4.1. ANOVA (Differences in Business Success for Categories of Initial Capital Startup)
The data for each group can be found in
Table 2 above (number in each group, mean, standard deviation, minimum and maximum, etc.). The means for each group are shown in the table.
The homogeneity of variance option in
Table 3a provides Levene’s test for homogeneity of variance, which determines if the variance in scores is the same for all three groups. Because the significance value (Sig.) for Levene’s test was larger than 0.05, the premise of homogeneity of variance was not broken (0.111; based on median and with adjusted df).
The Brown and Forsythe Test is a population variance equality test. It is a reliable test based on absolute differences from the group median within each group. It is a good substitute for Bartlett’s Test for Equal Variances, which is sensitive to sample size imbalances and lack of normality. The Modified Levene Test is the name given to the Brown and Forsythe Test. Levene came up with the notion of changing the data so that an F test on the modified data would result in a test for equal population variances. Results indicate that the assumptions were not violated.
Table 3c’s (ANOVA) section displays sums of squares, degrees of freedom, and other statistics for both between and within groups. The column labelled Sig. is of relevance. There is a significant difference between the mean scores on our dependent variable for the three groups because the Sig. value is less than or equal to 0.05 (0.000). A significance test shows that the null hypothesis, stating that the population means are equal, can be rejected. It does not, however, specify which categories are different. To discover where these discrepancies exist, post hoc tests were utilized.
Table 4 demonstrates the statistical significance of the differences between each pair of groups, as well as the results of post hoc testing (described below).
Study Hypothesis. There are no differences in business success for the different levels of Capital startups.
Using post hoc testing,
Table 4 indicates where the differences between the groups originate. The asterisks (*) indicate the relevant values when looking at the Mean Difference column. This indicates that the two groups being compared are statistically significant at the p.05 level. The actual significance value can be found in the Sig column. Low capital is statistically substantially different from medium capital and high capital in the data provided above. On the other hand, the medium group is not statistically different from the high group.
From
Table 4 above, it is evident that there are differences among the three categories of startup capitals, namely those with low-, medium-, and high-capital startup capitals. This was in tandem with the findings by
Marimuthu and Singh (
2021) that there is a hierarchy of order in the capital requirements of business organizations. At a significant level of 5%, SMEs in the service sector with low startup capital (R7000 and less) and those with medium startup capital (R7001–R22,700) are statistically significant in performance. In essence, startup capitals for the various categories of sources are heterogeneous. To calculate the effect size, the
eta-squared index was used. The eta squared already has the information needed to determine the effect magnitude. The eta squared is the sum of squares between groups divided by the total sum of squares. In this case, 0.051 is obtained by dividing the sum of squares between groups (285.8) by the entire number of squares (5542.79). The calculated eta squared number, according to
Cohen (
1988), implies a near medium effect magnitude. According to Cohen, a small influence is 0.01; a medium effect is 0.06, and a high effect is 0.14.
While studies have confirmed accessibility to finance as one of the major factors limiting the survival of the service-providing sector at the early stage in South Africa (
Bamata et al. 2019;
Bushe 2019), the effects differ from low, medium, and high startups. It was a confirmation of the hierarchy of financing within the service-providing SME sector of South Africa as enunciated in the Pecking Order Theory. This was not surprising, as reported by
Schmidt et al. (
2017), that finance gap and resource dependency theories hold for SMEs in South Africa. These two theories, as components of the POT, have been said to be the best fit for the service-providing sector as they gave preference to internally sourced funds compared to external borrowings since external sources are costlier and less accessible (
Schmidt et al. 2017;
Marimuthu and Singh 2021). Three conclusions could be drawn from the differences in the three categories of startup capital. Firstly, it shows that access to both internal and external sources differ from one category to another (
Abisuga-Oyekunle et al. 2019;
Nyide and Zunckel 2019). While the low-level group depends mainly on internal sources for obvious reasons, both medium and high groups have better access to external sources as startup seeds.
