Chapter 4: Findings and discussion

4.1 Introduction

The present chapter is divided into two subsections; findings and discussion. The findings

section focuses on reporting the results of statistical analysis while the discussion section

critically discusses the findings so that meaningful conclusions can be drawn and

recommendations be made.

4.2 Findings

The data for a period of ten years for 70 companies had been collected for manufacturing

companies listed on the LSE. The data as per methodology has been run in SPSS to generate

regression results. The results for model 1 mentioned in the previous chapter with ROA as the

DV have been presented in appendix section from table 1-4.

Table 1 highlights that the model used with ROA as the DV and the remaining dimensions of

capital structure including firm size predicts 71.8% variation in the ROA dimension of

profitability (the R-square value) which implies that the model is a good fit. This can be

further confirmed from table 2 which shows that the significant value in in the last column is

less than 0.05 which further endorses that the results produced by the model are statistically

significant. Moving further, table 3 explains that all the IVs are positively related with ROA

and the relation is statistically significant. The only IV that does not influence ROA

dimension of profitability in a significant manner is current ratio because the significant value

for that variable is greater than 0.05. Table 4 explains why this dimension of capital structure

does not have a significant impact on ROA; it shows that CR has multicollinearity issue

because of which it did not turn out to be significantly related with ROA. Tabachnick and
Fidell (2007) explain that any IV that is highly correlated with another IV may distort

regression results.

Table 5 onwards the results for regression model two with ROCE as the DV, outlined in the

methodology section are presented. It could be observed from table 5 that the regression

model with all dimensions of CS and the firm size variable as IVs and ROCE component of

profitability can predict 78.8% variation in the DV. This percentage according to Hair et

al.,(2006) is a reasonably high percentage to consider this model a good fit. This result can

further be supported by the results of table 6 through which it is evident that the results

presented by the regression model would be statistically significant as the significant value is

less than 0.05. Additionally, table 7 shows that the total debt to total asset and debt to equity

dimensions of capital structure are positively and significantly related with ROCE. The

significant value less than 0.05 points out towards a statistically significant relationship. The

other dimension pf CS; CR is negatively and significantly related to ROCE which employs

that a change in 0.125 units of CR will bring about a negative change of 1unit in ROCE. Firm

size on the other hand though is negatively related with ROCE, but the relationship is not

statistically significant which implies that firm size does not have any significant impact on

ROCE. Table 8 further confirms why TA does not have a significant influence on ROCE; it

has multicollinearity issue.

The results specify that capital structure as a whole along with size of the firm has different

impact on the two dimensions of profitability. While firm size does not have any impact on

ROCE dimension of profitability, CR dimension of CS does not have any impact on ROA.

As for the direction of relationship, all the CS variables and firm size is positively related to

ROA whereas CR and firm size are negatively related to ROCE dimension of profitability.

The impact of all the variables combined on profitability as a whole show that there is no

statistically significant relationship between profitability and CR and profitability and firm
size (Table 9) which implies that impact of combination of these variables on profitability in

terms of statistically significant relationship is combination of the effect of IVs separately on

both the DVs. However, the direction of relationship in the combined model (table 9) are all

positive as compared to different results of model 1 and 2.

The next subsection is that of discussion where findings of the present study are discussed

critically to see if the findings of the present study have supported the previous works or have

been able to refute their findings to bring to light any critical issue that needs to be addressed.

4.2 Discussion

The present study is designed to examine the relationship between aspects of profitability and

capital structure for which secondary data has been used. The choice of data type is in line

with all previous studies such as Jindrichovska (2013), Kodongo et al., (2015) and Mwangi

et al., (2014). It is important to note that the type of data required to achieve objectives of the

study will always be secondary because aspects of profitability and capital structure of the

company can be gathered only from their annual reports which makes the data secondary.

Additionally the present study has employed a sample of 70 companies which is not even

close to previous studies. For example, Gill et al., (2011) had used a sample size of 272 firms

whereas Juan Garcia-Teruel et al., (2007) had used 8872 firms. The sample size used for the

present study has been calculated using the formula for minimum sample size for regression

provided by Cohen et al., (2013) which implies that this sample size is also not insufficient

and can be considered good enough for regression results to be valid. However, an important

implication of a relatively smaller sample size needs to be highlighted that this sample size

specifies that the analysis could have been more in depth in case a larger sample size would

have been used, but the fact that majority of the manufacturing firms listed on LSE have been

made part of the study cannot be overlooked. This points out to yet another strength of the
study because the researcher has gathered data from majority of the manufacturing firms

listed on LSE.

