Assessing the Comprehensiveness of Managerial Support for SMMEs in South Africa

Abstract

In South Africa, small enterprise development is at the top of the government agenda. However, a significant issue lies in the type of skills and support necessary to develop SMMEs through the various phases of the business cycle. This study addresses a knowledge gap regarding whether SMME support interventions comprehensively address all managerial functions as per the P-O-L-C model. Guided by the Resource-Based Theory (RBT), and the Human Capital Theory (HCT), data was collected from 350 SMMEs in the Eastern Cape province using a structured self-administered questionnaire. Multiple regression analyses revealed that non-financial support from both the public (R² = 0.089, p = 0.215) and private (R² = 0.161, p = 0.207) sectors was not significantly associated with SMME success. Furthermore, while private sector support explained 14.8% (R² = 0.148, p < 0.001) and public sector support 7.6% (R² = 0.076, p < 0.001) of the variation in meeting SMME needs, support in key functional areas remains fragmented and poorly targeted. These findings highlight a systemic disconnect between the supply and demand sides of the SMME support ecosystem. To address this, this study proposes an integrative support model that aligns RBT and HCT within the P-O-L-C managerial framework, ensuring phase-appropriate, function-specific support. This framework departs from prior applications by reconceptualizing managerial support not as a generic intervention, but as a strategically sequenced process aligned with the business lifecycle. The model contributes a new lens for theorizing support efficacy and offers practical guidance for more targeted intervention design. This framework offers both theoretical and practical contributions toward improving the design and implementation of business interventions in South Africa.

1. Introduction

In South Africa, the unemployment rate has escalated over the last few years due to the downsizing of a lot of industries. There is a lot of evidence that shows how small-, medium-, and micro-enterprises (SMMEs) influence employment creation, the redistribution of income, the growth of domestic product, poverty reduction, and many other economic growth variables across all economies (Mxunyelwa & Vallabh, 2025; Enaifoghe & Vezi-Magigaba, 2023). SMMEs are critical to the socio-economic development of South Africa, accounting for over 60% of employment and contributing significantly to the GDP (Bvuma & Marnewick, 2020; Manzoor et al., 2021).

The National Development Plan (NDP) 2030, adopted in 2012, outlines the South African government’s long-term socio-economic goals. Central to the NDP is the goal of creating 11 million jobs by 2030 with SMMEs playing a critical role in driving inclusive economic development. The NDP calls for improved support mechanisms and the strengthening of entrepreneurial ecosystems. It further breaks down SMMEs’ role in enhancing growth in the economy through the creation of employment, poverty alleviation, the redistribution of income, and the removal of inequalities (Xulu, 2025; Bowmaker-Falconer & Meyer, 2022; Dladla & Mutambara, 2022). There is evidence that shows that SMMEs have contributed to the reduction in unemployment by approximately 6%. Given the continuous rise in the levels of unemployment, it is vital to offer relevant SMME business support to address these challenges in South Africa (Xulu, 2025; Mashavira et al., 2022; Manzoor et al., 2021).

In response, several SMME support programs and policies have been launched to support the growth of the SMME sector. These include the Small Enterprise Development Agency (SEDA), which offers non-financial support such as training, mentorship, and business development services; the National Empowerment Fund (NEF) and the Industrial Development Corporation (IDC), which provide financial assistance; and targeted initiatives like the Black Business Supplier Development Programme (BBSDP) and the Youth Enterprise Development Strategy, aimed at addressing historical disparities and promoting entrepreneurship among disadvantaged groups (Mxunyelwa & Vallabh, 2025; Xulu, 2025). Yet, despite years of targeted policy interventions, South Africa continues to experience one of the highest SMME failure rates among developing economies, estimated between 60% and 80% within the first three years of operation (Mhlongo & Daya, 2023; Matekenya & Moyo, 2022).

While a lot of SMME support programs and policies exist to address the success rate of a business, it remains unclear whether small businesses acquire enough support in all functional areas of management (Weilbach, 2025; Sethwana & Ramukumba, 2024; Enwereji, 2023; Alkahtani et al., 2020). However, the continued instability and high failure rates of SMMEs raise questions about the effectiveness, targeting, and relevance of these interventions (Weilbach, 2025; Rungani, 2022). As such, this study explores whether the support provided to SMMEs is comprehensive across these functional areas and how the support influences the SMME’s capability to overcome challenges. For this study, the functional areas of management will be defined based on the P-O-L-C model, which describes the key managerial functions as planning, organizing, leading, and controlling. The research findings will provide us with valuable insights into the relevance and success of the current SMME support mechanism, as well as identify any potential gaps that need to be addressed as we foster a more favorable SMME business environment. There is a lot of evidence that shows that SMMEs are key to economic growth, job creation, and innovation across all economic sectors.

Despite their contribution, SMMEs face a lot of challenges, such as access to support structures, limited resources, and inadequate business skills, which hinder businesses from achieving sustainable growth. A significant amount of effort has been made to promote and enhance the development of small businesses in South Africa (Mxunyelwa & Vallabh, 2025; Chukwuneme et al., 2023). However, besides all these support programs and laws and strategies, a lot of SMMEs continue to fail and to be unstable due to a lot of the challenges they face (Mhlongo & Daya, 2023; Dladla & Mutambara, 2022; Botha et al., 2021). The SMME failure rate in South Africa falls between sixty and eighty percent in the first few years after inception. These results confirm that there is a high failure rate in South Africa as compared with other similar economies in developing countries (Mxunyelwa & Vallabh, 2025; Mhlongo & Daya, 2023; Lose & Mapuranga, 2022; Matekenya & Moyo, 2022).

While many studies have examined the effectiveness of SMMEs’ support interventions, much of the existing literature has approached SMME support from a supply-side perspective, emphasizing the availability of programs rather than their relevance or fit with the specific managerial needs of the business from the recipient’s viewpoint. There is still a limited understanding of whether support is targeted, comprehensive, or strategically aligned with how businesses operate and grow. In addition, prior research often treats SMME support as uniform, failing to differentiate between the functional areas of management or the stages of business development (Lose & Mapuranga, 2022; Ezennia & Mutambara, 2022). As such, the question remains: Do support programs offer the right type of support, at the right time, and across all critical management functions?