Hence, startup capital requirements by the low, medium, and high groups are hierarchical. Secondly, the contributions of each of these sources to the early survival of each of the startup capital requirements also differ. It is more advantageous for the medium and the high group to have profits reinvested back into the business and ensure continuity without much pressure from external finance givers than the low startup capital group. Finally, the findings reveal that there is a clear relationship between startup capital and the size and success of an SME. Furthermore, an SME with a low-capital startup is predicted to earn lower profits than SMEs with medium or high-capital startups. On the other hand, results in
Table 4 show that there was no statistically significant difference between those within the brackets of medium- and high-capital seeds, as defined above. This result shows that there is little demarcation in the structure of the service-providing sector as far as the medium and high groups are concerned.
Table 5 below shows the subsets of the business score index.
The mean scores of the various groups can be easily compared using
Figure 1. The group with the lowest capital received the lowest business success scores, while the group with the highest capital had the highest. Even though the difference in mean scores among the groups appears to be large on the graph, the real difference is little (19.72, 20.95, and 21.23). Although the actual difference in the groups’ mean scores was relatively small, we obtained a statistically significant result in this instance. This is demonstrated by the near-medium effect size obtained. Nonetheless, With a large enough sample (in this case, N = 501), even little differences can become statistically significant, even if the difference between the groups is of little practical importance.
4.2. Contextual Analysis of Capital-Startup Categories with Business Plans, Government Support, and Customer and Market Base
Table 6 below shows the summarized cases in the model.
Out of a total of 501 respondents, 481 (96%) of these businesses obtained some form of government support; 481 (96%) respondents out of a total of 501 had some form of a business plan, and finally, 477 (95.2%) had some level of customer and market access base. There were more missing cases for the customer and market access score than others. The crosstabulation of the contextual analysis of SMEs in the service sector, as shown in
Table 7,
Table 8 and
Table 9 below, is in respect of the second objective of this study. Three categories of entrepreneurs are identified: those with (i) government support, (ii) a business plan, and lastly, (iii) a customer and market access base (CMA).
Government funds are grants, loans, or other financial assistance from federal, provincial, or local governments or governmental agencies. The low-capital startup category has the highest number of entrepreneurs (213) who started their businesses with a small capital (R7000). Out of these 213 entrepreneurs, the majority (74.2%) of them obtained little funding from the government, while (25.8%) obtained high government funding to facilitate the smooth running of their businesses. The high-capital category has the second-highest number of entrepreneurs who started their businesses with a high capital (> or = R22,701). More than half of them (63.2%) obtained high support from the government, while (36.8%) obtained low government funding to help facilitate their businesses. The medium capital category has the least number of entrepreneurs who started their businesses with medium capital (R7001–R22,701). A majority (57.3%) obtained little support from the government, while 53 (42.7%) obtained high government funding to help facilitate their businesses.
The South African government funded a total of 481 businesses, of which 58.6% obtained low funding from the government. In addition, 74.2% of the low-capital startup category, 57.3% of the medium startup category, and 36.8% of the high-capital category all received a small fund from the government. The low category recorded the highest number of low government funds (74.2%) compared to the high and medium categories. It is so because a very small amount of money was used to start up the business, thus inhibiting the fast growth of the business. Contrariwise, out of the 481 businesses funded by the South African government, a total of 41.4% obtained high government funding. Amongst these 199 businesses, 91 (63.2%) from the high category, 42.7% from the medium category, and 25.8% from the low category all received high government funding. The high category received the greatest number of high government funds (63.2%) compared to the low and medium categories because its entrepreneurs started their businesses with a larger amount of capital. In addition, the low startup capital recorded the highest number of businesses (282) which began operating with small funds; these low startup capitals usually come from the entrepreneur’s own funds. It is obvious that high and medium startup capital comes from many or several sources, including crowdfunding, business loans, and venture capital.
