SMEs, Success, and Capital Startups: Evidence from the Service Sector in South Africa

Abstract

Financing has been identified as a major stumbling block to the effective launch and long-term success of SMEs, particularly in emerging economies. However, little is known about the impact of capital startups on SMEs’ performance in South Africa. Thus, the aim of this article is to compare differences in business success for three different levels of Capital startups and to determine the impact of capital startups on SMEs’ performance. The analysis was premised on a two-tail hypothesis test that the average weights of the three groups are different. The Cronbach Alpha test and the test–re-test reliability approach on the survey instrument indicated values of 0.70 and 0.875, respectively. A one-way between-groups analysis of variance was conducted startup. There was a statistically significant difference at the p < 0.05 level in scores for the three groups (F (2, 477) = 12.967 p = 0.0000). Post hoc comparisons using the Tukey HSD test indicated that the mean scores for Group 1 (M = 19.93, SD = 6.343) were significantly different from Group 2 (M = 20.94, SD = 2.309) and Group 3 (M = 21.73, SD = 2.880). Despite reaching statistical significance, the actual difference in mean scores between the groups was quite small. The one-way ANOVA approach indicates that finance (startup capital) remains a significant impediment to the effective launch of service-providing SMEs. It is recommended that SMEs who want to raise startup capital must create a solid business plan or build a prototype to sell the idea. This will assist them in discussions with venture capitalists, angel investors, banks, or other financial institutions.

1. Introduction

In South Africa, just like many other countries of the world, the service sector, which accounted for over 60% of the GDP and employment generation as of 2019 (Bhorat et al. 2016; https://www.statssa.gov.za/?m=2020), spans across businesses, community, and social services, transport services, distribution, and financial services. This sector is rapidly receiving the attention of South African policymakers and is used in addressing the drop in manufacturing compared to 2.4% and 25.2% for the agriculture and industrial sectors (2018a) and Owusu et al. (2020); the global economy is gradually moving towards a service-induced/driven status. Since the reduction in employment in the mining and manufacturing sectors of South Africa’s economy, the service sector has assumed the role of a driving force toward a service-driven economy. The service-providing sector of the South African economy remains a source of persistent expansion in the face of the country’s economic woes, according to the Small Enterprise Development Agency (SEDA) 2019 report.

The trend analyses of sectoral contributions to national GDP by Statistic South Africa (2020) showed that the service sector contributed more in terms of employment generation and GDP than other sectors. For instance, between 2010 and 2020, the service sector’s contribution to the GDP was 61.45% as compared to 2.4% and 25.2% for the agriculture and industrial sector in 2020. In the year 2009, the service sector accounted for 69.7% of total employment generation for the country as compared to 25.25% and 5.05% for the agriculture and industrial sectors, respectively. The highest rate (5.61%) for employment generation was observed for agriculture in the year 2015 but later reduced to 5.28% as of 2019. In the case of industry, 2009 was the year this sector had its highest contribution, but this reduced to 23.42% in 2014 and 22.31% in 2019 in terms of employment generation. Despite these lofty contributions and the importance of SMEs, the concept of startup capital remains an issue in less developed and developing economies such as South Africa (Prohorovs et al. 2018; Bushe 2019).

The term startup capital refers to the money raised by a new company to meet its initial costs. Startup capital is often a large sum of money that covers any or all of the company’s major initial costs, such as inventory, licenses, office space, and product development. Startup capital is what entrepreneurs use to pay for any or all the required expenses involved in creating a new business or supporting an existing business. This includes paying for the initial hires, obtaining office space, permits, licenses, inventory, research and market testing, product manufacturing, marketing, or any other operational expense. In many cases, more than one round of startup capital investment is needed to get a new business off the ground.

