 2003}  \hyperref[b7]{(Bourgeois, 1981}\hyperref[b9]{, Bromiley, 1991}\hyperref[b13]{, Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}.\par
Based on these similarities we used available, recoverable, and potential slacks, consistent with studies of \hyperref[b7]{(Bourgeois, 1981}\hyperref[b9]{, Bromiley, 1991}\hyperref[b13]{, Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}. While available slacks clarify the types of slack that are unused, but readily available \hyperref[b7]{(Bourgeois, 1981}\hyperref[b9]{, Bromiley, 1991}\hyperref[b13]{, Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}, potential slacks present the current potential to the firms to obtain resources through debt financing \hyperref[b50]{(Marlin and Geiger, 2015)} and recoverable slacks capture the extent to which organizations have embedded resources in the form of excess costs that the firm can recover it during the financially hardship times \hyperref[b7]{(Bourgeois, 1981}\hyperref[b9]{, Bromiley, 1991}\hyperref[b13]{, Daniel et al., 2004}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}.\par
Regarding its relationship with performance different components of the slack has been treated \hyperref[b56]{(Nohria and Gulati, 1997)} though, different views exist about the paybacks of slack (Geoffrey \hyperref[b26]{Love and Nohria, 2005)}. For instance, some empirical studies documented the positive relationship between slack and performance \hyperref[b74]{(Vanacker et al., 2013}\hyperref[b12]{, Cyert and March, 1963}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)} while others predicted negative nexus \hyperref[b31]{(Jensen, 1986}\hyperref[b61]{, Picolo et al., 2017}\hyperref[b64]{, Shahzad et al., 2016}\hyperref[b3]{, Argilés-Bosch et al., 2016)} and curve-linear \hyperref[b27]{(George, 2005}\hyperref[b70]{, Tan, 2003}\hyperref[b71]{, Tan and Peng, 2003}\hyperref[b34]{, Kim et al., 2008}\hyperref[b77]{, Wiersma, 2017b} {\ref , Wiersma, 2017a}\hyperref[b14]{, Danneels, 2008)}. Resources-based theorists argue that slack resources can help the organization in the externalization of opportunities \hyperref[b58]{(Penrose, 2009)}, safeguard the firms from the environmental crisis \hyperref[b73]{(Thompson, 1967)} and upsurge a firm's strategic decisions \hyperref[b42]{(Ma et al., 2012)}. Especially during the turmoil, slack allows the organization to hang in there \hyperref[b65]{(Sharfman et al., 1988)}.\par
According to organizational theory, the slack has been used to perform four functions. First, slack acts as an incentive, which indicates payment to members of the alliance more than what is required to maintain the firm \hyperref[b12]{(Cyert and March, 1963)}. Second, slack can become a resource for conflict resolution assuming that, with adequate slack, there can be a solution to every problem. Third, slack is buffer, which insulates the technique core of the firm from environmental turmoil. Fourth, slack can facilitate the strategic behavior, which enables the organization to test novel strategies like familiarizing new products and inflowing new markets \hyperref[b72]{(Thompson and Levine, 1997)}. Therefore, slack is necessary to help ensure the long-run survival of the organization. Especially during the turmoil, slack allows the organization to hang in there \hyperref[b65]{(Sharfman et al., 1988)}.\par
Despite, organizational theorists confess that 'slack resources are extra costs to the firm' and that too much slack is incapable \hyperref[b24]{(Galbraith, 2007)}. However, they generally believed that, given the complex tradeoffs, the benefits of slack offset its costs, and that a zero-slack organization is not realistic. Hence, organizational theorists suggest that, before reaching an excessive level, the slack resources have a favorable impact on the firm's performance, which is supported by the result of several prior empirical studies  {\ref (Bradley et}  By painting a different picture of the benefit of slack, agency theory turns the organizational theory viewpoint 'upside down' \hyperref[b15]{(Davis and Stout, 1992)}. Agency theory cast-offs the viewpoint of organizational theory that the organization is an organism with humanlike properties such as interest in survival. Contrarily, the firm is not an individual but it is a legal entity that serves as a focus for multiple procedures which carried out the conflicting objectives of individuals into steadiness within a framework of contractual relations \hyperref[b32]{(Jensen and Meckling, 1976)}.This viewpoint concerns the firm as a nexus of contracts among principals and agents \hyperref[b22]{(Fama, 1980)}. Agency theorists argued that maintaining slack can be good for the firm; rather, it will only be good for executives acting as agents \hyperref[b32]{(Jensen and Meckling, 1976)}. Since executives integrally have a set of goals such as the chase of power, respect, money, and job securities, that are unaligned with the interests of principals, executives may use slack to involve in excessive diversification, empire-building, and on the job shirking. Thus, slack may become a source of agency problems, that breed inefficiency also called xinefficiency which means 'the variation between the efficient behavior of business assumed by economic theory and their real behavior due to uncompetitive pressure' \hyperref[b38]{(Leibenstein, 1969)}.The view of agency theory is empirically supported by prior studies \hyperref[b71]{(Tan and Peng, 2003}\hyperref[b64]{, Shahzad et al., 2016}\hyperref[b36]{, Lee and Wu, 2016)}. Based the arguments of agency theory and the results of prior empirical studies, we proposed the following hypothesis:\par
Hypothesis 2: Financial slacks have a negative association with the firm's performance.\par
The causal relationship between financial slack and firm performance deserves more attention than recently. Some prior studies evidenced positive  {\ref (Bradley et al., 2011, Vanacker et}   \hyperref[b36]{Lee and Wu, 2016}) association between financial slack and firm performance. These studies documented the direct association of slack and firm performance. Also, researchers seldom explored the association between the banking sector and the stock market development and firm performance. The banking sector development leads firm financial performance \hyperref[b21]{(Fafchamps and Schündeln, 2013)}. SimilarlyPera (2014) found that as the banking sector develops, the need for formal financial institution rapidly increases, so does the ratio of bank assets to GDP in Sub-Saharan Africa. Moreover \hyperref[b51]{Masoud and Hardaker (2014)} concluded that both the stock market and the banking sector development enhance the firm's growth that leads to performance. The link between financial slack and banking sector and the stock market development also has been hardly explored. Etudaiye-Muhtar and Ahmad (2014), investigated the banking sector development and corporate leverage in South Africa and they found that as the banking sector develops, firms in South Africa use less debt. Likewise, \hyperref[b0]{Agarwal and Mohtadi (2004)} documented that while the banking sector favors debt financing, equity market favors equity financing in developing countries. MoreoverDemirgüç-Kunt and Levine (1996), Demirgüç-Kunt and Maksimovic (1996a) and Demirgüç-Kunt and Maksimovic (1996b) evidenced the stock market development produces a higher debt-equity ratio for firms and help entrepreneurs diversify their portfolios. Also, Demirgüç-Kunt and Maksimovic (1996b) found that a significant positive relationship between bank development and debt-equity and a negative, but insignificant relationship between stock market development and debt-equity ratio. The results of the above studies, in general, are an indication that preference of firms to use internal or external sources of funds, for financing investment projects is dependent on the institutional development which can provide external sources of finances in the form of debt and or equity. That is, in countries where the banking sector and the stock market are well-developed, firms prefer external financing. In such countries, firms are supposed to retain more internal sources of finance in the form of slack. Contrarily, in countries where the banking sector and stock market are underdeveloped, firms rely on their interior resources support their investment projects and are supposed to have little slack resources. Therefore, we proposed the following hypotheses. 
