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INTERNATIONAL JOURNALS OF ACADEMICS & RESEARCH
IJARKE Business & Management Journal
standard deviation of 1.564. The statement that collection policy monitors account receivable to know had a mean score of 3.68
and standard deviation of 1.463. The statement that circumstances for offering credit to clients had a mean score of 3.93 and a
standard deviation of 1.078.
8.1.3 Account Receivables
The third objective was to determine the effect of account receivables on financial performance of transport firms in Mombasa
County. The statement in agreement that available debt collection policy has assisted towards effective debt management had a
mean score of 4.21 and a standard deviation of 1.210. The statement that transport firms sets and follows debt collection policy
and terms had a mean score 3.26 and a standard deviation of 1.435. The statement that the organization implements these terms
and policies in case of failure to pay the loan had a mean score 3.60 and standard deviation of 1.374. The statement that
favourable credit terms stimulate sales had a mean score of 3.59 and a standard deviation of 1.232.
Table 5 Account Receivables
Available debt collection policy has assisted towards
effective debt management
Transport firms sets and follows debt collection policy and
terms
The organization implements these terms and policies in
case of failure to pay the loan
Favourable credit terms stimulates sales
Valid N (listwise)
8.1.4 Credit Terms
ISSN: 2617-4138 www.ijarke.com
DOI: 10.32898/ibmj.01/1.4article07
Table 6 Credit Terms
Business growth has been as a result of proper financial
management practices undertaken by the firm.
There had been an improvement in debtor's collection by using
credit collection policies
The business growth depends on sales returns in terms of price
of the product, sales in the period, number of customers in a period
and credit collection policy in place
Solvency-Long-term debt against your assets and equity
Valid N (listwise)
N
103
92 IJARKE PEER REVIEWED JOURNAL
103
103
103
103
N
103
103
103
103
103
Mean
The fourth objective of the study was to examine influence of credit terms on financial performance of transport firms in
Mombasa County. The statement that available debt collection policy has assisted towards effective debt management had a mean
score of 4.21 and a standard deviation of 1.210. The statement that transport terms of sales had a mean score of 3.76 and a
standard deviation of 1.302. The statement that credit collection period had a mean score of 3.57 and a standard deviation of
1.684. The statement those terms of extension of credit facilities as shown in Table 6
N
103
4.21
3.26
103
103
3.60
3.59
Mean
Available debt collection policy has assisted towards
effective debt management
Transport terms of sales
Credit collection period
Terms of extension of credit facilities
Valid N (listwise)
8.1.5 Financial Performance
The statement that business growth has been as a result of proper financial management practices undertaken by the firm had a
mean score of 3.68 and a standard deviation of 1.463.
Table 7 Financial Performance
Vol. 1, Issue 4
4.21
3.76
3.57
3.51
103 3.48
103 3.13
Std.
Deviation
1.210
1.435
1.374
1.232
3.58
Std.
Deviation
Std.
Mean Deviation
3.68
1.463
1.210
1.302
1.684
1.552
1.259
1.525
1.492
May-Jul. 2019

INTERNATIONAL JOURNALS OF ACADEMICS & RESEARCH
IJARKE Business & Management Journal
The statement that business growth has been as a result of proper financial management practices undertaken by the firm had a
mean score of 3.68 and a standard deviation of 1.463. The statement that there had been an improvement in debtor's collection by
using credit collection policies had a mean score of 3.48 and a standard deviation of 1.259. The statement that business growth
depends on sales returns in terms of price of the product, sales in the period, number of customers in a period and credit collection
policy in place had a mean score of 3.13 and a standard deviation of 1.525. The statement that solvency-Long-term debt against
your assets and equity had a mean score of 3.58 and a standard deviation of 1.492.
8.2 Inferential Statistics
8.2.1 Correlation Analysis
Pearson Bivariate correlation coefficient was used to compute the correlation between the dependent variable (Financial
Performance) and the independent variables (Credit risk control, Credit Policy, Account Receivables and Credit Terms).
According to Sekaran, (2015), this relationship is assumed to be linear and the correlation coefficient ranges from -1.0 (perfect
negative correlation) to +1.0 (perfect positive relationship). The correlation coefficient was calculate I to determine the strength of
the relationship between dependent and independent variables (Kothari & Gang, 2014).
In trying to show the relationship between the study variables and their findings, the study used the Karl Pearson's coefficient
of correlation (r). This is as shown in Table 8 above. According to the findings, it was clear that there was a positive correlation
between the independent variables, Credit risk control, Credit Policy, Account Receivables and Credit Terms and the dependent
variable financial performance. The analysis indicates the coefficient of correlation, r equal to 0.215, 0.551, .267 and .167 for
Credit risk control, Credit Policy, Account Receivables and Credit Terms respectively. This indicates positive relationship
between the independent variable namely Credit risk control, Credit Policy, Account Receivables and Credit Terms and the
dependent variable financial performance.