Despite the importance and contributions of the SME service sector to the national economy, capital remains an issue to contend with in the sector. (Abisuga-Oyekunle et al. 2019; Witt and Gross 2020; Ogujiuba et al. 2021b). According to Prohorovs et al. (2018) and Abisuga-Oyekunle et al. (2019), the availability of startup capital for small and medium-scale entrepreneurs remains important if they are to exploit growth, make investment opportunities, and achieve higher stability in size. This was confirmed by Ahinful et al. (2021), who showed that a higher percentage of SMEs struggled to survive operations beyond the early stages due to insufficient or non-accessible financing. Most importantly, SMEs in the service-providing sector, which has now become the pride of the global economy (Bhorat et al. 2016, 2018; Owusu et al. 2020; Witt and Gross 2020), lack the necessary take-off capital that could guarantee their survival beyond the early stage of operation (Witt and Gross 2020; Blach et al. 2020; Bamata et al. 2019; Ogujiuba et al. 2021a). While obstacles to access capital by SMEs have been researched extensively, the contributions of each source of finance as startup capital have not received much attention in entrepreneurship-related studies, especially in the service-providing sector.

Leaning on existing studies on the relevance of startup capital to SMEs’ success and sustainability in South Africa, therefore, the pertinent question in this study is as follows: what is the impact of capital startups on SMEs’ performance in South Africa? This has become pertinent because SMEs remain one major alternative to job creation and social vices reduction in any nation. Therefore, re-echoing the relevance of startup capital which had been adjudged as one major unabated hindrance to the success of SMEs in developing countries such as South Africa (Bamata et al. 2019; Refiloe et al. 2020), seems to be the right step in the right direction for this study. Not only that, revealing that finance hierarchy matters in the capital base of the service-providing SMEs in South Africa indicates that policy summersault might be one major reason for not realizing the full benefits of past government policies in terms of startup financing categorization. Low, medium, and high startup capitals are designed to achieve different objectives at different stages of SMEs. Another unique contribution of the study is the testing and confirmation of the reliability of finance gap and resource dependency theories as enunciated in the Pecking Order Theory for the South African service-providing sector. These theories, according to Schmidt et al. (2017), are the best approach to the study of the hierarchy of finance within the service-providing sector of an economy. Anderson (2017) opined that testing theories in another context are a major contribution to the field of any knowledge.

Theoretical Framework

Modigliani and Miller’s foundational presentation, known as the MM Theory of capital structure, may be traced back to the notion of capital structure in any economic company. It is a theory based on the irrelevant ideology principle, which claims that a company’s value remains constant regardless of how it funds itself. As a result of the concepts’ unsustainable fundamental assumptions, other capital structure theories have evolved in the financial literature. Some of these theories include the Trade-Off Theory (Kraus and Litzenberger 1973), Agency Cost Theory (Jensen and Meckling 1976), Pecking Order Theory] (Myers 1984), and the Modified Pecking Order Theory (Gobardhun 2011). The Trade-Off Theory was founded on the concept that there is a trade-off between the cost of debt and the benefit (profit) accruable to an organization, in the spirit of its proponents. In essence, the cost of external debt in financing an organization (such as loans) will be covered by profits generated from operations, making the impact of such debt on the capital structure insignificant. However, numerous arguments have entered this premise, resulting in a wide range of reactions to the assumption made by the Trade-underpinning Off (Khemiri and Noubbigh 2018). The Agency Expense Theory is a theory based on the cost of conflicts of interest that might occur between firm owners and management. This theory arose from the free flow of cash, overhang debt, and asset substitution, according to Kao et al. (2018) and Anh and Thao (2019) and can be resolved by introducing debt into a firm’s capital structure, which will then encourage managers to make optimal use of the free cash flow for the benefit of all stakeholders. The Market Timing Theory is the most recent of all the theories, in which the weighted average cost of debt in the past vs. its book value is a function of its current market leverage. As a result, the issuance of debt instruments in the economy’s service section of time, and its market value, are determined by historical antecedents.

