5.4 Empirical Study A
5.4.5 Data validity and reliability
All the items in the research instrument were subjected to a factor analytical procedure to determine which items should be used in summated scales to calculate the relevant scores.
The factor analytical procedure revealed that the items measuring success loaded into 11 different categories. These categories were named; Credit product offering for small businesses, Shortcomings of the Khula Guarantee Scheme in enhancing credit extension, The bank’s Credit Policy differentiates for small business needs, Primary proof of income for
small business lending, Additional/supporting proof of income for small business lending, Challenges associated with lending to small businesses, Credit risks associated with lending to small businesses, Micro-economic factors: Financial, Micro economic factors: Strategy, Macro-economic factors, Financial ratios/key measures, Alternative financing methods.
The Kaiser-Meyer-Olkin (KMO) is a measure of how suited the data is for factor analysis hence this method was used to test the sample adequacy. The test measures sampling adequacy for each variable in the model and for the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. For the KMO, the value can vary between 0 and 1, where the values closer to 1 is better, and 0.6 is the suggested minimum (Cerny & Kaiser, 1977).
The Bartlett's Test of Sphericity is the test for the null hypothesis that the correlation matrix has an identity matrix. This would mean that the correlation between the variables is zero. For factor analysis, however, we need some relationship between the variables and therefore the result of the Bartlett’s test must be significant, in other words we want to reject the null hypothesis (Cerny & Kaiser, 1977).
The statistical analysis of the data included an assessment of the internal reliability of the measuring instrument. This included the calculation of the Cronbach Alpha coefficients, which is a measure of the internal homogeneity or consistency of a set of variables, therefore, it shows the extent to which the same set of respondents responded in a consistent manner to similar variables/questions. Higher values are always preferred over lower ones. Reliability coefficients less than 0.50 are deemed to be unacceptable, and those above 0.70 are preferable (Nunally, 1978:226).
An example of the analysis is provided below.
Questions A1 to A3 (Refer to Addendum A: Questionnaire) was analyzed using Principal component exploratory factor analyses with Oblimin rotation, to explore the underlying factors.
One factor was obtained from these questions and was named Credit product offering for small businesses (refer to Table 5-1).
Table 5-1 Factor: Credit product offering for small businesses
KMO Bartlett’s Test (p-value) % Variance explained Cronbach’s alpha
0.732 0.000 77.49% 0.854
The KMO and Bartlett’s test for this Factor analysis was 0.732 (should be above 0.5) and 0.000 (should be smaller than 0.05) respectively, indicating that a factor analysis can be done.
The Percentage variance explained was 77.49%. From the Component matrix (Table 5-2) it is seen that all of the questions loaded above 0.3 on one factor, thus one factor was formed.
The reliability for these questions was 0.854 (Cronbach’s Alpha).
Table 5-2 Component Matrix
Component Credit product offering
for small businesses The banks: | A3 Provide
suitable credit products to meet the needs of small businesses.
.892
The banks: | A2 Provide clear lending criteria requirements to small business owners.
.877
The banks: | A1 Understand the needs of small businesses.
.872
In order to determine the validity and internal consistency of the data, a confirmatory factor analysis was conducted on the hypothesized factors (9 factors). The results for each factor can be found in Table 5-3. The first factor in Table 5-3 (Credit product offering for small businesses) was used as an example above, in order to provide an explanation on how to read the data provided in the table. A description of the questions which form the components 1 and 2 for “Loan assessment: “Acceptable proof of income” and “Loan assessment: Micro- economic factors” can be found in Table 5-4.
Table 5-3: Factor analysis
Factor KMO Bartlett's Test
(p-value)
% Variance explained
Cronbach's alpha Credit product offering for
small businesses 0.732 <0.0001 77.49% 0.854
Khula Credit Guarantee
Scheme: Challenges for banks 0.553 <0.0001 71.89% 0.71
Bank Credit Policy 0.638 <0.0001 70% 0.78
Challenges for banks 0.782 <0.0001 50.90% 0.714
Acceptable proof of income
0.789 <0.0001 63.63% 0.853 Component 1 0.817 Component 2 Loan assessment: Micro-
economic factors 0.858 <0.0001 62.63% 0.874 Component 1
0.832 Component 2 Loan assessment: Macro-
economic factors 0.866 <0.0001 71.79% 0.901
Financial ratios/Key matrix 0.832 <0.0001 71.23% 0.898 Alternative sources of finance 0.642 <0.0001 58.04% 0.623
With reference to Table 5-3, the lowest observed Cronbach Alpha value is 0.62 which is still deemed acceptable, and the other values are all above 0.70; hence the measuring instrument can thus be regarded as reliable.