\section[{Hypothesis 3: The banking sector development has significant mediating effect on the slack-performance relationship.}]{Hypothesis 3: The banking sector development has significant mediating effect on the slack-performance relationship.}\par
Hypothesis 4: The stock market development has significant mediating effect on the slack-performance relationship.\par
IV. 
\section[{Methodology}]{Methodology}\par
Sample and Data Sources: We draw the study sample from 1,287 publicly listed firmsof33 African countries. We exclude financial companies considering that their operation is different from the non-financial firms that may lead to unique financial slack resources. We then included 457 firms and 13 countries over ten years  {\ref (2006)} {\ref (2007)} {\ref (2008)} {\ref (2009)} {\ref (2010)} {\ref (2011)} {\ref (2012)} {\ref (2013)} {\ref (2014)} {\ref (2015)}. We obtained the firm-level data from the Osiris database and the data for bank deposit to GDP\% and the stock market capitalization to GDP\% from the World Bank database. We include the banking sector and stock market development with missing values because we could not find the full years' data for some countries from the stated database. The effect of including the banking sector and the stock market development with missing values is a decrease in the observation of the study (see  \hyperref[b70]{Tan, 2003)}, ROA (Net income/Total assets) and ROS (Net income/Sales) were used as a measure of performance. The purpose of using ROA and ROS is to capture the firm's executive's effectiveness for the maximization of profits from investments in assets, and operational performance respectively. Explanatory Variables: We used available, potential, and recoverable slack resources as explanatory variables. Particularly we used two available slacks components;\par
(1) current assets/current liability (2) (current assetcurrent liabilities)/sales, three potential slack components; (1) debt/equity, (2) debt/sales and (3) debt/assets, and one recoverable slack component; (1) selling, general, and administrative expenses/sales. 
\section[{Control Variables:}]{Control Variables:}\par
We controlled the firm size since both slack and performance are dependent on firm size and firm growth (sales growth/asset growth). Also, unlike prior studies, we controlled the banking sector and stock market development using bank deposits to GDP\% and stock market capitalization to GDP\% respectively. 
\section[{Model Specification:}]{Model Specification:}\par
The following model is developed to test our hypotheses.?? ???? = ?? + ? ?? ?? ??=1 ?????????? ???? + ? ?? ? ? =1 ?????????????? ???? + ?? ?? + ?? ?? + µ ??\par
Where ?? ?????? is performance proxies(ROA, and ROS), i is firm, j is country, ?????????? ???? is a vector of slack components, ?????????????? ???? is a vector of control variables, ?? ?? and ?? ?? are coefficients of explanatory and control variables respectively and ?? ?? is error term, ?? ?? is industry effect and µ ?? is country effect.\par
Estimation Approach: Our data set is a panel, constituting 457 firms and 13 countries over ten years. Therefore, we employed the Hausman fixed-random specification test that suggests a fixed effect model with a Chi-square of 106.21 (p-value=0.000) is appropriate for our dataset.\par
We developed the hypothesis used to prove the mediating effects of the banking sector development (M 1 ) and the stock market development (M 2 ) on the slack (X) and performance (Y) linkage. The empirical testing of mediation effects involves two broad categories (1) the Baron and Kenny's approach, also known as segmentation (implicit) approach and (2) the transmittal (explicit) approach. Based on segmentation approach, three hypotheses are required: H1) independent variable (X) affects the mediator (M), H2) mediator (M) affects the dependent (Y) and H3) the mediator mediates the nexus of the independent (X)and the independent (Y) variables. Contrarily, the transmittal approach, also called Sobel test \hyperref[b68]{(Sobel, 1982)}  
\section[{Result and Discussion}]{Result and Discussion} 
\section[{Slack-performance nexus}]{Slack-performance nexus}\par
The first purpose of this study is to examine the relationship between financial slack and firm performance of firms in Africa. We then tested our hypotheses by estimating the model using the fixed effect (within) robust estimation approach. The Rsquare within (0.98 when we use ROS and 0.56 when we use ROA) shows that slack resources along with  
\section[{B}]{B}\par
Table \hyperref[tab_5]{1} reports the mean value, the correlation and the Variance Inflation Factor (VIF). We found that the ROS and ROA of firms, on average, is 5.92 and 0.04 suggesting the operational performance (ROS) of firms in Africa is higher than its return on assets (ROA). Firms in Africa reported, on average, the current ratio and working capital of 4.55 and 75.41 respectively,\par
Where WC is Working capital, Debt-Equity is the ratio of debt to Equity, Debt-Sales is the ratio of Debt to Sales, Debt-Asset is the ratio of Debt to total assets, Op.exp is the ratio of operating expense to sales, size is the natural logarithm of total assets, growth denotes firm growth, Bank denotes banking sector development is the Variance Inflation Factor and 1/VIF is tolerance Market is Stock market development, VIF a)\par
other control variables strongly explained the operational performance than the performance of investment of assets of firms in Africa (ROA). The F-test of the models are significant with a p-value of 0.00, and we believed that the empirical models used in this study as a whole are correctly specified. We first estimated the model (column 1) without controlling the control variables, industry, and country dummies and then, we run the model (column 2) controlling the control variables, industry, and country dummies and we found the same result.\par
Available slack and firm performance: We found a positive association between available slack and ROA that supported the organizational theory. There exists a mixed relationship between available slack and ROS. The ROS is positively (significant) and negatively (insignificant) associated with the working capital to sales and the current ratios. 
\section[{Potential slack and firm performance:}]{Potential slack and firm performance:}\par
We also found a mixed correlation between these variables. The main effect of debt/sales and debt/assets in the fixed regression result was negative and significant, indicating a positive relationship between potential slack and firm's ROA. This result revealed that the fewer the ratios of debt to sales and debt to assets, the better the ROA. To the contrary, we found a significant negative effect of debt/sales, debt/assets, and debt/equity, indicating there exists a positive and significant association of potential slack and operational performance (ROS) of firms in Africa. 
\section[{Recoverable slack and firm performance:}]{Recoverable slack and firm performance:}\par
We finally found that recoverable slack is found to have a positive, but not significant and a positive and significant association with ROA and ROS respectively. The results of this study confirmed the first but not the second hypothesis, suggesting that the organizational theory is essential to explain the slack-performance nexus of firms in Africa.\par
Our study also found evidence in the relationship between control variables and firm performance. Firm size has a positive relationship with ROA and ROS whereas growth has a negative association with ROA and ROS. The relationship between firms' growth and ROS has found to be statistically significant. Our study contributes to the corporate finance literature by providing insight on the slack-performance nexus by controlling institutional developments which hardly controlled by previous studies. The banking sector and the stock market development have been found to have a positive association with firms' ROA. Firms' ROS is found to have a negative association with banking sector development and positive association with stock market development.\par
The result of this study shows that different types of slack have different influence on the different performance of firms. That is there exists a positive and negative correlation between slacks and performances. This result is consistent with prior studies \hyperref[b6]{(Bourgeois III and} {\ref Singh, 1983, Marlin and}\hyperref[b50]{Geiger, 2015)}. Furthermore, \hyperref[b13]{Daniel et al. (2004)}found varying slack-performance nexus using different measures of slack and performance.\par
The findings of this study support the organizational theory which is in favor of a positive influences of slack resources on the firms' financial performance. Our study found a positive association between financial slack resources and firms' financial performance in Africa. Particularly, the relationship between available slack and recoverable slack with the firm ROA, and recoverable slack and the ROS of firms provide strong support of the resource-based view of the firm that the availability of financial slack boosts the firms' endeavor to perform well. In other words, slack resources are necessarily helping ensure the long-run survival of firms. The positive relationship between financial slack and firm performance is also an indication that slack resources are an incentive required to boost performance, and is also used for conflict resolution, buffer, and facilitates strategic behavior that enables African firms to test with new strategies such as introducing new products and entering new markets. This study also supported the resource-based view in that managers should look inside the firm to find the resources of competitive advantage, so that resources are helping companies achieve higher organizational performance. This result is also consistent with the empirical studies \hyperref[b74]{(Vanacker et al., 2013}\hyperref[b12]{, Cyert and March, 1963}\hyperref[b8]{, Bradley et al., 2011}\hyperref[b50]{, Marlin and Geiger, 2015)}.The negative impact of financial slack has been observed on the operational performance (ROS), indicating there exists a positive association between potential slack components and ROS of African firms. This result evident that the existence of such slack enhances the firms endeavor to perform well because it helps firms secure resources through debt financing. 