The Pecking Order Theory prioritized internal sources of finance over external sources and equity finance. Its principle is based on the concepts of asymmetry information and adverse selection of finance where preference is given to internally sourced startup capital. Because the presence of asymmetric information allows investors to charge a premium, which is an additional expense to an entrepreneur, business owners prefer internally generated money over borrowing from interest-bearing capital. In conclusion, the Pecking Order Theory, which favors internally sourced capital and focuses on the nature of SMEs, appears to be the best method for the service-oriented SMEs in our study. Others, such as Marimuthu and Singh (2021), have argued that the Pecking Order Theory is the best theory for explaining the capital structure of SMEs, including the service sector of the economy.

2. Literature Review

Globally, the modern-day economy appreciates the relevance of the service sector, and South Africa is not an exception to this service-driven economic paradigm. Access to capital was one of the key hurdles to the growth and development of SMEs in South Africa, according to Ogujiuba et al. (2021c) in their distinct results about the external and domestic factors affecting the success of SMEs in South Africa. Fatoki (2014) identified owners’ savings and financial aid from family and friends as the key startup finances for immigrants in the country while examining how foreigners acquired finance to establish their firms in South Africa. However, as the company grew, the qualitative method demonstrated that these sources became less important to the company’s existence. Financial literacy influenced capital availability and so contributed to the success of service-providing SMEs in South Africa, according to Ngek (2016). Similarly, Beverelli et al. (2016) discovered a positive association between service intermediary output and manufacturing performance, indicating that service activities with fewer constraints contribute more to manufacturing success. Adetokunbo and Edioye (2020) found a favorable relationship between economic growth and several service activities in their study. This indicates that the service sector contributes positively to the country’s economic prosperity. The results of the autoregressive distributed lag (ARDL) method also recommended a corrupt-free economy to maximize the service sector’s economic growth potential. Among others, Bamata et al. (2019) identified the strategic location of the business, feasibility study, and business model plan as the major determinants of SMEs’ access to capital financing by banks in South Africa. In Bushe (2019), accessibility to take-off funds was identified as a major factor responsible for the slow and inadequate number of service providers in South Africa. The study, which comprehensively reviewed the factors that accounted for the success and failures of SMEs in South Africa, further emphasized the relevance of necessary skills that are needed by an entrepreneur to manage the startup capital for the survival and expansion of the business enterprise. While aggregating the importance of SMEs in the areas of employment generation, poverty reduction, income generation, and growth inclusiveness in sub-Saharan Africa, Abisuga-Oyekunle et al. (2019) observed that 17% of SMEs could access capital out of almost 70% of demand: thereby suggesting a huge gap between supply and demand of capital investment to this sector. As a way of supporting SMEs, the provision of risk capital was suggested on a large scale.

According to Lampadarios et al. (2017), Al-Tit et al. (2019), and Ogujiuba et al. (2021d), startup capital is the bedrock of SMEs, including the service sector and the level of its adequacy at the commencement of entrepreneurial operations, which goes a long way in determining its sustainability. On the other hand, Startup capital is the money a business owner needs to start a new business (Carlson 2020). Concluding on the importance of startup capital to the sustainability of SMEs, Ogujiuba et al. (2021b) emphasized the need to enlarge the available startup financial resources allowed by policymakers in South Africa. The outcome of the two-way designed ANOVA method revealed that different amount of startup capital affects the growth of SMEs differently. By extension, there was evidence in support of variations in the effects of startup capital on the success of SMEs in South Africa. On a slightly different template, Xi et al. (2020) differentiated between take-over as a means of survival by SMEs and a complete startup that involves raising new funds and discovered that take-over is a better survival option. Entrepreneurial accomplishment is a function of profit-making which depends on its sources of startup capital. By extension, interest-bearing sources would not only take prior charge over other sources but will also reduce the profit accruable to the business cover. Equity financing in the form of the owner’s contribution and borrowings from friends and relatives was shown as having a positive relationship with the survival of SMEs in Russia. This was in tandem with Okrah et al. (2018), where returns on profit were identified as a decision-making factor by entrepreneurs before venturing into operations.