Moving forward to it is to be noted that the present research is focused primarily on the

manufacturing sector rather than considering all the firms listed on LSE as a whole as has

been done by Kodongo et al., (2015). The choice of methodology is in line with the work of

Olweny, and Shipho (2011) and Gill et al., (2011). The choice of statistical analysis technique

used in the present study is multiple regression. The same technique has been used earlier too

by researchers such as Magpayo (2011), and Taani (2013). Other than the fact that the same

technique has been used by various other researchers, the fact that the type of data sets

employed and the hypothesis that needs to be tested, pointed out to regression being the best

suited technique for the present study, cannot be ignored. Tabachnick and Fidell (2007) as

well as Hair et al.,(2006) have explicitly outlined that when one DV is examined against a set

of IVS, then multiple regression is the best suited technique given that the researcher intends

to observe the presence of a relationship between the two. Multiple regression according to

them explains not only the presence of relationship between the DV and set of IVs but also

the strength and direction of the relationship. Considering the explanation of the two

researchers and choice of methodology of previous study it could be considered that the

present study has made an appropriate choice of statistical technique to analyse the data

gathered.

Furthermore, the present study has chosen financial ratios such as current ratio, total debt to

total assets and debt to equity ratio as components of capital structure. Two of the ratios

chosen as CS variables is in line with the work of Yazdanfar, and Öhman, (2014) but the CR

has been taken from the work of Gill et al., (2011) so as to combine aspects of various models

proposed by previous researchers in a way that it is suitable for the present study. Another

variable has been included in the set of IVs which is the size of the firm. Size of the firm has
been used as log of total assets to make it more manageable for regression purpose. The

choice of this variable is according to the advice of Sekaran and Bougie, (2016) who argue

that certain macroeconomic variables which may not be part of the model directly, but their

impact cannot be neglected. Olweny, and Shipho (2011) have used firm size as a control

variable because Mujahid and Akhar (2014) emphasizes that bigger firms deliver better

performance whereas smaller firms may not be as efficient in performance as larger firms.

The present study has used size of the firm as log of total assets as the firm size variable to

test if it has any impact on firm profitability.

Moreover, the present study has produced results using two regression models in which both

aspects of profitability have been tested separately. Additionally, the study has run a third

model the equation of which has not been provided in the methodology section and it tests the

impact of CS variables on overall profitability; both the aspects of profitability combined.

One model tested the impact of firm size and CS variable on ROCE while other assessed the

impact on ROA. The models designed are in line with the work of Gill et al., (2011) and

Zeitun and Tian (2014). The results specify that all the variables of CS are positively related

to ROA. However, current ratio is not significantly related to ROA. The findings regarding

relationship of ROA with CS variables other than CR is against the findings of Habib et al.,

(2016) who have found an inverse and statistically significant relationship between these

variables specifically in the manufacturing firm. Their findings may be deemed correct and

findings of the present study can be challenged because the findings of previous researchers

can be supported theoretically as well. The level of gearing increases riskiness of the

company and thereby impact profitability in an inverse manner. However, this study suggests

otherwise which implies that either data provided in the annual reports is distorted or

companies listed on LSE need to look into the way they operate. As for size of the firm, the

relationship is not in accordance with any of the works discussed in literature. However, Juan
Garcia-Teruel et al., (2007) have specified that the ability of a firm to concert inventory and

sales into cash to meet its liquidity needs has a strong, positive and statistically significant

relationship with profitability. The liquidity aspect used by this study however is not CCC but

the results suggest otherwise that the ability of businesses to meet the immediate financial

needs in form of liquidity does not have a significant impact on profitability. Theoretically

also this result cannot be verified because Petty et al., (2015) explains that there is an inverse

relationship between profitability of the firms and liquidity. This relationship can sometimes

be weakly positive as well. The same finding has been presented empirically by Habib et al.,

(2016) but their data was based on banks which raises a question on generalizability of

findings on sectors other than banks. Firm size however in this model is a significant

predictor of profitability of manufacturing firms listed on the LSE. This finding can further

be confirmed by the work of Gill et al., (2011).

The second model with ROCE as the DV shows that the relationship of TDTA and DTE

aspects of capital structure have a positive and statistically significant relationship with

ROCE which is against the findings of Juan Garcia-Teruel et al., (2007) and Habib et al.,

(2016) as has been explained earlier. On the other hand CR has a statistically significant

relationship with ROCE but the relationship is negative. The negative aspect could be

supported theoretically from the work of Petty et al., (2015) and empirically from the results

of Juan Garcia-Teruel et al., (2007). However, the relationship being statistically

insignificant is something that needs to be examined again as the current literature has

scarcely taken into account ability of firms to pay off their short term liabilities and more

focus on long term debts and gearing. Firm size on the other hand has a negative relation with

profitability but it is not statistically significant which implies that irrespective of the firm

size, firms can be profitable if they manage their capital efficiently.