Table 8 Pearson Correlation
Financial
Performance
Credit
Risk
Management
Credit
Policy
Account
Receivable
Credit
Terms
Financial
Performance
1
103
.215
.000
103
.551"
.000
103
Table 9 Model Summary
R
Square
.656
93
Credit
Risk
Management
.000
103
1
103
.007
.000
103
.267** .736"
.004
.000
103
103
.167**
.247*
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
8.2.2 Coefficient of Determination (R²)
.000
103
ISSN: 2617-4138 www.ijarke.com
DOI: 10.32898/ibmj.01/1.4article07
Credit
Policy
1
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103
.339**
.000
103
.445**
.000
103
Model
Adjusted R Square
.646
1
R
.810ª
a. Predictors: (Constant), Credit Terms, Account Receivable,
Management
Account
Receivable
1
To assess the research model, a confirmatory factors analysis was conducted. The four factors were then subjected to linear
regression analysis in order to measure the success of the model and predict causal relationship between independent variables
(Credit risk control, Credit Policy, Account Receivables and Credit Terms), and the dependent variable (Financial Performance).
Vol. 1, Issue 4
103
.136
.172
103
Credit
Terms
1
103
Std. Error of the Estimate
1.97094
Credit Policy, Credit Risk
May - Jul. 2019

INTERNATIONAL JOURNALS OF ACADEMICS & RESEARCH
IJARKE Business & Management Journal
The model explains 65.6% of the variance (Adjusted R Square= 0.646) on Financial Performance. Clearly, there are factors
other than the four proposed in this model which can be used to predict financial sustainability. However, this is still a good model
as Bryman and Bell, (2018) pointed out that as much as lower value R square 0.10-0.20 is acceptable in social science research.
This means that 65.6% of the relationship is explained by the identified four factors namely credit risk control, credit policy,
account receivables and credit terms. The rest 34.4% is explained by other factors in the financial performance not studied in this
research. In summary the four factors studied namely, credit risk control, credit policy, account receivables and credit term or
determines 65.6% of the relationship while the rest 34.4% is explained or determined by other factors.
8.2.3 Analysis of Variance (ANOVA)
The study used ANOVA to establish the significance of the regression model. In testing the significance level, the statistical
significance was considered significant if the p-value was less or equal to 0.05. The significance of the regression model was as
per Table 10 below with P-value of 0.00 which is less than 0.05. This indicates that the regression model is statistically significant
in predicting factors of financial performance. Basing the confidence level at 95% the analysis indicates high reliability of the
results obtained. The overall Anova results indicates that the model was significant at F = 14.506, p = 0.000
Table 10 ANOVA
Model
1
Regression
Residual
Total
Model
1
Sum
Squares
(Constant)
Credit Risk
Management
Where;
225.404
380.693
606.097
Credit Policy
Account
Receivable
of
a. Dependent Variable: Financial Performance
b. Predictors: (Constant), Credit Terms, Account Receivable, Credit Policy, Credit Risk Management
8.2.4 Regression Coefficients
The researcher conducted a multiple regression analysis as shown in Table 11 so as to determine the relationship between
financial performance of transport firms in Mombasa County and the four variables investigated in this study.
The regression equation below established that taking all factors into account (Financial Performance of Transport firms in
Mombasa County) constant at zero financial performance of transport firms in Mombasa County will be 15.430. The findings
presented also showed that taking all other independent variables at zero, a unit increase in credit risk control would lead to a
0.223 increase in the scores of financial performance of transport firms in Mombasa County; a unit increase in credit policy would
lead to a 0.481 increase in the scores of financial performance of transport firms in Mombasa County; a unit increase in account
receivables would lead to a 0.138 increase the scores of financial performance of transport firms in Mombasa County and a unit
increase in credit terms would lead to 0.185 increase the scores of financial performance of transport firms in Mombasa County
(Fama, 2017).
Table 11 Regression Coefficients
Coefficients
df
4
98
102
Unstandardized
B
15.430
Std.
Error
Mean
1.473
.099
.085
.104
.105
Square
56.351
3.885
.223
.481
.138
Credit Terms
.185
a. Dependent Variable: Financial Performance
The regression equation was:
Y = 15.430 +0.223X₁ + 0.481X2 + 0.138X3 +0.185X4
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ISSN: 2617-4138 www.ijarke.com
DOI: 10.32898/ibmj.01/1.4article07
Standardized
Coefficients
Beta
.290
.607
.051
.168
F
14.506
Sig.
.000⁰
t
10.476
2.243
5.685
Vol. 1, Issue 4
2.362
3.769
Y = the dependent variable (Financial Performance)
X₁ = Credit Risk Management, X₂ = Credit Policy, X3 = Account Receivable and X4 = Credit Terms
Sig.
.000
.000
.000
.001
.000
May-Jul. 2019