Bhorat et al. (2018) elucidated the fast transformation of the global economy in South Africa towards the service-providing sector, where a rapid and significant contribution to the national GDP was registered. Placing the service sector with other sectors of the South African economy in a study that was tailored to the service sector’s understanding and characterization, the service-providing SMEs were shown as outperforming other sectors in virtually all the used parameters and econometric approaches. Another dimension to the argument on sources of financing SMEs at the take-off level is that most of these entrepreneurs have not been exploiting all available sources during the take-off stage. Employing internet-based interviews, Lewicki and Wierzejski (2020) concluded that most of the SMEs in Poland have not fully exhausted available startup capital due to ignorance on the part of entrepreneurs and inadequate publicity on the part of startup capital providers in that country. Nonetheless, the knowledge gap has also been identified as one of the major challenges to accessing startup capital by SMEs.

Furthermore, in a study by Li et al. (2021) regarding the aftermath effects of the COVID-19 pandemic on different sectors of the Chinese economy, it was observed that the service sector (hospitality) was the most hit by the pandemic. This shows how susceptible the service sector is to any economic shock and the need for adequate startup capital for SMEs’ sustainability. According to Wu and Xu (2019), the relationship between the backed-up venture capital of SMEs and accessibility to credit and loans in China is key to SME success. The authors found that small and medium enterprises with venture capital backup have no difficulty in accessing loans from banks, unlike their counterparts without such backups. Furthermore, Blach et al. (2020) juxtaposed innovative activities and adequate startup capital in a study that extended the frontier of knowledge in the capital structure of SMEs across the border. They assumed that internally generated capital remains a panacea to the sustainability of SMEs, including the service-providing sector in the selected EU countries. However, the result of the non-parametric approach (Spearman’s rank correlation) countered most of the existing findings in that a heterogeneous relationship was observed between the different sources of financing SMEs and innovativeness. In essence, the relationship between each of the identified sources of financing services (internal, external equity, and debt) differs from the clustered EU countries and regions.

Nonetheless, Marimuthu and Singh (2021) tested the existence of the Pecking Order Theory for SMEs in South Africa and reported the absence of this theory in the capital structure of entrepreneurs and, by extension, the service sector. By implication, this outcome of the panel regression model suggests that the capital market in South Africa is a developing one. On the contrary, Nyide and Zunckel (2019) proved that the capital composition of an enterprise, such as the service-providing sector, contributes in no small way to the survival and economic expansion of South Africa. In the middle of these submissions stands Schmidt et al. (2017), where the relevance of external startup capital for the service-providing sector was emphasized with the caveat that accessibility by SMEs remains a major challenge. Above all, opinions still differ on the prioritization of capital structure of SMEs about sources of finance. The July edition of Economic Outlook South Africa (EOSA 2021) reported that the service sector of the economy had contributed 61.45% to the GDP as against 25.2% and 2.4% for industry and agriculture, respectively. Since the seminal work of Modigliani and Miller (1958) about the relevance of capital structure, different opinions have permeated the ideal capital structure for business outfits (Nguyen et al. 2019; Marimuthu and Singh 2021; Msomi and Olarewaju 2021). In this regard, limited startup capital has been attributed as a major constraint to the survival of SMEs and has been a major concern in recent times to policymakers and academic researchers.

Nonetheless, practical experiences have shown that debt-to-equity finance depends largely on the objectives (both in terms of profit maximization and owners’ wealth maximization) for setting up a business organization (Maziriri et al. 2018; Marimuthu and Singh 2021). A study that focused on the relationship between sources of financing SMEs in South Africa (Agyei 2018; Nyide and Zunckel 2019) emphasized the need for small business owners to understand the fundamental principles and relevance of sources of finance and the contributions of each of these sources to the operational success of business enterprises. Similarly, Brixiova et al. (2020) and Marimuthu and Singh (2021) observed in their separate studies that the lack of the needed capital and financial managerial skill by SME owners accounted for their failures within the first year of operations. This further alluded to the importance of capital (both as a startup and working) to the survival of any business outfit, including the SMEs and the service sector, by extension. In a study that appraised the relevance of the Pecking Order Theory in South Africa, Marimuthu and Singh (2021) observed that enterprises in South Africa care less about the theory and concluded that early-stage business failures arose from owners’ inability to evaluate and appraise the relevance of each of the means of financing as take-off capital. It was further emphasized in the study that one consequence of wrong capital structure selection is financial distress and early lifespan liquidation, especially the harbinger of bankruptcy. The above scenario indicates that the inability of SMEs to identify and adopt the best form of incubation capital structure remains an issue in the entrepreneurial world.