\section[{b) Mediation analysis (Structural Equation Modeling)}]{b) Mediation analysis (Structural Equation Modeling)}\par
The main contribution of this study is to examine the mediating effects of the banking sector and the stock market developments on the association of financial slack and firm's performance. Growing literature currently advocates the use of bootstrapping for evaluating indirect effects \hyperref[b41]{(Lockwood and MacKinnon, 1998}\hyperref[b48]{, MacKinnon et al., 2004}\hyperref[b66]{, Shrout and Bolger, 2002}\hyperref[b62]{, Preacher and Hayes, 2004)}. Boot strapping is one of several re-sampling strategies for mediation estimation and hypothesis testing. Any statistics can use bootstrapping, but this study focuses on the mediation, its standard error and significance level of paths. Thus, we estimated a bootstrapped standard errors and path coefficients with Replications 1000. Before we run the mediation, we tested the goodness of fit of the model using a two-index presentation strategy, as suggested by \hyperref[b29]{Hu and Bentler (1999)}. The SRMR less than 0.08 and the RMSEA less than 0.06 shows the goodness of fit of the model. As can be seen from Table \hyperref[tab_7]{3} the value of SRMR and RMSEA is 0.000 indicating the model fits well. Table \hyperref[tab_7]{3} shows the estimate of the path observed coefficients, bootstrap standard error, and P-values. We used the banking sector development (M1) and the stock market development (M2) in the model as the mediators on the relationship between the slack and financial performance of firms in Africa. The mediating effects of the banking sector and the stock market development on the relationship between slack and performance is presented in Table \hyperref[tab_7]{3}.\par
The evidence of mediation exists when the relationship between the dependent and independent variables is partially or fully influenced by the mediating variable \hyperref[b41]{(Lockwood and MacKinnon, 1998)}. That is, mediation exists if the coefficient of a direct path between the independent variable (financial slacks) and the dependent variable (firm performance) is smaller when we included the mediator in the model. Moreover, full mediation exists if the indirect path (a 1 b 1 and a 2 b 2 ) is not significant, but the direct path (c?) is statistically significant. Partial mediation exists if the direct (c?), indirect (a 1 b 1 and a 2 b 2 ) and total (c?+ a 1 b 1 and c?+a 2 b 2 ) paths are statistically significant. Strong mediation also exists with significant indirect and insignificant direct effects. The mediating be tested using the coefficient of the indirect path and the p-value. The higher the coefficient and the lower p-value (p<0.05),the strong the mediation effect. However, there might be mediation with the small coefficient of indirect paths if the p-value is lower (p<0.05).Moreover, the presence of mediating effects can be detected by using the confidence interval of the indirect effects, that is, if the confidence interval of indirect effect contains non-zero, it shows there exists mediation. Based on these arguments we reported the result of bootstrapping as follows.\par
The coefficient of the indirect effect of the ratio of debt to assets (path a 1 b 1 ) on ROA via the banking sector development, holding the stock market development constant, is -0.00003 and is significant at the 5\% level of significance. The direct and total effects of the ratio of debt to assets via the banking sector development, holding the stock market development constant, are the same (-0.061) and statistically significant (p=0.04). This result shows that the banking sector development partially mediates the relationship between the ratio of debt to assets and return on assets (ROA) of firms in Africa. More specifically, the ratio of the debt to asset has indirect effect on ROA, that is, a unit increase in the ratio of debt to assets will reduce the ROA by 0.061 units via the banking sector development.\par
The estimates of the direct, indirect and total effects of the current ratio, working capital, and debt to equity, debt to sales and operating expense to sales doesn't confirm any evidence of the mediating effect of the banking sector development on the relationship between the financial slack and the ROA of firms in Africa. For clarity, the coefficients (indirect effects) of the current ratio, working capital, debt to equity, debt to sales, and operating expense to sales are very small (close to zero), and the p-values are too high (nonsignificant). Table \hyperref[tab_7]{3} also presents the SEM analysis, using bootstrapping, of path a 2 b 2 by introducing the stock market development into the model as a mediator, holding the banking sector development constant. The coefficients of indirect effects (a 2 b 2 ) of all slack variables are significantly small or close to zero with a non significant p-values(p>0.05) indicating that the relationship between slack and performance of firms in Africa is not strongly mediated by the stock market development. Moreover, the confidence intervals of the indirect effects of slack variables, through the banking sector and the stock market development, on ROA and ROS are close to zero (see the appendix) suggesting there exists a week mediating role of the banking sector and the stock market development on the relationship between slack components and firm performance.\par
The direct and the total effects of the ratio of operating expense to sales, holding the banking sector constant, on ROS found to be the same and the highest (3.135). However, when we introduce the banking sector development in the model as a mediator, the indirect effect of operating expense to sales become very low (0.000053) and non-significant. The coefficients of the indirect effects of other slack variables are found to be close to zero with the higher p-values (P>0.05) suggesting that the banking sector development is not mediating the slack-performance nexus of firms in Africa. Similarly, introducing the stock market development in the model as a mediator, we estimate the indirect effects of financial slack components on the firm's operational performance (ROS). However, we found that the coefficients are too small (almost close to zero) and are an indication that slack-performance relationship is not strongly mediated by the stock market development. However, the result of our study doesn't confirm the third and the fourth hypotheses of the study. That is, both the banking sector and the stock market development have no strong mediating effect on the relationship of slack and performance of firms in Africa. Thus, the third and fourth hypotheses of our study are not confirmed in this study. The pecking order theory suggested that in the presence of asymmetric information in the credit market, firms tend to use their internal sources to support their investment projects. This study found that the preference of firms to use internal sources of finance, for financing investment projects is dependent on the institutional development which can provide external sources of finances. For instance, in countries where the banking sector and stock market are well-developed, firms can easily have external sources of finance and can support their investment with it and can have more financial slack in the form of more retained earnings. But this is not true for firms in Africa because it is due to the immature banking sector and the stock market as we discussed in section 3 of this study.\par
We also found inconsistent mediation that possibly happened when the sign of the direct (c?) and the indirect effects (a 1 b 1 or a 2 b 2 ) are opposite \hyperref[b45]{(MacKinnon et al., 2007)}. The direct path of the debt to sales and debt to assets on ROA, through stock market development, is negative. Contrarily, the indirect effects of the same variables with the same mediator on the ROA is positive. We also found the opposite signs of other paths (see Table \hyperref[tab_7]{3}). Inconsistent mediation might be happening when the first step of Baron and Kenny (the dependent variable has a significant relationship with the independent variable) would not be met. However, it has been argued that this step is not the necessary condition of mediation \hyperref[b33]{Kenny et al. (1998)} because it is not part of the mediation.  