Therefore, this study responds to these critical gaps by assessing the comprehensiveness of managerial support across the P-O-L-C framework from a demand-side perspective. This study moves beyond prior descriptive studies and goes a step further by not only examining whether support exists, but also whether it is functionally and temporally appropriate, using an integrative framework that combines the Resource-Based Theory and the Human Capital Theory. In doing so, this study contributes to the design of SMME interventions that are context-sensitive, business-life-cycle appropriate, and strategically sequenced.

Although a lot of studies have looked at the impact of SMME business interventions and support, it will not be sufficient to rely on these results, considering the complexities and the changes that have occurred over time across all the sectors (Dekel-Dachs et al., 2021; Bowmaker-Falconer & Meyer, 2022). Most studies have looked at the numerous categories of small business support interventions in the South African context from the supply side; however, few studies have looked at the relevance of these support interventions from the demand side. Hence, the key aim of this study was to address these overlooked dimensions by investigating the comprehensiveness and alignment of managerial support provided to SMMEs in South Africa. In contrast to prior studies, this research adopts a demand-side lens, gathering insights directly from SMMEs across the Eastern Cape province.

Given the alarming portion of SMMEs whose businesses fail within three years of inception, it was key to explore those factors that lead to the SMME failure and poor performance. SMME failure is a very important research area as it contributes to the development and implementation of the relevant SMME support policies and procedures for management (Weilbach, 2025; Sethwana & Ramukumba, 2024; Enwereji, 2023). Thus, this research examined the significance of SMME business support in line with the functional areas of business. This research seeks to move beyond generic policy prescriptions by offering empirical and theoretical contributions that address the why, what, and how of SMME support grounded in both contextual realities and management theory.

2. Theoretical Framework

This study draws on an integrated theoretical framework that combines the Resource-Based Theory (RBT) and the Human Capital Theory (HCT), embedded within the P-O-L-C (planning, organizing, leading, and controlling) managerial model, to offer a novel lens on how managerial support interventions can enhance SMME performance in South Africa.

The Resource-Based Theory (RBT) argues that for any business to grow and be sustainable, it needs to have resources and capabilities that will influence that business’s growth. This theory provides a basis for identifying suitable measures for SMMEs to overcome barriers to business growth. It emphasizes the need for SMMEs to have a sustainable stakeholder management approach to help the business realize the required resource capacity, which is significant for their survival and success (Weilbach, 2025; Sibiya et al., 2023; Ogujiuba et al., 2023). In the context of SMMEs, external business support programs such as funding, infrastructure, training, and mentorship can be viewed as strategic resources that, if aligned with the specific needs of the enterprise at different stages of development, can enhance performance, innovation, and sustainability (Lose, 2021).

The Human Capital Theory (HCT) complements the Resource-Based perspective by conceptualizing individual capabilities as a core productive asset that can be developed through investment in training and learning. This theory brings in the viewpoint of capabilities, which are also explained in the RBT. This theory argues that codified and explicit knowledge skills can be transferred. According to the theory, SMMEs and entrepreneurs can develop their knowledge through training, personal experience, and education (Xulu, 2025; Mokoena & Liambo, 2023; Bamata & Phiri, 2022). Even though this theory applies to the SMME context, it is important to note that while skills and knowledge may be transposable, they are not easy to transfer since they are linked to capabilities within a particular business. In the context of this study, business support that targets all managerial functions (POLC) represents an investment in the development of human capital and can significantly improve the enterprise’s ability to adapt and compete.

While the RBT focuses on “what” (resources) the HCT focuses more on who leverages these resources and how they develop the capability to do so effectively. As such, this research argues that the combined application of these theoretical viewpoints with the key managerial functional areas will yield positive results in the development of SMMEs. It is imperative to note that for any business support intervention to work successfully, it needs to be introduced at the right time in line with the business development stage needs. The application of the two theories cannot be separated since they both look at the accessibility and utilization of specific resources and the role that business support and intervention play as agents of sustaining SMMEs through the creation of a favorable environment for their sustained growth. As such, to make the support that SMMEs receive relevant, it should be introduced in line with the specific managerial functional area sequence of planning, organizing, leading, and controlling. This is the contribution to the body of knowledge emanating from this paper.

In South Africa, the importance of investing in the development of SMMEs has been expressed by many key players. There is a growing commitment to the fostering and promotion of entrepreneurship across all sectors (Majadibodu et al., 2023; Ezennia & Mutambara, 2022; Botha et al., 2021). In a bid to address the SMME failures, new SMME development structures are being explored to foster SMME growth. Notwithstanding all these efforts, SMME failure is still very high. Some of the reasons for failure being cited are the non-integration of business management, financial management, and accounting practices as a way of creating an all-inclusive managerial capacity for their business. In the literature, there is evidence that identifies three general categories for explaining the reasons for SMME failure (Mxunyelwa & Vallabh, 2025; Chukwuneme et al., 2023; Ezennia & Mutambara, 2022).

• Resources and opportunities as a point of view for explaining SMME failure.
• Business management expertise as an explanation of SMME failure.
• Multiple origins/causes of failure as an explanation of SMME failure (Rungani, 2022; Sheik & Kader, 2022).

The literature on SMME support also highlighted that one of the major causes of the ineffectiveness of SMME support initiatives is based on the type of support given to small businesses, which does not essentially separate between the various business phases such that they deliver tailor-made support interventions appropriate to that specific business growth stage. This study argues that the specific stage in the business cycle of an SMME should determine the nature of the support needed. In support of this argument, other authors also argue that there is a need to group businesses into pre-venture businesses and established businesses before defining their specific needs, as they differ (Molope et al., 2025; Ezennia & Mutambara, 2022; Ncube & Zondo, 2022; Rungani, 2022; Sheik & Kader, 2022).

This study further argues that business support must be given according to the functional areas, and it adopts the four management functional areas proposed by Du Toit et al. (2010). This scholar argues that planning is one of the significant managerial functions that occurs at different levels in a business. SMMEs need to have enough knowledge and skills to assist them in laying down business goals (Molope et al., 2025; Lose & Mapuranga, 2022; Kuhlase, 2022). The existing evidence in the literature indicates that SMMEs have a challenge in sufficiently providing a strategic plan for their business, which in turn affects the sustainability of their businesses (Mxunyelwa & Vallabh, 2025; Ramokgopa, 2022).