Table 5-4: Description of the questions which form the components 1 and 2 for “Loan assessment: Micro-economic factors” and “Acceptable proof of income”
LOAN ASSESSMENT: MICRO-ECONOMIC FACTORS
Component 1 Component 2
The financial strength of the owners (by obtaining a statement of assets and liabilities of each
owner/member/director).
Viability of business idea (demonstrated in a business plan).
Owner’s financial contribution to the business.
Realistic envisaged growth.
Clear credit bureau report of owners/members/directors.
Projections are in line with past performance.
Clear credit bureau of the business. Business experience of the owners/members/directors.
Owners/members/directors personal credit history with the bank.
Qualifications of
owners/members/directors.
Business credit history with the bank.
ACCEPTABLE PROOF OF INCOME
Component 1 Component 2
Financial Statements and/or Sales invoices.
Cash flow statement. Purchase receipts.
Projected cash flow statement. Income tax return (owner/s).
Personal (owner/s) account bank statements.
Income tax return (business).
Business account bank statements.
Proof of contract.
The key outcome from the information provided in Table 5-4 above is that the mentioned factors can be formed and used in further analysis. Table 5-5 presents the descriptive statistics with an example of how to read the content of the table.
Table 5-5: Descriptive statistics for factors
N Minimum Maximum Mean Std.
Deviation Credit Product Offering for SB’s 323 1.00 4.00 2.5134 .80479
Credit Policy SB needs 320 1.00 4.00 2.2344 .72685
Credit Risk in lending to SB’s 319 1.00 4.00 1.9224 .57638
Shortcomings of Khula 71 1.00 3.33 1.9491 .58049
Proof of Income 295 1.00 4.00 1.7153 .63717
Additional Proof of Income 295 1.00 4.00 2.0686 .74755
Micro-economic factors: Financial 255 1.00 4.00 1.8359 .62238 Micro-economic factors: Strategy 255 1.00 4.00 1.6729 .55940
Macro-economic factors 255 1.00 4.00 1.6894 .60847
Financial ratios/Key measures 240 1.00 4.00 1.6233 .56223
Alternative Finance Methods 218 1.00 3.67 1.9159 .59488
Challenges in Lending to SB’s 320 1.00 4.00 2.1813 .61038
Valid N (listwise) 72
With reference to Table 5-5, Credit product offering for small businesses had the highest mean of 2.51, whereas the Financial ratios/Key measures had the lowest mean of 1.62. This means the respondents tended to disagree with the questions in Credit product offering for small businesses and they agreed the most with the questions in Financial ratios/Key measures.
The Correlations Matrix is presented in Table 5-6 below.
Table 5-6 Correlations Matrix
Credit Product Offering
Credit Policy needs
Credit Risk Lending
SB
Shortcom ings Khula
Proof of
Income Add Proof Income
Micro:
Financial
Micro:
Strategy Macro Financial ratios
Alternative Finance Methods
Challenges Lending SB
Credit Product
Offering 1.000 .280** -.027 -.055 .048 -.003 .136* .015 -.007 .071 -.128 -.172**
Credit Policy needs .280** 1.000 .213** .166 .071 .080 .041 .031 .108 .094 .039 .100
Credit Risk Lending
SB -.027 .213** 1.000 .122 .229** .217** .227** .294** .372** .277** .171* .368**
Shortcomings Khula -.055 .166 .122 1.000 .191 .151 .303** .178 .177 .276* -.023 -.172
Proof of Income .048 .071 .229** .191 1.000 .592** .417** .465** .479** .466** .264** .206**
Add Proof
Income -.003 .080 .217** .151 .592** 1.000 .292** .292** .394** .299** .180** .134*
Micro: Financial .136* .041 .227** .303** .417** .292** 1.000 .520** .529** .464** .171* -.055
Micro: Strategy .015 .031 .294** .178 .465** .292** .520** 1.000 .652** .459** .215** .073
Macro-Economic -.007 .108 .372** .177 .479** .394** .529** .652** 1.000 .582** .337** .149*
Financial
ratios .071 .094 .277** .276* .466** .299** .464** .459** .582** 1.000 .326** .048
Alternative Finance
Methods -.128 .039 .171* -.023 .264** .180** .171* .215** .337** .326** 1.000 .259**
Challenges Lending
SB -.172** .100 .368** -.172 .206** .134* -.055 .073 .149* .048 .259** 1.000
With reference to Table 5-6, all the variables marked with an asterisk (**) must be considered as they are significantly related to the comparing variable. For example, with reference to the value in the second row (.280**) one can derive the following: there was a positive medium relationship of 0.28 between Credit Product offering and Credit Policy needs, meaning that the more the participants agreed with Credit Product offering, the more they agreed with Credit Policy needs.