\section[{VII. Conclusion and Implication}]{VII. Conclusion and Implication}\par
While firm-level data is obtained from the OSIRIS database, the institutional development indexes data is extracted from World Bank. Based on the data availability, the study period covers ten years, from 2006 to 2015.Accordingly, 457 non-financial publicly quoted companies with ten years firm-level data from 2006 to 2015 of 13 African countries were included in the study. We used firm performance as dependent and slack as explanatory variables. While firm performance is measured by ROA and ROS, financial slack are categorized as available, potential, and recoverable slack, and firm size, firm growth, banking sector, and stock market development as control variables. We employed the Hausman specification test that suggested the fixed effect model is the super choice over the random effect model. Thus, we run a fixed effect (within) robust regression, (1) without controlling the control variables, the country, and the industry effect and (2) with controlling control variables, country, and industry effects. Furthermore, we tested the mediation effect of the banking sector and the stock market development on the slack-performance nexus using Structural Equation Modeling (SEM) through bootstrapping.\par
The result of this study supported the organizational theory which favors positive slackperformance relationship. The first hypothesis of this study assumed a positive correlation between financial slack and firms' financial performance. The result of our study doesn't confirm this hypothesis because the association is mixed. We found no strong association between available slack and ROA. Potential slack is found to have a strong relationship with ROA and ROS. More specifically, the ratio of debt to equity has a strong relationship with the ROA, and the ROS. We also found that the ratio of debt to assets has a strong relationship with the ROA and the ROS. Debt to sales has statistically strong and negative correlation with operational performance (ROS) of firms. Recoverable slack is found to have statistically strong and positive relationship with ROS and has no strong relationship with ROA. The mediation analysis using Structural Equation Modeling revealed that both the banking sector and the stock market developments have no strong mediating effects on the financial slack and firm performance of firms in Africa.\par
Concerning the relationship between financial slack and firm performance, our finding implied that this result might not behold true in a universal context, but it is definitely dependent upon the environment in which the firms are embedded. Thus, our study offers some managerial implication for policy formulations. First, given the result that slack resources have a positive association with firms' operational performance (ROS) and financial performance (ROA), it is indicative that African firms need to strengthen the monitoring mechanism such as corporate governance to boost the commitments of those resources. The effective corporate governance mechanism help firms enhance the optimum use of financial resources to enhance performance. We found no evidence of mediation effect of the institutional development on the slackperformance nexus of firms in Africa. We further compared theses institutional developments (banking sector and the stock market of Africa) with of Asia, Europe, and World average. Both the stock market and the banking sector development of Africa lags behind the Asia's, Europe, and even world average. Hence, it is imperative to improve the institutional development in Africa. A well developed banking sector and stock market helps secure the external financing(debt and equity) so that firms will have sufficient internal sources of finance (slack) in the form of retaining earnings which can support both the financial and operational performance of firms in Africa.   \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.11805555555555555\textwidth}P{0.2361111111111111\textwidth}P{0.11805555555555555\textwidth}P{0.14166666666666666\textwidth}P{0.07083333333333333\textwidth}P{0.16527777777777777\textwidth}}
2003,\tabcellsep Bourgeois,\tabcellsep 1981,\tabcellsep Nohria\tabcellsep and\tabcellsep Gulati,\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.4940625\textwidth}P{0.15583333333333332\textwidth}P{0.031875\textwidth}P{0.035416666666666666\textwidth}P{0.019479166666666665\textwidth}P{0.010625\textwidth}P{0.10270833333333333\textwidth}}
\multicolumn{4}{l}{approaches used to categorize slack are similar in that}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{slack resources are either available and unabsorbed or}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{already absorbed and recoverable, are considered}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{internal slack, when slacks are not currently in the}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{organization are taken as external, potential, and}\tabcellsep \tabcellsep \tabcellsep \\
unabsorbed\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{6}{l}{Avarage Bank deposit to GDP\% and Stock market capitalization to GDP\% (2006-2014)}\\
\tabcellsep \tabcellsep 72.228\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
56.096\tabcellsep 59.567\tabcellsep 55.558\tabcellsep 30.8257 47.392\tabcellsep 41.931\tabcellsep 39.493\tabcellsep Bank deposit to GDP\% Stock market capitalization to GDP\%\\