In line with this argument, authors such as Lose and Mapuranga (2022) and Kuhlase (2022) highlighted that, notwithstanding the higher failure rates of SMMEs in South Africa, there is evidence which shows that a lot of these SMMEs do not consider strategic management as a necessary skill. Some research on why SMMEs do not consider strategic management as a necessity shows that SMMEs are discouraged due to having inadequate time for planning, the absence of specialized expertise, and having insufficient knowledge of the planning processes (Ogujiuba et al., 2023; Lose & Mapuranga, 2022; Kuhlase, 2022). This paper argues that to have a sustainable way of improving SMMEs’ success, they need skills to assist them in planning at different growth phases of their business.

Evidence from the literature shows that there is unanimity amongst scholars on the role that is played by SMMEs in the economy. Despite their contribution, SMMEs are still facing obstacles that inhibit their growth, and there is a need to shift how support is given to SMMEs, as their needs are not uniform due to the different growth phases in which they operate. The SMME support is there; however, how it is delivered must be determined by the specific needs for their success rate in South Africa. A key argument that is seen in the different views in the literature review is that there is no one-size-fits-all solution to the problems that SMMEs experience, as they have different challenges that are unique to their business growth stage (Weilbach, 2025; Sethwana & Ramukumba, 2024; Enwereji, 2023).

International Perspectives on SME Support Alignment

While many South African studies critique the inefficiency and uniformity of public SMME support programs, these limitations are also echoed globally. For example, Battistella et al. (2023) examined technology transfer services in European SMEs and found that support mechanisms are only effective when tailored to the enterprise’s maturity level. They emphasize the critical role of intermediary institutions in adapting services based on organizational life-cycle stages, a challenge also observed in the South African context where most interventions are blanket in nature.

In addition, Landjohou (2025) offers a comparative review of SME policies across Africa, Asia, and Latin America. His study identifies similar structural weaknesses in public support systems, including rigid program design, lack of feedback loops, and misalignment with SME needs. To address this, Landjohou proposes an “adaptive and stage-based” support model that incorporates enterprise diagnostics to inform policy timing and content. These international insights reinforce the core argument of this study: that SMME interventions in South Africa must be both life-cycle-sensitive and function-specific to achieve meaningful impact.

3. Materials and Methods

My research approach was motivated by the nature of the research problem at hand; therefore, a post-positivist research approach was employed. The research remained independent from the phenomena being researched to enhance objectivity. The target population was formally registered small businesses in the Eastern Cape province within all six districts and two metropolitan municipalities. A sample size of 350 SMMEs was used, using the sample frame acquired from the Eastern Cape Development Corporation Agency (ECDC). The justification for the choice of this population was that these SMMEs were registered and were directly involved in support programs. The research instrument was developed using measurement scales identified in the literature review.

To collect data for this study, a survey was used with a structured self-administered questionnaire, which was distributed across the province. Stratified sampling was used as a sampling technique, where the strata were developed according to the provinces’ number of districts and metropolitan municipalities. In each stratum, simple random sampling was used to select the participants. The questionnaire was self-administered and distributed by field workers under the supervision of the researcher. The researcher only adopted measurement scales with a Cronbach’s Alpha greater than 0.70 to ensure internal validity. A thorough literature review was performed during the development of the research instrument, followed by a pilot study.

To evaluate the appropriateness of the research instrument, the researcher used a statistician and a panel of experts. A statistician was used to assist the process of data analysis, and the Statistical Package for Social Sciences (SPSS) version 30.0 was used in the process of data analysis. Descriptive and inferential statistics were used to summarize and describe the data obtained from the survey. A written ethical clearance was attained from Northwest University through the Human Sciences Research Ethics Committee (HSREC); the study approval number was NWU-00473-16-A9. All ethical guidelines were followed, such as obtaining written informed consent from the respondents before participation. The researcher also observed confidentiality by not revealing the identities of the respondents. To address the self-reporting bias, respondents were assured of the anonymity and confidentiality of their responses to encourage honesty; in addition, the research used validated constructs in the research instrument.

Reliability Tests

To measure the research instrument’s overall reliability for the study, it was assessed using Cronbach’s Alpha test. The researcher adopted measurement scales with a Cronbach’s Alpha greater than 0.70. To ensure internal validity, a thorough literature review was performed during the development of the research instrument, followed by a pilot study. However, two items with a Cronbach’s Alpha of 0.68 were also used, as they were newly developed measurement scales specifically developed for this study, which clarifies their lower scores, though they were close to 0.70. Moreover, the scale comprises only a few items, which can result in a modest Alpha value.

4. Results

The following sections will provide the results of this study obtained from the data analysis.

4.1. Descriptive Statistics

From this study’s target population, the response rate was 96.57%, with SMMEs from the seven districts of the Eastern Cape province represented. This was a good response rate to ensure correct and reliable survey results. Demographic descriptor data was used to test the normality, and the researcher used a histogram. The results showed a normal distribution, which implied that most of the scores lay around the center of the distribution. The results also showed that the majority of the respondents in the study were owner-managers operating in rural and semi-urban areas.

Table 1. Reliability tests.

Construct/Domain	No. of Items	Sample Questions	Reliability (Cronbach’s α)
Business Performance Indicators	8	6	0.815
Entrepreneurial Support Services	26	10	0.877
Perceived Effectiveness of Government Support	7	15	0.682
Perceived Effectiveness of Private Support	9	15	0.680
Outcome Tracking and Evaluation Mechanisms	4	16	0.873

gives a summary of the reliability tests conducted on the scales used in this study.

4.1.1. Statistical Assumptions

Prior to conducting regression analyses, the data were assessed for key statistical assumptions. Normality was tested using histograms and Q-Q plots, confirming an approximately normal distribution of residuals as shown in Figure 1. Multicollinearity was examined through Variance Inflation Factor (VIF) values, all of which were below the threshold of 5, indicating no significant multicollinearity. Homoscedasticity was visually assessed via residual plots and no major violations were found. These diagnostics confirm the suitability of the data for linear regression analyses.