There is only one negative correlation (-.172**), which is between the Challenges in small business lending and the Credit Product offering. The reason for this may be that the challenges for small business lending is not so much the product offering to small businesses but the challenges are more related to other reasons. Judging from Table 5-6, the challenges for small business lending are more associated with proof of income, where proof of income is not readily available from the small business segment as they mainly lack formal financial statements.
Furthermore, the more the participants agreed with macro-economic factors to be considered when assessing small business applications, the more they agreed with the challenges in small business lending, which highlights that macro-economic factors such as the current economic environment in South Africa could also negatively impact on small businesses.
There is also a correlation between alternative finance methods and challenges in small business lending which may be an alternative financing solution considering the challenges identified.
What is also prominent from Table 5-6, is the correlation between micro (firm-based) factors, macro-economic factors, financial ratios, challenges, and credit risk. This signifies that firm- based factors, macro-economic factors, financial ratios, and the challenges have a significant impact on the credit risk in small business lending.
In addition, there is a prominent correlation between firm-based and macro-economic factors, financial ratios, alternative finance methods, credit risk and proof of income, or additional proof of income types.
Below the results of the Anova test is provided.
Table 5-7 Anova Test
Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means (Gamst, Meyers & Guarino, 2018). With reference to Table 5-7, the effect sizes larger than 0.45 (which signifies a practical significant difference) (Gamst et al., 2018) are reported on below.
There was a medium effect size of 0.46 between the Lenders and Accountants in practice for Credit Product offering. This means the Lenders and Accountants in practice had a practical significant difference in their response when answering questions. Accountants in practice agreed more with the questions in Credit Product offering than the Lenders with means 2.71 and 2.34 respectively.
There was a medium effect size of 0.59 between the Lenders and Accountants in practice for “alternative finance methods”. The Lenders agreed more with the questions in “alternative finance methods” than the Accountants in practice with means 2.02 and 1.69 respectively.
There was a medium effect size of 0.63 between the Lenders and Accountants in practice for challenges in lending to small businesses. The Lenders agreed more with the questions in challenges in lending to small businesses than the Accountants in practice with means 2.41 and 2.05 respectively.
There was a medium effect size of 0.48 between the Non-lenders and Accountants in practice for “additional acceptable proof of income”. Accountants in practice agreed
Lenders Non-lenders Accountants in
practice Effect Size
N Mean Std N Mean Std N Mean Std Lenders with Non-Lenders Lenders with Accountants in practice Non-Lenders with Accountants in practice
Credit Prod Off SB 89 2.34 0.79 161 2.52 0.79 73 2.71 0.80 .22 .46 .24 Credit Policy SB
needs 89 2.28 0.69 159 2.24 0.72 72 2.17 0.80 .06 .14 .09 Credit Risk Lending
SB 89 1.89 0.52 159 1.93 0.59 71 1.95 0.62 .06 .10 .05 Shortcomings Khula 29 1.86 0.59 30 2.00 0.53 13 2.03 0.69 .23 .24 .04 Proof of Income 84 1.69 0.57 146 1.70 0.64 65 1.78 0.71 .00 .13 .12 Add Proof Income 84 2.07 0.73 146 1.95 0.73 65 2.32 0.76 .17 .32 .48 Micro: Financial 80 1.73 0.62 124 1.88 0.61 51 1.91 0.65 .24 .27 .04 Micro: Strategy 80 1.70 0.57 124 1.67 0.56 51 1.64 0.54 .04 .10 .07 Macro-economic 80 1.73 0.57 124 1.64 0.61 51 1.75 0.66 .15 .04 .17 Financial ratios 78 1.56 0.51 114 1.65 0.58 48 1.66 0.62 .15 .16 .01 Alternative Finance
Methods 76 2.02 0.53 100 1.93 0.63 42 1.69 0.56 .14 .59 .38 Challenges Lending 89 2.41 0.54 159 2.12 0.64 72 2.05 0.57 .46 .63 .11
more with the questions in “additional acceptable proof of income” than the Non- lenders with means 2.32 and 1.95 respectively.
There was a medium effect size of 0.46 between the Lenders and Non-lenders for
“challenges in small business lending”. The Lenders agreed more with the questions in “challenges in small business lending” than the Non-lenders with means 2.41 and 2.12 respectively. This may be attributable to the fact that the Lenders have first-hand experience in the challenges to lend to small businesses, as the Lenders are credit grantors which assess small business credit applications on a daily basis.