The most important source of planning for a business is the business plan. A business plan is a document that lays out the entrepreneur’s strategy for how he or she intends to run his or her business. The business plan is a roadmap for the path the owners want to take with their business. The low-capital startup category (< or = R7000) has the highest number of entrepreneurs (213). Out of these 213 entrepreneurs, 66.7% have an unstructured business plan, while 33.3% have a structured business plan. The high-capital startup category (> or = R22,700) has the second-highest number of entrepreneurs. However, not all of them have a business plan; the majority, 65.3%, have a structured business plan, while 34.7% do not have a structured business plan. The medium-capital startup category (between R7001 and R22,701) has the least number of entrepreneurs.
A good number of them, 62.9%, have a structured business plan, while 37.1% have an unstructured business plan. Among the 481 businesses, a majority (49.5%) have unstructured business plans. Overall, 66.7% from the low-capital startup category, 37.1% from the medium startup category, and 34.7% from the high-capital category all have and use unstructured business plans to run their businesses. However, the low startup capital category (66.7%) had the highest number of businesses with unstructured plans. It is obvious why the lower category used unstructured plans: the inadequate amount of money, lack of time, and management needed to boost the business. Nonetheless, out of the 481 businesses, a total of 33.3% have structured business plans. Amongst these 481 businesses, 65.3% from the high startup category, 62.9% from the medium startup category, and 33.3% from the low startup category all have structured business plans. However, the high startup capital category (65.3%) recorded the highest number of businesses with structured plans.
Well-structured business plans in these high startup categories are a huge pointer to lenders that owners know their business very well and take them seriously (
Meg 2022). In addition, the structured business plans would increase sales and tip scales in favor of the owner getting a business loan. In all, there are more structured (243) than unstructured businesses (238). The low startup capital category (66.7%) recorded the highest number of businesses with unstructured plans. Obviously, these poorly structured (unstructured) business plans will reduce the confidence of their owners or lenders, and as such, affect production and sales within these businesses.
The low-capital startup category has the highest number of entrepreneurs (209) who started their businesses with a low capital (< or = to R7000). The majority (64.6%) have low customer and market access, while 74 (35.4%) have high customer and market access. The high-capital startup category has the second-highest number of entrepreneurs who started their businesses with a high capital (R22,700 and above). The majority (52.1%) have low customer and market access, while 47.9% have high customer and market access. The medium capital category has the least number of entrepreneurs (124) who started their businesses with a medium capital (between R7000 and R22,700). A total of 73 (58.9%) have low customer and market access, while 51 (41.1%) have high customer and market access. Among 477 businesses, 58.1% have low customer and market access. Correspondingly, 64.6% from the low-capital startup category, 58.9% from the medium startup category, and 69 (47.9%) from the high-capital category all have low customer and market access. Out of the 477 businesses, a total of 41.9% have high customer and market access. Amongst these 200 businesses, 52.1% from the high category, 41.1% from the medium category, and (35.9% from the low category all have high customer and market access.
There are more businesses with low customer and market access (58.1%) compared to those with a high customer and access base (41.9%). Low market access is superfluous for businesses as entrepreneurs would not be able to sell their commodities for more money, especially when they have more reliable access to home and foreign markets. The two essential elements of successful market access strategies include regulatory clearance and product reimbursement. Access also includes acceptability, affordability, availability, and accessibility (
Penchansky and Thomas 1981). A high customer and access base of 41.9% reflects the compatibility between the traits and demands of the clients and the providers within these businesses. The highest number of low startup capital businesses (64.6%) recorded were those with the lowest customer and market access base, while only 35.4% of the low startup category had a high customer and market access base. On the other hand, 47.9% of the high startup capital businesses had low customer and market access, while only 52.1% had high customer and market access. This signifies that the poorly structured plans limited the customer and market access of these companies.
Bamata et al. (
2019) and
Bushe (
2019) show that external capital was identified as a hindrance to the take-off and sustainability of SMEs in South Africa. One major implication of the findings is that expansion might not be an easy task for this sector in the province.
The Chi-Square tests in
Table 10 indicate high levels of significance for government support, business plan, and customer and market access. This suggests that the above-named contextual factors influence the sourcing of Capital startups and successes for SMEs.