Unlike past studies that merely emphasized the importance of capital to entrepreneurship growth and development (Lampadarios et al. 2017; Kumar 2017; Blach et al. 2020; Ogujiuba et al. 2021a, 2021b), this study is a deviation by being specific in analyzing the impact of startup capital for SMEs in South Africa’s service-providing sector of the economy. Additionally, this article contextualizes capital-startups levels with government support, business plans, and access to customers and markets. The choice of South Africa as a case study is considered timely because, just like many other economies, the country is fast assuming the status of a service-driven economy, and the rate at which SMEs cannot grow beyond the early stage of the operation is becoming a worrisome event (Msomi and Olarewaju 2021). This is not only politically relevant in terms of policy formulation and execution about the startup financing requirements of SMEs in South Africa but will also appraise the importance of each source as incubating capital.

3. Data and Methods

This article compared the differences in business success for three different levels of Capital startups. We used a quantitative design [ANOVA] and crosstabulation that is in line with our intention. To address this objective, we used the following techniques: (i) ANOVA to identify where differences exist for business success and (ii) contextual analysis of the amount of capital with business plans, government support, and customer and market base.

3.1. One-Way ANOVA with Post hoc Tests

The variance (variability in scores) between the different groups (believed to be related to the independent variable) is compared to the variability within each of the groups using an analysis of variance (believed to be due to chance). The analysis of variance, or ANOVA, is a sophisticated statistical approach that uses significance tests to find differences between two or more means or components. It also proposes a mechanism for comparing the averages of multiple populations. The ANOVA test evaluates two types of variation: sample mean differences and variation within each sample. One-way ANOVA is a mathematical extension of the two-sample t-test, in which the F statistic compares group variability to group variability.

Table 1. ANOVA equations.

Source of Variation	Sum of Square	Degree of Freedom	Mean	F
Within	$SSW={{\displaystyle \sum _{j=1}^k{\displaystyle \sum _{j=1}^t\left(x-{\overline{x}}_j\right)}}}^2$	$d{f}_w=k-1$	$MSW=\frac{SSW}{d{f}_w}$	$F=\frac{MSB}{MSW}$
Between	$SSB={\displaystyle \sum _{j=1}^k{\left({\overline{x}}_j-\overline{x}\right)}^2}$	$d{f}_b=n-k$	$MSB=\frac{SSB}{d{f}_b}$
Total	$SST={\displaystyle \sum _{j=1}^n{\left({\overline{x}}_j-\overline{x}\right)}^2}$	$d{f}_t=n-1$

Where F = ANOVA coefficient, MSB = mean sum of squares between the groups, MSW = mean sum of squares within the groups, SST = total sum of squares, n = the total number of samples in a population, SSW = sum of squares within the groups, SSB = sum of squares between the groups, and s = standard deviation of the samples.

below shows the ANOVA equations.

Justification of ANOVA Technique

ANOVA is helpful for testing three or more variables. It is similar to multiple two-sample t-tests. However, it results in fewer type I errors and is appropriate for a range of issues. ANOVA groups differences by comparing the means of each group and includes spreading out the variance into diverse sources. In the case of comparing two groups, the t-test is preferred over ANOVA. However, when we have more than two groups, as is the case in this instance, the t-test is not the optimal choice because a separate t-test needs to be performed to compare each pair. We set the null and alternative hypotheses as follows below:

Null Hypothesis (H0).