4.1.2. SMME Needs and Expectations

Figure 2 shows that the majority of SMMEs who participated in the survey were managed by their owners and employed between 6 and 50 permanent employees. Due to the nature of the province, most SMMEs were in rural and semi-urban areas. The findings indicated that the majority of SMMEs who participated in the survey were owner-managed.

To ascertain the existence of a gap between the needs and expectations of SMMEs, the respondents were asked to highlight their specific needs and expectations. Figure 3 provides a summary of the needs and how they were ranked by the SMMEs.

From the findings of this survey, the top five most important needs as per the SMME ranking were financial support, accounting skills, business plan writing, and business management. These results support the RBT and the results from other scholars, such as Weilbach (2025), Ezennia and Mutambara (2022), and Mboweni (2022), indicating that financial support is one of the most important SMME needs. To fully address the key aim of this study, the researcher needed to have a full understanding of the types of business support needed for SMMEs to succeed. In the context of this study, business support will include financial and non-financial support offered by government entities and those entities in the private sector. This question also helped the researcher to ascertain if a gap exists between what SMMEs need and what they acquire from a demand-side perspective.

The findings from the survey indicate that SMMEs need support in areas such as the provision of financial assistance, accessing funding opportunities, business skills training, and access to the available support services. These findings align with the core resource categories as defined in the RBT, indicating persistent gaps in foundational business resources. The results are also consistent with studies such as Weilbach (2025), Xulu (2025), and Rungani (2022), who also used the RBT and concluded that for small businesses to succeed, they need resources in the form of skills, knowledge, and finances. This argument is also reinforced by the Human Capital Theory, which also suggests the importance of human capital development in a business.

4.1.3. Do SMMEs Receive Training in All Functional Areas?

This question was asked to determine the comprehensiveness of managerial support for SMMEs in South Africa from all sectors. In the South African context, both the private and public sectors provide SMMEs with support, which includes financial and non-financial support. The results are presented in Figure 4.

From the survey results as presented in Figure 4, the majority of SMMEs revealed that the support they received from all the sectors did not cover all functional areas from all sectors. To be specific, 60% of SMMEs highlighted that the government was not providing support in all functional areas. The SMMEs indicated almost the same results, with 55% indicating that the private sector support received did not cover all key functional areas needed by the business. Despite years of structured support initiatives, SMME needs remain unmet, suggesting poor targeting or ineffective delivery. These findings also reflect the Human Capital Theory perspective: although support may be offered to SMMEs, the codified knowledge is not effectively transferred or internalized by the targeted recipients.

These findings are also critical within the P-O-L-C framework, showing that without providing phase-appropriate support across planning, organizing, leading, and controlling, interventions become fragmented. In addition, the results also reflect the RBT critique that resource provision in silos fails to create a sustained competitive advantage. An alternative hypothesis could be that support services focus more on compliance and registration tasks rather than core operational and strategic needs.

These findings validate one of the major challenges in the process of developing and crafting SMME support, where a gap still exists in terms of the relevancy of the support given to SMMEs in line with their business development stage and the specific functional areas of support needed (Sethwana & Ramukumba, 2024; Munnik, 2021). Another gap that was also identified was that in some instances, both sectors tended to duplicate the same types of support they provided to small businesses. This validates the argument proposed this study for both sectors to work in synergy to address the duplication of support services (Rungani, 2022; Alkahtani et al., 2020).

4.1.4. Meeting SMMEs’ Expectations Adequately

This question assisted in answering the question of the existence of a gap between SMME needs and expectations. The findings are presented in Figure 5 and from the findings, it is very clear that SMME expectations are not met in both sectors. This shows a clear indication of the existence of a gap between the expectations of small business owners/managers and the business development support they have received. These findings are in line with Weilbach (2025), Xulu (2025), and Meyer et al. (2022), who investigated the factors that impede SMME success, and this validated the argument that there is a mismatch that exists between the demand and supply sides of the SMME development ecosystem.

The mismatch shown in Figure 5 confirms a disconnect between the supply and demand sides of the SMME ecosystem. While support is to be provided from all sectors, its form and timing appear to be misaligned with the business development stages. This is consistent with the Human Capital Theory, which emphasizes that knowledge must be contextually relevant and must be delivered when the enterprise is ready to internalize and apply it. Possible reasons for this mismatch may be external factors such as rigid program design, bureaucratic inertia, and poor feedback loops in intervention planning.

4.1.5. SMME Level of Success

From

Table 2. Level of business success.

Descriptor		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1 (Not successful)	12	3.6	3.6	3.6
	2	14	4.1	4.1	7.7
	3	1	0.3	0.3	8.0
	4	5	1.5	1.5	9.5
	5	209	61.8	61.8	71.3
	6	89	26.3	26.3	97.6
	7 (Very successful)	8	2.4	2.4	100.0
	Total	338	100.0	100.0

it shows that the SMME success level reported was consistent with the other responses on similar questions, which assessed the success of SMMEs. Participants rated their business success, and from the results above, no SMME owner/managers rated their businesses at the lowest level of success; only a small portion (8.05%) rated their businesses’ success between levels 1 and 3. From the results, it can be noted that most rated their businesses at level five of success (61.8%), with some also selecting level six (26.3%).

Although many of the respondents rated their businesses as moderately successful, this self-assessment did not correlate strongly with the support indicators. This suggests that other factors, such as informal networks, founder resilience, or luck, may compensate for ineffective formal support. This highlights the need to incorporate broader socio-economic and behavioral variables in future research.

4.2. Hypothesis Testing

The next section focuses on the testing of the research hypothesis, with the outcomes for each presented below.

H_01a. 

There is no significant relationship between non-financial support given by the public sector and SMME success.

H_1a. 

There is a significant relationship between non-financial support given by the public sector and SMME success.

H_01b. 

There is no significant relationship between non-financial support given by the private sector and SMME success.

H_1b. 

There is a significant relationship between non-financial support given by the private sector and SMME success.

From

Table 3. Correlation results: Hypothesis (H_01a and H_01b).