\multicolumn{2}{l}{ASIA}\tabcellsep EUROPE\tabcellsep AFRICA\tabcellsep \multicolumn{2}{l}{WORLD}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_4}Table 2 )}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.01566820276497696\textwidth}P{0.05581797235023041\textwidth}P{0.0048963133640553\textwidth}P{0.00195852534562212\textwidth}P{0.00195852534562212\textwidth}P{0.6756912442396313\textwidth}P{0.09400921658986175\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2018\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 35\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Following this argument, Hu and Bentler (1999) suggested a two-index presentation strategy, namely Tucker-Lewis Index (TLI) \& Standardized Root Mean Square Residual (SRMR), Root Mean Square Error of Approximation (RMSEA) \& Standardized Root Mean\tabcellsep Volume XVIII Issue VII Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Square Residual (SRMR) and Comparative Fit Index (CFI) \& Standardized root mean square residual\tabcellsep ( )\\
?4 ?5 ?6 ?7 ?8\tabcellsep cr wcsale debtequity debtsales debtassets\tabcellsep slack\tabcellsep a1\tabcellsep a2\tabcellsep (SRMR). We, thus, used combinations of RMSEA and SRMR. We choose these indices because they are more sensitive to sample size, model misspecification and parameter estimates and the values of SRMR less than 0.08, and RMSEA less than 0.06 indicate a good fit of the model. perf ?1 roa ?2 ros ?3 ?10 ?11 bdgdp stkmtgdp b1 b2 c'\tabcellsep Global Journal of Management and Business Research\\
?9\tabcellsep operatingexpsale\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2018 Global Journals\tabcellsep \end{longtable} \par
  {\small\itshape [Note: B Figure 2: Path diagram of the mediation model suggesting]} 
\caption{\label{tab_5}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.1329465186680121\textwidth}P{0.14409687184661957\textwidth}P{0.0025731584258324924\textwidth}P{0.1372351160443996\textwidth}P{0.21700302724520684\textwidth}P{0.0025731584258324924\textwidth}P{0.21357214934409685\textwidth}}
Variables\tabcellsep ROA\tabcellsep (1)\tabcellsep ROS\tabcellsep ROA\tabcellsep (2)\tabcellsep ROS\\
CR\tabcellsep 4.04E-05** (4.79E-05)\tabcellsep \tabcellsep -0.0004*** (0.0009)\tabcellsep 4.06E-05** (4.82E-05)\tabcellsep \tabcellsep -0.00051*** (0.00093)\\
WC\tabcellsep 9.13E-07*** (1.47E-06)\tabcellsep \tabcellsep 0.076253* (0.00137)\tabcellsep 8.19E-07*** (2.13E-06)\tabcellsep \tabcellsep 0.077111* (0.00123)\\
Debt-Equity\tabcellsep 7.87E-06* (4.14E-06)\tabcellsep \tabcellsep -0.00056* (7.45E-05)\tabcellsep 1.09E-05* (5.92E-06)\tabcellsep \tabcellsep -0.00108* (0.000222)\\
Debt-Sales\tabcellsep -1.7E-05** (1.61E-05)\tabcellsep \tabcellsep -0.03826* (0.01576)\tabcellsep -2E-05** (2.25E-05)\tabcellsep \tabcellsep -0.04762* (0.014345)\\
Debt-Assets\tabcellsep -0.06424* (0.017369)\tabcellsep \tabcellsep -0.04207* (0.01507)\tabcellsep -0.06395* (0.017579)\tabcellsep \tabcellsep -0.01004** (0.013952)\\
Op.exp\tabcellsep 0.000225*** (0.00053)\tabcellsep \tabcellsep 3.044028* (0.39762)\tabcellsep 7.46E-05*** (0.00071)\tabcellsep \tabcellsep 3.303782* (0.351602)\\
size\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.097149** (0.096619)\tabcellsep \tabcellsep 2.398616** (1.8975)\\
Growth\tabcellsep \tabcellsep \tabcellsep \tabcellsep -4.80E-06** (9.81E-06)\tabcellsep \tabcellsep -0.018* (0.005268)\\
Bank\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.001359** (0.000887)\tabcellsep \tabcellsep -0.24363** (0.163697)\\
Market\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.000447** (0.00033)\tabcellsep \tabcellsep 0.096777** (0.069304)\\
\textunderscore cons\tabcellsep 0.090505* (0.013629)\tabcellsep \tabcellsep 0.803843* (0.15741)\tabcellsep -0.47087** (0.610076)\tabcellsep \tabcellsep -7.82082** (10.08582)\\
Number of obs =\tabcellsep 4570\tabcellsep \tabcellsep 4570\tabcellsep 4301\tabcellsep \tabcellsep 4301\\
R-sq: within\tabcellsep 0.5535\tabcellsep \tabcellsep 0.9835\tabcellsep 0.5590\tabcellsep \tabcellsep 0.9847\\
F-test\tabcellsep 21.08*\tabcellsep \tabcellsep 33058.51*\tabcellsep 11.95*\tabcellsep \tabcellsep 58783.04*\\
Country dummies\tabcellsep No\tabcellsep \tabcellsep No\tabcellsep Yes\tabcellsep \tabcellsep Yes\\
Industry dummies\tabcellsep No\tabcellsep \tabcellsep No\tabcellsep Yes\tabcellsep \tabcellsep Yes\end{longtable} \par
 
\caption{\label{tab_6}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.06582491582491581\textwidth}P{0.13212682379349044\textwidth}P{0.138327721661055\textwidth}P{0.13641975308641974\textwidth}P{0.12783389450056118\textwidth}P{0.12640291806958473\textwidth}P{0.12306397306397307\textwidth}}
Variables\tabcellsep \tabcellsep ROA <-\tabcellsep \tabcellsep ROS <-\tabcellsep \tabcellsep \\
\tabcellsep Direct effect\tabcellsep Indirect effect\tabcellsep Total effect\tabcellsep Direct effect\tabcellsep Indirect\tabcellsep Total effect\\
\tabcellsep (c?)\tabcellsep (a 1 b 1 )\tabcellsep (c?+a 1 b 1 )\tabcellsep (c?)\tabcellsep effect (a 1 b 1 )\tabcellsep (c?+a 1 b 1 )\\
Bank (M1)\tabcellsep 0.0004* (0.0002)\tabcellsep \tabcellsep 0.00039* (0.0002)\tabcellsep -0.003 (0.0074)\tabcellsep \tabcellsep -0.003 (0.007)\\
CR\tabcellsep 0000282 (0.003)\tabcellsep 0.0000015 (0.00002)\tabcellsep 0.0000296 (0.003)\tabcellsep 0.0002 (0.051)\tabcellsep -0.0000126 (0.0002)\tabcellsep 0.00019 (0.051)\\
WC\tabcellsep 0.0000005 (0.00001)\tabcellsep -0.00000002 (0.0000005)\tabcellsep 0.0000005 (0.000001)\tabcellsep 0.077* (0.037)\tabcellsep 0.0000002 (0.000004)\tabcellsep 0.077403 (0.037)\\
Debt-\tabcellsep 0000203\tabcellsep 0.0000002\tabcellsep 0.0000205\tabcellsep 0.0008\tabcellsep -0.0000014\tabcellsep 0.0008\\
Equity\tabcellsep (0.0002)\tabcellsep (0.000005)\tabcellsep (0.00002)\tabcellsep (0.0029)\tabcellsep (0.00006)\tabcellsep (0.003)\\
Debt-Sales\tabcellsep -0.0000112 (0.00003)\tabcellsep 0.0000004 (0.0000005)\tabcellsep -0.000011 (-0.00003)\tabcellsep -0.036** (0.0305)\tabcellsep -0.0000034 (0.00001)\tabcellsep -0.036 (0.0305)\\