The average weights of the three groups are not different.

Alternative Hypothesis (H1).

The average weights of the three groups are different.

Two variables were employed to conduct the analysis. Three unique categories are in one categorical independent variable. In our sample, capital startup was recoded to create three equal groups (high, average, and low). The Business Success Index is our continuous dependent variable in this case. The technique (one-way ANOVA) examines whether the mean scores on the dependent variable differ significantly across the three groups. The F ratio is calculated by dividing the variation between the groups by the variance within the groupings. A high F ratio indicates that there is more variation between groups than within each group (due to the independent variable) (referred to as the error term).

3.2. Study Sample

The sample for the analysis (stratified technique) came from a general survey of active SMEs in South Africa’s Mpumalanga Province. This article is based on the European Union’s (https://single-market-economy.ec.europa.eu/smes/sme-definition_en) classification of SMEs, which considers businesses with fewer than 250 employees as middle-sized businesses and businesses with fewer than 50 employees as small businesses. With an estimated population size of 6000 SMMEs in the SEDA database and using a 95% confidence level, our estimated required sample size is 907. This was also based on a 2% margin of error and an 80% response rate from the respondents. A stratified technique was used to produce our sample. In order to achieve the required sample size based on the expected response rate, we requested over 1200 respondents to participate in the survey. The business sector in the survey is divided into six categories: manufacturing, mining, agriculture, services, construction, and others. Out of a total of 940 respondents, services had the highest number of entrepreneurs, 501 (52.3%), followed by agriculture, 303 (31.6%), others, 62 (6.5%), construction, 43 (4.5%), manufacturing, 19 (2%), and finally, mining, 12 (1.3%). This justifies why this study focuses on services only, since it remains the highest.

The target population was divided into three strata, with samples drawn from each stratum for the survey. For this research, we used a two-part structured questionnaire. The survey’s first segment covered demographics and business types, while the second half focused on business success factors relevant to this study’s scope. All research protocols were followed. A section on the questionnaire also asked for participants’ informed consent, as well as information about their rights, the data storage method, and other protections. The Cronbach Alpha test (internal consistency) and the test–re-test reliability approach (questionnaire trustworthiness assessment) both provided good results of 0.70 and 0.875, respectively. SPSS version 25.0 was used to analyze the data. The analyses are divided into three sections: descriptive, assumption tests, and ANOVA results. A 5% level of significance was used to establish the degree of significance

4. Results and Discussion

The results of a one-way ANOVA can be considered reliable as long as the following assumptions are met. The response variable residuals are normally distributed (or approximately normally distributed), and the variances of populations are equal.

Table 2. Descriptive statistics (business success score).

					95% Confidence Interval for Mean
	N	Mean	Std. Deviation	Std. Error	Lower Bound	Upper Bound	Minimum	Maximum
Low-Capital Startup <= 7000	213	19.93	3.643	0.250	19.44	20.42	11	25
Medium-Capital Startup = 7001–22,700	123	20.94	3.209	0.289	20.37	21.52	13	25
High-Capital Startup = 22,701+	144	21.73	2.880	0.240	21.25	22.20	14	25
Total	480	20.73	3.402	0.155	20.42	21.03	11	25

below shows the descriptive statistics for the business score index. In this instance, all the assumptions were met, thus making the results valid and robust (see results of homogeneity of variance and robust test of equality of means in

Table 3. Statistics.

a.
Test of Homogeneity of Variances
		Levene Statistic	df1	df2	Sig.
Business Success Score	Based on mean	2.185	2	477	0.114
	Based on median	2.207	2	477	0.111
	Based on median and with adjusted df	2.207	2	449.845	0.111
	Based on trimmed mean	2.231	2	477	0.109
b.
Robust Tests of Equality of Means
	Statistic a	df1	df2	Sig.
Welch	13.498	2	293.642	0.000
Brown-Forsythe	13.707	2	446.381	0.000
a. Asymptotically F distributed.
c.
ANOVA (Business Score)
Between Groups	285.809	2	142.904	12.967	0.000
Within Groups	5256.983	477	11.021
Total	5542.792	479

a,b).