	Private Sector				Public Sector
Model	Unstandardized Coefficients		t	Correlation with SMME Success	Unstandardized Coefficients		t	Correlation with SMME Success
	B	Std. Error			B	Std. Error
(Constant)	1.979 ***	0.341	5.799		2.136 ***	0.287	7.447
Advice on raising finance	0.043	0.098	0.445	−0.529	−0.137	0.109	−1.251	−0.464
Advice on accessing new markets	0.128	0.079	1.611	0.105 *	0.279 *	0.104	2.693	0.664
General business planning	0.267	0.105	2.536 *	0.353	−0.032	0.097	−0.330	−0.632
Resource allocation	0.027	0.102	0.262	−0.453	−0.058	0.106	−0.547	−0.566
Financial management	−0.088	0.090	−0.975	−0.581	0.053	0.104	0.506	0.411
Operation management	0.027	0.108	0.249	−0.435	0.013	0.100	0.133	−0.322
Marketing management	−0.044	0.105	−0.418	−0.657	−0.069	0.109	−0.633	−0.608
Entrepreneurship training	−0.110	0.112	−0.985	−0.487	0.011	0.087	0.123	−0.724
Business planning writing course	0.169	0.111	1.526	0.220	−0.076	0.102	−0.746	−0.774
Resource allocation training	−0.035	0.085	−0.411	−0.454	0.010	0.093	0.109	−0.623
Managerial control training	−0.091	0.102	−0.894	−0.582	−0.004	0.098	−0.042	−0.642
Leadership training	−0.051	0.105	−0.485	−0.562	−0.072	0.102	−0.702	−0.511 *
Self-management training	0.062	0.085	0.723	−0.411	0.038	0.093	0.416	0.505
Financial planning training	−0.014	0.031	−0.461	−0.628	0.043	0.097	0.442	−0.457
Business ethics	0.020	0.033	0.608	0.617	−0.170	0.096	−1.782	−0.145 *
Business networking	−0.157	0.085	−1.853	−0.489	−0.140	0.107	−1.301	−0.128 *
Product development training	0.106	0.087	1.220	0.613	−0.085	0.101	−0.841	−0.111 *
Start-up coaching	−0.065	0.082	−0.801	−0.762	0.139	0.094	1.482	0.411
Business incubation	0.008	0.108	0.074	−0.633	−0.037	0.111	−0.336	−0.333
Business expansion mentoring	−0.048	0.087	−0.547	−0.833	0.084	0.091	0.922	−0.233

* p < 0.05 *** p < 0.001.

the correlation analysis, the results show a negative relationship between some non-financial support variables and SMME success. From the private sector support received, the results show that variables such as marketing information (−0.694), and advice on accessing new markets (−0.105) were negatively correlated with SMME success, suggesting that these types of support do not significantly contribute to SMMEs’ success.

Similarly, support from the public sector also showed negative correlations between SMME success and many non-financial variables: leadership training (−0.511), generic business start-up advice (−0.128), product development (−0.111), business ethics (−0.145), and business networking (−0.128). These results indicate that non-financial support from the public sector does not significantly enhance SMME success. From the results, we do not reject the null hypothesis because the p-values for both sectors are greater than 0.05, the private sector is 0.207, and the public sector is 0.215, indicating that no significant relationship exists between support from both sectors and SMME success. This is in line with studies such as Rungani (2022) and Alkahtani et al. (2020), who argue that there is a mismatch between the support that is given to SMMEs and their needs.

Table 4. Multiple regression model (H_01a and H_01b).

Model Private Sector		Sum of Squares	df	Mean Square	F	Sig.	R	R Square	Std. Error of the Estimate
1	Regression	14.442	25	0.578	1.234	0.207	0.401	0.161	0.684
	Residual	146.067	312	0.468
	Total	160.509	337
Public Sector
2	Regression	14.342	25	0.574	1.225	0.215	0.299 a	0.089	0.684
	Residual	146.167	312	0.468
	Total	160.509	337

“a” indicates the model includes a constant (intercept) term.

presents result of the multiple regression for hypothesis (H_01a and H_01b). The findings from the correlation and multiple regression analyses initially suggest a statistically significant relationship between the functionality score of interventions and SMME performance. However, the regression analysis ultimately indicates that this relationship is not statistically significant within the context of the model used. These results align with those reported in previous studies, highlighting the complexity of measuring the true impact of intervention functionality on SMME outcomes. These results are consistent with studies performed by Molope et al. (2025), Rungani (2022), and Alkahtani et al. (2020).

Hypothesis 2a and 2b.

Testing for public sector business intervention.

The multiple regression analysis yielded non-significant results for both the private and public sectors. Results for the private sector produced R² = 0.16, F 1.234, and p = 0.207 (greater than the 0.05 significance threshold), indicating that even though the private sector non-financial support predictors explained a 16% variation in SMME success, this relationship was not statistically significant.

Similarly, in the results for the public sector, the model showed an R² of 0.89, F = 1.226, and a p-value of 0.215 (>0.05), indicating that non-financial support from the public sector accounted for only 8% of SMME success variation, making this relationship not statistically significant. In both cases, the null hypothesis is not rejected, as the p-values exceed 0.05. This implies that support from either the private or public sector does not have a statistically significant effect on the success of SMMEs.

Although these findings are not in line with some previous studies performed in South Africa, it is key to indicate that one of the main reasons for not having a significant relationship may be attributed to the fact that the support the SMMEs are receiving from all sectors does not necessarily match their needs and expectations (Rungani, 2022; Alkahtani et al., 2020). It is important to note that even though support is being provided, if it is not the right support, then it will not yield the intended result.

H_02a. 

Public sector business interventions do not align with the needs of SMMEs.

H_2a. 

Public sector business interventions align with the needs of SMMEs.

H_02b. 

Private sector business interventions do not align with the needs of SMMEs.

H_2b. 

Private sector business interventions align with the needs of SMMEs.

In

Table 5. Correlation: Hypothesis (H_02a and H_02b).