Debt-\tabcellsep -0.061*\tabcellsep -0.00003**\tabcellsep -0.061*\tabcellsep -0.048**\tabcellsep 0.0002\tabcellsep -0.048\\
Assets\tabcellsep (0.03)\tabcellsep (0.000035)\tabcellsep (-0.0303)\tabcellsep (0.07)\tabcellsep (0.0006)\tabcellsep (0.07)\\
Op.exp\tabcellsep 0.0004 (0.002)\tabcellsep 0.000006 (0.000034)\tabcellsep 0.00042 (0.0025)\tabcellsep 3.135** (2.49)\tabcellsep -0.000053*** (0.0003)\tabcellsep 3.1352*** (2.492)\\
\tabcellsep Direct effect\tabcellsep Indirect effect\tabcellsep Total effect\tabcellsep Direct effect\tabcellsep Indirect\tabcellsep Total effect\\
\tabcellsep (c?)\tabcellsep (a 2 b 2 )\tabcellsep (c?+a 2 b 2 )\tabcellsep (c?)\tabcellsep effect (a 2 b 2 )\tabcellsep (c?+a 2 b 2 )\\
Market\tabcellsep 0.0000034\tabcellsep \tabcellsep 0.000003\tabcellsep 0.0032\tabcellsep \tabcellsep 0.0032\\
(M2)\tabcellsep (0.0001)\tabcellsep \tabcellsep (0.00014)\tabcellsep (0.0076)\tabcellsep \tabcellsep (0.0076)\\
CR\tabcellsep 0.00003 (0.003)\tabcellsep 0.000000012 (0.000001)\tabcellsep 0.00003 (0.0026)\tabcellsep 0.0002 (0.04)\tabcellsep 0.00001 (0.0012)\tabcellsep 0.00019 (0.04)\\
WC\tabcellsep 0.0000005 (0.00001)\tabcellsep 0.000000001 4\tabcellsep 0.0000005 (0.00001)\tabcellsep 0.077402* (0.0399)\tabcellsep 0.00000135* ** (0.00003)\tabcellsep 0.0774032 (0.0399)\\
Debt-\tabcellsep 0.00002\tabcellsep 0.000000001\tabcellsep 0.00002\tabcellsep 0.0008\tabcellsep 0.000038\tabcellsep 0.00086\\
Equity\tabcellsep (0.0004)\tabcellsep (0.000002)\tabcellsep (0.0004)\tabcellsep (0.0051)\tabcellsep (0.0003)\tabcellsep (0.0053)\\
Debt-Sales\tabcellsep -0.000011 (0.00003)\tabcellsep 0.00000002 (0.00004)\tabcellsep -0.000011 (0.00003)\tabcellsep -0.036** (0.0304)\tabcellsep 0.00002 (0.00005)\tabcellsep -0.037** (0.0304)\\
Debt-\tabcellsep -0.06*\tabcellsep 0.000001\tabcellsep -0.06\tabcellsep -0.049***\tabcellsep 0.00096\tabcellsep -0.048\\
Assets\tabcellsep (0.0309)\tabcellsep (0.00004)\tabcellsep (0.031)\tabcellsep (0.082)\tabcellsep (0.0055)\tabcellsep (0.081)\\
Op.exp\tabcellsep 0.0004 (0.003)\tabcellsep 0.00000108 (0.00004)\tabcellsep 0.0004 (0.003)\tabcellsep 3.134** (2.4032)\tabcellsep 0.001 (0.0031)\tabcellsep 3.135** (2.403)\\
Obs.\tabcellsep 4556\tabcellsep \tabcellsep \tabcellsep 4301\tabcellsep \tabcellsep \\
Replication s\tabcellsep 1000\tabcellsep \tabcellsep \tabcellsep 1000\tabcellsep \tabcellsep \\
RMSEA\tabcellsep 0.000\tabcellsep \tabcellsep \tabcellsep 0.000\tabcellsep \tabcellsep \\
SRMR\tabcellsep 0.000\tabcellsep \tabcellsep \tabcellsep 0.000\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.15003355704697985\textwidth}P{0.1762751677852349\textwidth}P{0.10895973154362415\textwidth}P{0.06275167785234899\textwidth}P{0.0753020134228188\textwidth}P{0.15630872483221475\textwidth}P{0.12036912751677853\textwidth}}
\tabcellsep \tabcellsep Bootstrap\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep Std. Err.\tabcellsep z\tabcellsep P>z\tabcellsep \multicolumn{2}{l}{[95\%Conf. Interval]}\\
structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0037208\tabcellsep 0.050512\tabcellsep 0.07\tabcellsep 0.941\tabcellsep -0.095281\tabcellsep 0.1027227\\
WC\tabcellsep \multicolumn{2}{l}{-0.0000599 0.001314}\tabcellsep -0.05\tabcellsep 0.964\tabcellsep -0.002636\tabcellsep 0.002516\\
Debt-Equity\tabcellsep 0.0004054\tabcellsep 0.01201\tabcellsep 0.03\tabcellsep 0.973\tabcellsep -0.023133\tabcellsep 0.0239441\\
Debt-Sales\tabcellsep 0.0010033\tabcellsep 0.001387\tabcellsep 0.72\tabcellsep 0.469\tabcellsep -0.001715\tabcellsep 0.0037216\\
Debt-Assets\tabcellsep -0.073036\tabcellsep 0.082309\tabcellsep -0.89\tabcellsep 0.375\tabcellsep -0.234359\tabcellsep 0.0882874\\
Op.exp\tabcellsep 0.0155414\tabcellsep 0.08669\tabcellsep 0.18\tabcellsep 0.858\tabcellsep -0.154367\tabcellsep 0.1854502\\
ROA<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep 0.0003944\tabcellsep 0.000176\tabcellsep 2.24\tabcellsep 0.025\tabcellsep \multicolumn{2}{l}{0.0000491 0.0007397}\\
CR\tabcellsep 0.0000282\tabcellsep 0.002593\tabcellsep 0.01\tabcellsep 0.991\tabcellsep -0.005054\tabcellsep 0.0051102\\
WC\tabcellsep 5.15E-07\tabcellsep 1.15E-05\tabcellsep 0.04\tabcellsep 0.964\tabcellsep -2.21E-05\tabcellsep 0.0000231\\
Debt-Equity\tabcellsep 0.0000203\tabcellsep 0.000228\tabcellsep 0.09\tabcellsep 0.929\tabcellsep -0.000426\tabcellsep 0.0004669\\
Debt-Sales\tabcellsep \multicolumn{2}{l}{-0.0000112 3.36E-05}\tabcellsep -0.33\tabcellsep 0.739\tabcellsep -7.71E-05\tabcellsep 0.0000547\\
Debt-Assets\tabcellsep \multicolumn{2}{l}{-0.0606111 0.030319}\tabcellsep -2\tabcellsep 0.046\tabcellsep -0.120036\tabcellsep -0.001186\\
Op.exp\tabcellsep 0.0004159\tabcellsep 0.002461\tabcellsep 0.17\tabcellsep 0.866\tabcellsep -0.004407\tabcellsep 0.0052391\\
\tabcellsep \multicolumn{2}{l}{Indirect effects}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
ROA<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep 0\tabcellsep (no path)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 1.47E-06\tabcellsep 1.98E-05\tabcellsep 0.07\tabcellsep 0.941\tabcellsep -3.73E-05\tabcellsep 0.0000402\\
WC\tabcellsep -2.36E-08\tabcellsep 5.18E-07\tabcellsep -0.05\tabcellsep 0.964\tabcellsep -1.04E-06\tabcellsep 9.92E-07\\
Debt-Equity\tabcellsep 1.60E-07\tabcellsep 4.74E-06\tabcellsep 0.03\tabcellsep 0.973\tabcellsep -9.13E-06\tabcellsep 9.45E-06\\
Debt-Sales\tabcellsep 3.96E-07\tabcellsep 5.77E-07\tabcellsep 0.69\tabcellsep 0.493\tabcellsep -7.36E-07\tabcellsep 1.53E-06\\
Debt-Assets\tabcellsep \multicolumn{2}{l}{-0.0000288 3.49E-05}\tabcellsep -0.82\tabcellsep 0.41\tabcellsep -9.73E-05\tabcellsep 0.0000397\\
Op.exp\tabcellsep 6.13E-06\tabcellsep 3.42E-05\tabcellsep 0.18\tabcellsep 0.858\tabcellsep -6.08E-05\tabcellsep 0.0000731\\