The overall model points combines all the relations and produces the same results with CR

having a negative relation with profitability which is not statistically significant. This again

points out to gap in literature where short term financial standing for the company needs to be

assessed if one is to analyse profitability of the firm. Furthermore, all the findings are

suggestive that firms can stay profitable irrespective of the size if capital structure variables

are appropriately managed which needs more confirmation. How efficiently the capital

structure is managed is all that would matter. But a surprising finding is that a higher DTE

and TDTA ratio increases riskiness therefore it should have had a negative impact on

profitability yet the findings are opposite. This finding however cannot be questioned because

researchers such as Abor (2005) and Olweny, and Shipho (2011) have found that a higher

DTE may be an indicator of higher profitability in some sectors such as banking whereas it

may not indicate higher profitability in other sectors therefore this finding could be deemed

acceptable. However, this finding can be challenged theoretically. In order to challenge these

findings though a thorough understanding of UK legislation regarding taxation should be

developed because Gill et al., (2011) is suggestive that more debt can lead to profitability in

USA whereas more debt in Kenya an push organizations into losses which implies that

legislation and business environment of the country in which the firms operate case an impact

on the choice of capital structure.

The next chapter is that of recommendations and conclusion where the study closes with the

final, conclusive remarks.

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Appendix

Table 1: Model Summary

Model R R Square Adjusted R Std. Error of

Square the Estimate

1 .718a .515 .512 .69831

a. Predictors: (Constant), TA, DTE, CR, TDTA

Table 2: ANOVAa

Model Sum of df Mean Square F Sig.

Squares

Regression 360.097 4 90.024 184.616 .000b

1 Residual 338.903 695 .488

Total 699.000 699

a. Dependent Variable: ROA

b. Predictors: (Constant), TA, DTE, CR, TDTA

Table 3: Coefficientsa
Model Unstandardized Standardized t Sig. Collinearity
Coefficients Coefficients Statistics

B Std. Error Beta Tolerance VIF

(Constant) 1.386E-017 .026 .000 1.000

TDTA .399 .038 .399 10.635 .000 .495 2.019

1 DTE .343 .037 .343 9.244 .000 .505 1.979

CR .037 .035 .037 1.080 .280 .581 1.720

TA .104 .035 .104 2.952 .003 .562 1.779

a. Dependent Variable: ROA

Table 4: Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Variance Proportions

Index
(Constant) TDTA DTE CR TA

1 1.884 1.000 .00 .08 .08 .05 .07

2 1.473 1.131 .00 .07 .07 .14 .09

1 3 1.000 1.373 1.00 .00 .00 .00 .00

4 .354 2.306 .00 .05 .14 .70 .70

5 .289 2.555 .00 .81 .71 .11 .13

a. Dependent Variable: ROA

Table 5: Model Summary

Model R R Square Adjusted R Std. Error of

Square the Estimate

1 .788a .621 .618 .61770

a. Predictors: (Constant), TA, DTE, CR, TDTA

Table 6: ANOVAa

Model Sum of df Mean Square F Sig.

Squares

Regression 433.824 4 108.456 284.252 .000b

1 Residual 265.176 695 .382

Total 699.000 699

a. Dependent Variable: ROCE

b. Predictors: (Constant), TA, DTE, CR, TDTA

Table 7: Coefficientsa

Model Unstandardized Standardized t Sig. Collinearity

Coefficients Coefficients Statistics

B Std. Error Beta Tolerance VIF

(Constant) 1.470E-017 .023 .000 1.000

TDTA .369 .033 .369 11.121 .000 .495 2.019

1 DTE .480 .033 .480 14.598 .000 .505 1.979

CR -.125 .031 -.125 -4.080 .000 .581 1.720

TA -.029 .031 -.029 -.919 .358 .562 1.779

a. Dependent Variable: ROCE

Table 8: Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Variance Proportions

Index
(Constant) TDTA DTE CR TA

1 1.884 1.000 .00 .08 .08 .05 .07

2 1.473 1.131 .00 .07 .07 .14 .09

1
3 1.000 1.373 1.00 .00 .00 .00 .00

4 .354 2.306 .00 .05 .14 .70 .70

 5 .289 2.555 .00 .81 .71 .11 .13

a. Dependent Variable: ROCE

Table 9: Coefficientsa

Model Unstandardized Standardized t Sig. Collinearity

Coefficients Coefficients Statistics

B Std. Error Beta Tolerance VIF

(Constant) 7.143E-006 .020 .000 1.000

TDTA .384 .029 .422 13.406 .000 .495 2.019

1 DTE .412 .028 .451 14.498 .000 .505 1.979

CR -.044 .026 -.048 -1.660 .097 .581 1.720

TA .038 .027 .041 1.399 .162 .562 1.779

a. Dependent Variable: Profitability