	Private Sector				Public Sector
Model	Unstandardized Coefficients		t	Correlation	Unstandardized Coefficients		t	Correlation
	B	Std. Error			B	Std. Error
(Constant)	2.408 ***	0.461	5.224		2.021 **	0.582	3.472
Satisfied with financial access	0.191 *	0.069	2.774	0.156 **	−0.157	0.087	−1.806	−0.095 *
I have access to different types of business training	−0.230 ***	0.053	−4.363	−0.314 ***	0.263 ***	0.067	3.943	0.236 ***
Have access to business information	−0.085	0.063	−1.337	−0.237 ***	−0.125	0.080	−1.566	0.063
Have managerial skills due to assistance	0.010	0.062	0.153	−0.184 ***	0.053	0.079	0.671	0.100 *
Have access to various types of technological support	−0.081	0.068	−1.186	−0.182 ***	0.119	0.086	1.386	0.136 **
Training on selected functional areas has been received	0.162	0.103	1.571	0.041	0.036	0.130	0.276	0.043
Have received training in all functional areas	0.151	0.103	1.460	0.095 *	−0.012	0.130	−0.092	−0.014

* p < 0.05 ** p < 0.01 *** p < 0.001.

the correlation analysis indicated mixed results across the public and private sectors. Results from the private sector showed a positive correlation between SMME needs and access to diverse training (p-value of 0.236) and managerial support skills (p-value of 0.100). These results suggest that these types of interventions are aligned with SMME requirements. However, negative correlations also emerged between variables such as SMMEs’ needs and satisfaction with financial support (correlation coefficient of −0.095) and training received across all functional areas (correlation coefficient of −0.014), indicating the failure of this intervention in addressing the SMME-specific needs.

Findings in the public sector indicated positive correlations between variables such as SMME needs and satisfaction with financial support (p-value of 0.156), access to diverse business training (p-value of 0.315), and training received in all functional areas (p-value of 0.095). This indicates that the interventions received from the public sector effectively addressed SMME needs. However, the results also showed negative correlations between SMME needs and access to business information (p-value of −0.237) and managerial skill support (p-value of −0.1840). These findings are consistent with the results from studies such as Weilbach (2025); Sethwana and Ramukumba (2024); Enwereji (2023); and Alkahtani et al. (2020), who argue that the specific interventions provided by both the government and private sector did not adequately address SMME needs and expectations.

The multiple regression analysis yielded significant results for both sectors. For the private sector, the model showed R² = 0.148, F = 8.177, and p < 0.001, indicating that business intervention predictors from private sector institutions explain 14.8% of the variation in SMME needs. For the public sector, results showed R² = 0.076, F = 3.857, and p < 0.001, meaning that public sector business intervention predictors account for 7.6% of SMME needs variation. In both cases, the null hypothesis is rejected, as the p-values are below 0.05 (p < 0.001), indicating a statistically significant relationship between business interventions delivered in the private and public sectors and SMME needs. These results align with earlier studies conducted by Lose and Mapuranga (2022), Kelly et al. (2021), and Lose (2021).

Hypothesis 3a.

Examining the Impact of the Intervention’s Functionality Score.

H_03a. 

There is no statistically significant association between SMME performance and the functionality score of the intervention.

H_3a. 

There is a statistically significant association between the SMME performance and the functionality score of the intervention.

The correlation analysis examining the impact of the intervention’s functionality score from both sectors revealed a negative but non-significant correlation (p = −0.054). This finding indicates that the functionality score of the SMME interventions does not have a meaningful impact on SMME performance. In other words, how functional these interventions are does not appear to significantly influence how well SMMEs perform. These findings align with previous research by Molope et al. (2025); Rungani (2022); and Alkahtani et al. (2020).

The multiple regression analysis examining the impact of the intervention’s functionality score from both sectors yielded R² = 0.054, F = 0.968, and p = 0.326. This indicates that the functionality score of interventions explains only 5.4% of the variation in SMME performance. Since the p-value (0.326) exceeds the significance threshold (0.05), we do not reject the null hypothesis. This confirms there is no statistically significant relationship between intervention functionality score and SMME performance.

These conclusions are in alignment with previous research by Molope et al. (2025), Rungani (2022), and Alkahtani et al. (2020), who found that SMME support typically does not address all functional business areas. This result is also consistent with earlier hypothesis testing regarding functional areas.

Hypothesis 3b.

Testing for functionality scores of SMME interventions (H03b).

H_03b. 

The functionality scores of SMME interventions differ between public and private sector institutions.

H_3b. 

The functionality scores of SMME interventions are consistent across both public and private sector institutions.

The correlation analysis in

Table 6. Correlation results: Hypothesis (H_03b).

Model		Unstandardized Coefficients		t	Correlation
		B	Std. Error
1	(Constant)	2.028 ***	0.067	30.298
		−0.047	0.036	−1.327	−0.072

*** p < 0.001.

revealed a significant negative correlation (p = −0.72). This strong negative correlation indicates that the functionality scores of SMME interventions differ substantially between the public and private sectors. In other words, these findings demonstrate that the two sectors do not provide the same level or type of functional support to SMMEs, with a clear inverse relationship between their respective functionality scores. These results align with previous research conducted by Rungani (2022) and Alkahtani et al. (2020).

The multiple regression analysis as shown in

Table 7. Multiple regression model: Hypothesis (H_30b).

Model		Sum of Squares	df	Mean Square	F	Sig.	R	R Square	Std. Error of the Estimate
1	Regression	0.098	1	0.098	1.760	0.186 b	0.872 a	0.405	0.236
	Residual	18.719	336	0.056
	Total	18.817	337

a. Model includes a constant (intercept) term. b. Not statistically significant at the 0.05 level.

examines the functionality score differences between public and private sector SMME interventions yielded R² = 0.405, F = 1.760, and p = 0.186. This indicates that 40.8% of the variation between public and private sectors can be explained by the functionality score of SMME interventions. Since the p-value is 0.186, greater than the 0.05 significance level, we do not reject the null hypothesis. These results indicate a statistically significant difference in the functionality scores of SMME interventions between the public and private sectors, suggesting that the two sectors implement interventions with varying levels of effectiveness. These results align with previous research conducted by Molope et al. (2025), Rungani (2022), and Alkahtani et al. (2020), who likely found similar sectoral differences in SMME support functionality.