\tabcellsep Total effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 ) <-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0037208\tabcellsep 0.050512\tabcellsep 0.07\tabcellsep 0.941\tabcellsep -0.095281\tabcellsep 0.1027227\\
WC\tabcellsep \multicolumn{2}{l}{-0.0000599 0.001314}\tabcellsep -0.05\tabcellsep 0.964\tabcellsep -0.002636\tabcellsep 0.002516\\
Debt-Equity\tabcellsep 0.0004054\tabcellsep 0.01201\tabcellsep 0.03\tabcellsep 0.973\tabcellsep -0.023133\tabcellsep 0.0239441\\
Debt-Sales\tabcellsep 0.0010033\tabcellsep 0.001387\tabcellsep 0.72\tabcellsep 0.469\tabcellsep -0.001715\tabcellsep 0.0037216\\
Debt-Assets\tabcellsep -0.073036\tabcellsep 0.082309\tabcellsep -0.89\tabcellsep 0.375\tabcellsep -0.234359\tabcellsep 0.0882874\\
Op.exp\tabcellsep 0.0155414\tabcellsep 0.08669\tabcellsep 0.18\tabcellsep 0.858\tabcellsep -0.154367\tabcellsep 0.1854502\\
ROA<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep 0.0003944\tabcellsep 0.000176\tabcellsep 2.24\tabcellsep 0.025\tabcellsep \multicolumn{2}{l}{0.0000491 0.0007397}\end{longtable} \par
 
\caption{\label{tab_8}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.14112506826870563\textwidth}P{0.16340797378481703\textwidth}P{0.13137629710540688\textwidth}P{0.060813762971054064\textwidth}P{0.0812397596941562\textwidth}P{0.1383397050791917\textwidth}P{0.13369743309666848\textwidth}}
\tabcellsep Direct effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Normal-based}\tabcellsep \\
\tabcellsep Observed\tabcellsep Bootstrap\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep z\tabcellsep P>z\tabcellsep {}[95\%Conf.\tabcellsep Interval\\
Structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0037208\tabcellsep 0.053181\tabcellsep 0.07\tabcellsep 0.944\tabcellsep -0.100512\tabcellsep 0.1079533\\
WC\tabcellsep -0.0000599\tabcellsep 0.001241\tabcellsep -0.05\tabcellsep 0.962\tabcellsep -0.002493\tabcellsep 0.002373\\
Debt-Equity\tabcellsep 0.0004054\tabcellsep 0.016852\tabcellsep 0.02\tabcellsep 0.981\tabcellsep -0.032625\tabcellsep 0.0334354\\
Debt-Sales\tabcellsep 0.0010033\tabcellsep 0.001615\tabcellsep 0.62\tabcellsep 0.534\tabcellsep -0.002162\tabcellsep 0.0041681\\
Debt-Assets\tabcellsep -0.073036\tabcellsep 0.078848\tabcellsep -0.93\tabcellsep 0.354\tabcellsep -0.227576\tabcellsep 0.0815037\\
Op.exp\tabcellsep 0.0155414\tabcellsep 0.093513\tabcellsep 0.17\tabcellsep 0.868\tabcellsep -0.167742\tabcellsep 0.1988243\\
ROS<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep -0.0033846\tabcellsep 0.007374\tabcellsep -0.46\tabcellsep 0.646\tabcellsep -0.017838\tabcellsep 0.0110686\\
CR\tabcellsep 0.0002059\tabcellsep 0.050865\tabcellsep 0\tabcellsep 0.997\tabcellsep -0.099487\tabcellsep 0.0998992\\
WC\tabcellsep 0.0774029\tabcellsep 0.036945\tabcellsep 2.1\tabcellsep 0.036\tabcellsep 0.0049919\tabcellsep 0.1498139\\
Debt-Equity\tabcellsep 0.0008275\tabcellsep 0.002867\tabcellsep 0.29\tabcellsep 0.773\tabcellsep -0.004792\tabcellsep 0.0064467\\
Debt-Sales\tabcellsep -0.0366469\tabcellsep 0.030454\tabcellsep -1.2\tabcellsep 0.229\tabcellsep -0.096336\tabcellsep 0.0230417\\
Debt-Assets\tabcellsep -0.0488627\tabcellsep 0.069623\tabcellsep -0.7\tabcellsep 0.483\tabcellsep -0.185322\tabcellsep 0.0875964\\
Op.exp\tabcellsep 3.135279\tabcellsep 2.491808\tabcellsep 1.26\tabcellsep 0.208\tabcellsep -1.748575\tabcellsep 8.019134\\
\tabcellsep Indirect effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
ROS<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep 0\tabcellsep (no path)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep -0.0000126\tabcellsep 0.000186\tabcellsep -0.07\tabcellsep 0.946\tabcellsep -0.000376\tabcellsep 0.0003512\\
WC\tabcellsep 2.03E-07\tabcellsep 4.25E-06\tabcellsep 0.05\tabcellsep 0.962\tabcellsep -8.12E-06\tabcellsep 8.53E-06\\
Debt-Equity\tabcellsep -1.37E-06\tabcellsep 0.000057\tabcellsep -0.02\tabcellsep 0.981\tabcellsep -0.000113\tabcellsep 0.0001103\\
Debt-Sales\tabcellsep -3.40E-06\tabcellsep 9.58E-06\tabcellsep -0.35\tabcellsep 0.723\tabcellsep -2.22E-05\tabcellsep 0.0000154\\
Debt-Assets\tabcellsep 0.0002472\tabcellsep 0.000585\tabcellsep 0.42\tabcellsep 0.673\tabcellsep -0.000899\tabcellsep 0.0013936\\
Op.exp\tabcellsep -0.0000526\tabcellsep 0.000347\tabcellsep -0.15\tabcellsep 0.879\tabcellsep -0.000732\tabcellsep 0.0006267\\
\tabcellsep Total effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0037208\tabcellsep 0.053181\tabcellsep 0.07\tabcellsep 0.944\tabcellsep -0.100512\tabcellsep 0.1079533\\
WC\tabcellsep -0.0000599\tabcellsep 0.001241\tabcellsep -0.05\tabcellsep 0.962\tabcellsep -0.002493\tabcellsep 0.002373\\
Debt-Equity\tabcellsep 0.0004054\tabcellsep 0.016852\tabcellsep 0.02\tabcellsep 0.981\tabcellsep -0.032625\tabcellsep 0.0334354\\
Debt-Sales\tabcellsep 0.0010033\tabcellsep 0.001615\tabcellsep 0.62\tabcellsep 0.534\tabcellsep -0.002162\tabcellsep 0.0041681\\
Debt-Assets\tabcellsep -0.073036\tabcellsep 0.078848\tabcellsep -0.93\tabcellsep 0.354\tabcellsep -0.227576\tabcellsep 0.0815037\\
Op.exp\tabcellsep 0.0155414\tabcellsep 0.093513\tabcellsep 0.17\tabcellsep 0.868\tabcellsep -0.167742\tabcellsep 0.1988243\\
ROS<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Bank (M 1 )\tabcellsep -0.0033846\tabcellsep 0.007374\tabcellsep -0.46\tabcellsep 0.646\tabcellsep -0.017838\tabcellsep 0.0110686\\
CR\tabcellsep 0.0001933\tabcellsep 0.050795\tabcellsep 0\tabcellsep 0.997\tabcellsep -0.099363\tabcellsep 0.09975\\
WC\tabcellsep 0.0774031\tabcellsep 0.036946\tabcellsep 2.1\tabcellsep 0.036\tabcellsep 0.0049913\tabcellsep 0.1498149\\
Debt-Equity\tabcellsep 0.0008261\tabcellsep 0.002842\tabcellsep 0.29\tabcellsep 0.771\tabcellsep -0.004744\tabcellsep 0.0063963\\
Debt-Sales\tabcellsep -0.0366503\tabcellsep 0.030452\tabcellsep -1.2\tabcellsep 0.229\tabcellsep -0.096334\tabcellsep 0.0230337\\