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. Post hoc (multiple comparisons).

(I) Capital Startup Amount (Binned)	(J) Capital Startup Amount (Binned)	Mean Difference (I-J)	Std. Error	Sig.	Lower Bound	Upper Bound
Low-Capital Startup <= 7000	Medium-Capital Startup = 7001–22,700	−1.014 *	0.376	0.020	−1.90	−0.13
	High-Capital Startup = 22,701+	−1.800 *	0.358	0.000	−2.64	−0.96
Medium-Capital Startup = 7001–22,700	Low-Capital Startup <= 7000	1.014 *	0.376	0.020	0.13	1.90
	High-Capital Startup = 22,701+	−0.786	0.408	0.132	−1.74	0.17
High-Capital Startup = 22,701+	Low-Capital Startup <= 7000	1.800 *	0.358	0.000	0.96	2.64
	Medium-Capital Startup = 7001–22,700	0.786	0.408	0.132	−0.17	1.74
Dependent variable: business success score.
Tukey HSD.

*. The mean difference is significant at the 0.05 level.

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. Homogeneous subsets (business success score).

Tukey HSD a,b
Capital Startup Amount (Binned)	N	1	2
Low-Capital Startup <= 7000	213	19.93
Medium-Capital Startup = 7001–22,700	123		20.94
High-Capital Startup = 22,701+	144		21.73
Sig.		1.000	0.099
Means for groups in homogeneous subsets are displayed.

^a. Uses harmonic mean sample size = 151.750. ^b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

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. Case processing summary.

	Valid		Cases Missing		Total
	N	Percent	N	Percent	N	Percent
Govt Support Score (Binned) * Capital Startup Amount (Binned)	481	96.0%	20	4.0%	501	100.0%
Business Plan Score (Binned) * Capital Startup Amount (Binned)	481	96.0%	20	4.0%	501	100.0%
Customer and Market Access Score (Binned) * Capital Startup Amount (Binned)	477	95.2%	24	4.8%	501	100.0%

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. Govt Support Index * capital startup amount crosstabulation.

		Low-Capital Startup <= 7000		Medium-Capital Startup = 7001–22,700		High-Capital Startup = 22,701+		Total
		N	%	N	%	N	%	N	%
Govt Support Score	(Low Govt Support)	158	74.2%	71	57.3%	53	36.8%	282	58.6%
	(High Govt Support)	55	25.8%	53	42.7%	91	63.2%	199	41.4%
Total		213	100.0%	124	100.0%	144	100.0%	481	100.0%

Note: $1 = R17.

,

Table 8. Business Plan Index * capital startup amount crosstabulation.

		Low-Capital Startup <= 7000		Medium-Capital Startup = 7001–22,700		High-Capital Startup = 22,701+		Total
		N	%	N	%	N	%	N	%
Business Plan Index	(Unstructured Business Plan)	142	66.7%	46	37.1%	50	34.7%	238	49.5%
	(Structured Business Plan)	71	33.3%	78	62.9%	94	65.3%	243	50.5%
Total		213	100.0%	124	100.0%	144	100.0%	481	100.0%

and

Table 9. Customer and market access score * capital startup amount crosstabulation.

		Low-Capital Startup <= 7000		Medium-Capital Startup = 7001–22,700		High-Capital Startup = 22,701+		Total
		N	%	N	%	N	%	N	%
Customer and Market Access Score	(Low CMA Base)	135	64.6%	73	58.9%	69	47.9%	277	58.1%
	23+ (High CMA Base)	74	35.4%	51	41.1%	75	52.1%	200	41.9%
Total		209	100.0%	124	100.0%	144	100.0%	477	100.0%

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. Chi-Square Tests.