5. Discussion

5.1. Hypotheses H_01a and H_01b

After testing H_01a and H_01b, no statistically significant relationship was found between non-financial support from all sectors and SMME success. Rather than interpreting this as a lack of impact, this may reflect that the support being provided is poorly timed and not aligned to the business life-cycle stage. The results may also indicate that interventions lack content relevance or practical applicability. The findings may also indicate external barriers such as market saturation, load shedding, and crime, which may dilute the potential gains from the support. These interpretations suggest that the current support models may not fully operationalize the RBT and HCT principles. Instead of rejecting their value, it calls for a redesign to create support programs that are more dynamic and participatory.

5.2. Hypotheses H_02a and H_02b (Alignment with Needs)

The findings show significant results between some of the support variables, such as access to diverse training and managerial support, which were positively correlated with SMME needs. However, the results also showed negative correlations with variables such as training in all functional areas, which raises red flags. These mixed outcomes suggest that while support is available, its blanket nature may overwhelm, missing the nuanced requirements of different enterprises. This supports the need for a Support Alignment Tool as proposed later in this study to systematically map P-O-L-C needs against growth stages.

These mixed outcomes in

Table 8. Summary of results.

Hypothesis	Decision
H01a. There is no significant relationship between non-financial support given by the public sector and SMME success.	Do not reject
H01b. There is no significant relationship between non-financial support given by the private sector and SMME success.	Do not reject
H02a. Public sector business interventions do not align with the needs of SMMEs.	Reject
H02b. Private sector business interventions do not align with the needs of SMMEs.	Reject
H03a. There is no statistically significant association between SMME performance and the functionality score of the intervention.	Do not reject
H03b. The functionality scores of SMME interventions differ between public and private sector institutions.	Do not reject

suggest that while support is available, its blanket nature might overwhelm or miss the nuanced requirements of different enterprises. This supports the need for a Support Alignment Tool (SAT), as proposed later in this paper, to systematically map POLC needs against growth stages.

5.2.1. Functionality Score (H_03a)

Despite its intended purpose, the functionality score of the interventions showed no significant association with SMME success. One possible explanation is that functionality assessments focus on program design, not program delivery or SMME readiness. Future evaluations should separate these dimensions.

5.2.2. Functionality Score Variations (H_03b)

There was a significant difference in the functionality scores between sectors. This validates the concern that private and public sectors are duplicating efforts and delivering uneven support. This fragmentation reduces the synergistic value of interventions. It further supports calls for cross-sectoral collaboration and unified impact-investment metrics.

The presence of negative or non-significant correlations between support variables (e.g., leadership training, marketing information) and SMME success may appear counterintuitive. However, from the Human Capital Theory perspective, this suggests that while training is provided, it may not be timely, context-specific, or effectively internalized, thereby reducing its real-world impact. Similarly, the Resource-Based Theory posits that possession of a resource (such as support) alone does not confer advantage unless the resource is valuable, rare, and appropriately utilized. The negative associations could therefore indicate that interventions are perceived as irrelevant, burdensome, or duplicative, especially when offered outside of the appropriate phase in the business lifecycle.

This interpretation is reinforced by the international literature. For instance, Battistella et al. (2023) demonstrate that in European contexts, technology transfer services only support SME growth when tailored to the firm’s maturity level. Similarly, Landjohou (2025), in his comparative review of SME policy in Africa, Asia, and Latin America, highlights the superior outcomes of support models that apply enterprise diagnostics and lifecycle profiling. These models allow interventions to be adaptive, rather than one-size-fits-all. In contrast, South Africa’s SMME support system remains largely standardized, bureaucratically driven, and reactive—a situation worsened by overlapping mandates and poor inter-institutional coordination.

What distinguishes the South African context further is the rural and semi-urban concentration of many SMMEs, particularly in regions like the Eastern Cape. These businesses face infrastructural deficits, digital exclusion, and limited access to market information, structural barriers that magnify the inefficiencies of blanket interventions. Even when support is delivered, the managerial absorptive capacity is constrained by uneven educational backgrounds, informal sector dynamics, and limited entrepreneurial ecosystems. These conditions weaken the ability to internalize training or strategically utilize support, a gap that RBT and HCT both caution against.

The findings of this study hold that for SMME development to succeed, support that is given must be in line with the business development phase to ensure that the support is relevant and addresses the unique needs of each business. Therefore, it is important to provide support that addresses all the managerial functional areas of planning, organizing, leading, and controlling. This finding validates the evidence in the literature, which indicates that a gap exists between the support that is given to SMMEs and their specific business needs. There is a mismatch in the timing of the support, as it does not address all the functional areas of business, which are vital to the development and success of the business. This study restructured the business management model to design SMME support mechanisms, utilizing the P-O-L-C framework, as shown in Figure 6.

By embedding the P-O-L-C framework into a sequenced, stage-sensitive support model, this study contributes a theoretical advancement that is generalizable in logic but contextually grounded in execution to the literature. Unlike diagnostic studies that merely catalog weaknesses in support delivery, this research offers a conceptual bridge between strategic intent (resource and capability theories) and operational implementation (managerial functionality), a gap that remains underexplored in both the global and South African literature.

Ultimately, this comparative lens reveals that while South Africa’s SMME policy landscape shares structural limitations with other developing economies, its institutional rigidities and fragmented support delivery mechanisms demand a uniquely adaptive and functionally sequenced response. This further validates the framework proposed here, positioning it as both a tool for theoretical development and a platform for context-sensitive policy innovation.

This study proposes an integrative framework that bridges the RBT and HCT theoretical perspectives with the core functional areas of management: planning, organizing, leading, and controlling (P-O-L-C). The study demonstrates that the transformation of human and strategic resources into sustained SMME performance is facilitated by the application of a sound management process. Thus, this study adds conceptual depth to Resource-Based Theory (RBT) and Human Capital Theory by introducing managerial functionality as a missing link between possessing resources and leveraging them effectively for entrepreneurial success.

Figure 6 presents the proposed integrative framework that redefines the P-O-L-C managerial model through the lenses of Resource-Based Theory (RBT) and Human Capital Theory (HCT). Rather than treating the P-O-L-C components as static managerial categories, the framework sequences them across the business lifecycle: start-up, growth, maturity, and decline—acknowledging that the relevance and utility of each function shifts depending on a firm’s stage.