Debt-Assets\tabcellsep -0.0486155\tabcellsep 0.069624\tabcellsep -0.7\tabcellsep 0.485\tabcellsep -0.185075\tabcellsep 0.0878442\\
Op.exp\tabcellsep 3.135227\tabcellsep 2.491741\tabcellsep 1.26\tabcellsep 0.208\tabcellsep -1.748496\tabcellsep 8.01895\end{longtable} \par
 
\caption{\label{tab_9}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.1490077177508269\textwidth}P{0.1607221609702315\textwidth}P{0.13073318632855568\textwidth}P{0.05810363836824697\textwidth}P{0.08106394707828005\textwidth}P{0.1358875413450937\textwidth}P{0.13448180815876515\textwidth}}
\tabcellsep Direct effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Normal-based}\tabcellsep \\
\tabcellsep Observed\tabcellsep Bootstrap\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep z\tabcellsep P>z\tabcellsep {}[95\%Conf.\tabcellsep Interval\\
Structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Market (M 2 ) <-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0035397\tabcellsep 0.373759\tabcellsep 0.01\tabcellsep 0.992\tabcellsep -0.729014\tabcellsep 0.7360938\\
WC\tabcellsep 0.0004192\tabcellsep 0.010162\tabcellsep 0.04\tabcellsep 0.967\tabcellsep -0.019498\tabcellsep 0.0203363\\
Debt-Equity\tabcellsep 0.0117193\tabcellsep 0.080062\tabcellsep 0.15\tabcellsep 0.884\tabcellsep -0.1452\tabcellsep 0.1686386\\
Debt-Sales\tabcellsep 0.0062141\tabcellsep 0.009506\tabcellsep 0.65\tabcellsep 0.513\tabcellsep -0.012418\tabcellsep 0.0248459\\
Debt-Assets\tabcellsep 0.2973973\tabcellsep 1.529025\tabcellsep 0.19\tabcellsep 0.846\tabcellsep -2.699438\tabcellsep 3.294232\\
Op.exp\tabcellsep 0.3142367\tabcellsep 0.581213\tabcellsep 0.54\tabcellsep 0.589\tabcellsep -0.824921\tabcellsep 1.453394\\
ROS <-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Market (M 2 )\tabcellsep 0.0032189\tabcellsep 0.007603\tabcellsep 0.42\tabcellsep 0.672\tabcellsep -0.011683\tabcellsep 0.0181209\\
CR\tabcellsep 0.0001794\tabcellsep 0.039557\tabcellsep 0\tabcellsep 0.996\tabcellsep -0.07735\tabcellsep 0.0777088\\
WC\tabcellsep 0.0774018\tabcellsep 0.039902\tabcellsep 1.94\tabcellsep 0.052\tabcellsep -0.000805\tabcellsep 0.1556083\\
Debt-Equity\tabcellsep 0.0008244\tabcellsep 0.005141\tabcellsep 0.16\tabcellsep 0.873\tabcellsep -0.009251\tabcellsep 0.0108997\\
Debt-Sales\tabcellsep -0.0366724\tabcellsep 0.030392\tabcellsep -1.21\tabcellsep 0.228\tabcellsep -0.09624\tabcellsep 0.0228947\\
Debt-Assets\tabcellsep -0.0496291\tabcellsep 0.08174\tabcellsep -0.61\tabcellsep 0.544\tabcellsep -0.209837\tabcellsep 0.1105786\\
Op.exp\tabcellsep 3.13427\tabcellsep 2.403235\tabcellsep 1.3\tabcellsep 0.192\tabcellsep -1.575984\tabcellsep 7.844523\\
\tabcellsep Indirect effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
ROS <-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Market (M 2 )\tabcellsep 0\tabcellsep (no path)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0000114\tabcellsep 0.001202\tabcellsep 0.01\tabcellsep 0.992\tabcellsep -0.002344\tabcellsep 0.0023667\\
WC\tabcellsep 1.35E-06\tabcellsep 3.31E-05\tabcellsep 0.04\tabcellsep 0.967\tabcellsep -6.35E-05\tabcellsep 0.0000662\\
Debt-Equity\tabcellsep 0.0000377\tabcellsep 0.000272\tabcellsep 0.14\tabcellsep 0.89\tabcellsep -0.000496\tabcellsep 0.0005714\\
Debt-Sales\tabcellsep 0.00002\tabcellsep 5.54E-05\tabcellsep 0.36\tabcellsep 0.718\tabcellsep -8.85E-05\tabcellsep 0.0001285\\
Debt-Assets\tabcellsep 0.0009573\tabcellsep 0.005486\tabcellsep 0.17\tabcellsep 0.861\tabcellsep -0.009794\tabcellsep 0.011709\\
Op.exp\tabcellsep 0.0010115\tabcellsep 0.003103\tabcellsep 0.33\tabcellsep 0.744\tabcellsep -0.005071\tabcellsep 0.007094\\
\tabcellsep Total effects\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Structural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Market (M 2 )<-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CR\tabcellsep 0.0035397\tabcellsep 0.373759\tabcellsep 0.01\tabcellsep 0.992\tabcellsep -0.729014\tabcellsep 0.7360938\\
WC\tabcellsep 0.0004192\tabcellsep 0.010162\tabcellsep 0.04\tabcellsep 0.967\tabcellsep -0.019498\tabcellsep 0.0203363\\
Debt-Equity\tabcellsep 0.0117193\tabcellsep 0.080062\tabcellsep 0.15\tabcellsep 0.884\tabcellsep -0.1452\tabcellsep 0.1686386\\
Debt-Sales\tabcellsep 0.0062141\tabcellsep 0.009506\tabcellsep 0.65\tabcellsep 0.513\tabcellsep -0.012418\tabcellsep 0.0248459\\
Debt-Assets\tabcellsep 0.2973973\tabcellsep 1.529025\tabcellsep 0.19\tabcellsep 0.846\tabcellsep -2.699438\tabcellsep 3.294232\\
Op.exp\tabcellsep 0.3142367\tabcellsep 0.581213\tabcellsep 0.54\tabcellsep 0.589\tabcellsep -0.824921\tabcellsep 1.453394\\
ROS <-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Market (M 2 )\tabcellsep 0.0032189\tabcellsep 0.007603\tabcellsep 0.42\tabcellsep 0.672\tabcellsep -0.011683\tabcellsep 0.0181209\\
CR\tabcellsep 0.0001908\tabcellsep 0.039945\tabcellsep 0\tabcellsep 0.996\tabcellsep -0.0781\tabcellsep 0.0784821\\
WC\tabcellsep 0.0774032\tabcellsep 0.039903\tabcellsep 1.94\tabcellsep 0.052\tabcellsep -0.000804\tabcellsep 0.1556108\\
Debt-Equity\tabcellsep 0.0008621\tabcellsep 0.00527\tabcellsep 0.16\tabcellsep 0.87\tabcellsep -0.009467\tabcellsep 0.0111916\\
Debt-Sales\tabcellsep -0.0366524\tabcellsep 0.0304\tabcellsep -1.21\tabcellsep 0.228\tabcellsep -0.096235\tabcellsep 0.0229302\\
Debt-Assets\tabcellsep -0.0486718\tabcellsep 0.080687\tabcellsep -0.6\tabcellsep 0.546\tabcellsep -0.206815\tabcellsep 0.1094712\\
Op.exp\tabcellsep 3.135281\tabcellsep 2.402844\tabcellsep 1.3\tabcellsep 0.192\tabcellsep -1.574207\tabcellsep 7.84477\end{longtable} \par
 
\caption{\label{tab_10}Table 7 :}\end{figure}
 			\footnote{© 2018 Global Journals} 		 		\backmatter  			 
\subsection[{Appendix}]{Appendix}			 			  				\begin{bibitemlist}{1}
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\end{document}