Govt Support Score
	Value	df	Asymptotic Significance (2-Sided)
Pearson Chi-Square	39.856 a	3	0.000
Likelihood Ratio	40.080	3	0.000
Linear-by-Linear Association	23.920	1	0.000
N of Valid Cases	501
a.s The projected count of 0 cells (0.0 percent) is less than 5. The predicted minimum count is 11.26.
Business Plan Score
Pearson Chi-Square	14.707 a	3	0.002
Likelihood Ratio	14.906	3	0.002
Linear-by-Linear Association	6.419	1	0.011
N of Valid Cases	501
a. The projected count of 0 cells (0.0 percent) is less than 5. The predicted minimum count is 13.37.
Customer and Market Access Score
Pearson Chi-Square	9.359 a	3	0.025
Likelihood Ratio	9.303	3	0.026
Linear-by-Linear Association	1.997	1	0.158
N of Valid Cases	497
a. The projected count of 0 cells (0.0 percent) is less than 5. The predicted minimum count is 11.14.

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.

5. Conclusions

A well-structured business plan for SMEs should outline the market research conducted to analyze the profitability of the business, marketing, and sales strategies and financial projections, competition, records, amounts of financing requested, and how the money will be used. In addition, a good business plan would help obtain better participation and commitment from SMEs who will carry out the plans and improve outcomes. This will reduce the risk of failure risk, adapt operations and strategies to an evolving reality, and finally make SMEs’ objectives and plans consistent. This article compared the variations in business success for three different levels of Capital startups and appraised them with contextual factors (government support/business plans/access). The findings of this study are all supported by Ogujiuba et al. (2021c), Fatoki (2014), and Ngek (2016). These scholars all agree that capital is a key catalyst to the success of any business venture. Similarly, Maziriri et al. (2018), Brixiova et al. (2020), and Marimuthu and Singh (2021) show that business plans are related to initial capital invested. This lends credence to the pivotal role of capital in business success.

According to Kato and Germinah (2021), 63% of the respondents in their study confirmed the positive impact of the government’s involvement in enhancing the development of early-stage firms. Considering these results, the higher percentage suggests commendable success and the growth of enterprises. This study confirms that financing is a precursor for the quicker growth of early-stage enterprises.

The findings of this study are expected to guide the government on the best policy direction for strengthening the activities of the service-providing sector of the economy through better access to startup finance and capital adequacy in the face of the current COVID-19 pandemic that has ravaged the global economy. However, this study is limited to only Mpumalanga province. Because of the small size of the sample, compared with South Africa as a whole, the findings of this study do not represent the general outlook in the country. Thus, comparing what works in Mpumalanga vis-à-vis other provinces in South Africa with similar characteristics could be an interesting future research topic. In addition, future studies should consider using a two-way ANOVA for the dynamic effects on the dependent variable. In addition, investigating the effects of not having the necessary training and business planning could be an interesting area for future research.

Recommendations

• Governments at all levels are therefore encouraged to create conducive environments that will allow the service-providing sector accessibility to external loans. This can be achieved through less restrictive borrowing procedures by reappraising the existing fiscal framework of the government. In addition, enough education, especially in business planning design, is needed by the operators of this sector of the economy;
• To strengthen the activities of the service sector of the economy, the government of South Africa is further enjoined to review existing guidelines in the areas of matching grants, SMEs linkage programs, support for exports to the service sector, training, and possible tax simplification;
• SME finance should leverage e-lending platforms, alternative data for credit decisions, e-invoicing, e-factoring, and supply chain financing to improve credit infrastructure (credit reporting systems, secured transactions, and collateral registries). Funds raised from the different categories of finance providers can suit any of the medium and high groups for a startup. This is expected for developing economies, as the same medium startup capital may serve the same purpose for high startup seed. Notwithstanding, the initial capital structure goes a long way in ensuring the survival of any SME;
• There is, therefore, a need for SMEs wanting to raise startup capital to create a solid business plan or build a prototype for selling their ideas. Developing a business plan with adequate training could, therefore, be a needed challenge to contend with before any SME commences operation.