For example, during the start-up phase, planning and organizing are paramount, as firms establish their strategic direction and basic structures. In contrast, in the growth phase, leading becomes critical to scaling operations and managing people. The maturity phase places more weight on controlling, monitoring systems, performance, and compliance. By mapping support interventions across this structure, the framework provides a diagnostic tool that moves beyond generic capacity building, ensuring that support is both stage-sensitive and function-specific.

From the proposed Integrated SMME Support Framework, the author infused the need for SMME support into each functional area. The foundation of the proposed framework is the understanding that as businesses expand, SMMEs must receive support that meets the needs of the business according to its growth stage. The public and private sectors must develop support and training packages that focus on the different functions, in line with the P-O-L-C model, to have positive results.

Applying support in the proper P-O-L-C sequence ensures that foundational elements are set before moving to action and refinement. Skipping steps can lead to disorganization, poor decision-making, and wasted resources. By aligning support with both business phase and functional area, stakeholders can deliver meaningful, timely interventions that accelerate SMME development and sustainability (Molope et al., 2025; Rungani, 2022; Alkahtani et al., 2020).

This framework makes three theoretical advancements:

• Sequencing P-O-L-C dynamically, linked to SMME lifecycle stages.
• Embedding functional support within resource and capability development, using RBT and HCT as anchoring theories.
• Transforming the P-O-L-C model from a descriptive taxonomy to a prescriptive guide for policy and program design.

As such, the framework not only contributes an Integrated SMME Support Framework but a reconceptualization of managerial functionality in constrained, high-failure environments like South Africa’s SMME ecosystem. It invites future research and policy experimentation into adaptive, rather than static, models of managerial support.

5.3. Theoretical Implications

This study makes three core contributions to the theory, particularly at the intersection of strategic resource use and operational management in the context of SMMEs.

Firstly, this study advances Resource-Based Theory (RBT) and Human Capital Theory within its specific context of SMME development by operationalizing them through a structured managerial lens of the P-O-L-C framework. Although both theories provide valuable insights, with the Resource-Based Theory emphasizing the role of firm-specific resources in competitive advantage and the Human Capital Theory emphasizing the value of knowledge, skills, and capabilities, neither of these theories has been largely applied at a strategic level. This study bridges the gap between these strategic orientations and the practical realities of day-to-day management by showing how resource deployment and capability development must be functionally and temporally sequenced to be effective.

Secondly, this study reconceptualizes the P-O-L-C model as a dynamic, stage-sensitive structure rather than a static taxonomy of managerial roles. In doing so, it proposes a sequenced, lifecycle-based interpretation of managerial functionality. This reframing allows the P-O-L-C model to serve not only as a descriptive framework but also as a theoretical engine for structuring external interventions, capability development, and strategic alignment within SMMEs.

Third, this study introduces a hybrid theoretical framework that demonstrates how resource possession (RBT) and capability-building (HCT) only result in competitive advantages when functionally activated through appropriately timed managerial processes. This provides a new conceptual bridge between high-level strategic theories and ground-level management practice that is relevant in resource-constrained and failure-prone environments like those facing many SMMEs.

This study advances the theoretical debate on entrepreneurial development by revisiting the longstanding question: Can entrepreneurs be made? This study argues that support mechanisms, particularly non-financial support interventions, such as business training and mentorship, must be theoretically grounded in a strategic sequencing of the business processes (POLC) to be effective (Molope et al., 2025; Rungani, 2022; Alkahtani et al., 2020). This perspective moves beyond the traditional binary of financial vs. non-financial support and introduces a process-oriented theoretical lens, highlighting the importance of timing, relevance, and alignment between support and business development stages.

In doing so, this study reframes the role of external support, not merely as input provision but as a strategic enabler that activates internal capabilities through structured management practices. This offers a more dynamic and context-sensitive interpretation of both the Resource-Based Theory (RBT) and Human Capital Theory, particularly for policy formulation and support programs in developing economies.

This effectively synthesizes this study’s significance and contributions regarding SMME development support. This research addresses the critical issue of high SMME failure rates by examining the relevance of private and public sector support interventions. This study acknowledges the importance of understanding business malfunctioning to develop effective SMME policies. It highlights the need for ongoing evaluation of support interventions due to changing business landscapes, noting that past research findings may not remain relevant over time (Mxunyelwa & Vallabh, 2025; Enaifoghe & Vezi-Magigaba, 2023). By grounding intervention logic in a theoretically sequenced managerial framework, this study enables policy design that is both empirically informed and conceptually robust.

5.4. Managerial Implications

The literature has shown that both the public and private sectors are investing in the development of SMMEs. However, there is a gap in how the support is given by both the private and public sectors (Molope et al., 2025; Rungani, 2022; Alkahtani et al., 2020). To address this, a participatory support design framework is proposed, where SMMEs are directly involved in co-designing the support they receive. This implies regular needs assessments, collaborative planning, and adaptive implementation models that evolve alongside the business growth stages should be conducted.

Additionally, collaboration among the private sector, higher education institutions, consulting firms, and the public sector is essential. Rather than duplicating efforts, these stakeholders should leverage their respective strengths to share responsibilities effectively. This approach would enhance efficiency and ensure the optimal utilization of resources across both public and private sectors (Molope et al., 2025; Rungani, 2022; Alkahtani et al., 2020). Support for SMMEs should shift from being a one-size-fits-all intervention to a developmental investment strategy. This requires the reclassification of public and private sector interventions as impact investments, with key performance indicators (KPIs) tied to measurable business outcomes such as employment creation and turnover growth. To improve the return on investment, post-intervention monitoring of SMME performances must be institutionalized.

This study indicates a need to include SMMEs in the development of support initiatives and to monitor and evaluate the performance of SMMEs after intervention to enable their success, which in turn will allow both private and public sectors to attain the return on investment through SMMEs plowing back into the SMME sector. The development of SMMEs must not be seen as an expenditure but rather treated as an investment (Mxunyelwa & Vallabh, 2025; Enaifoghe & Vezi-Magigaba, 2023).

A practical solution will also be to develop a Support Alignment Tool (SAT) which gives a diagnostic checklist that allows support providers to map their offerings against the POLC needs for each enterprise. This can ensure timely targeted interventions, reducing resource wastage and increasing the likelihood of SMME success.

The South African government should continue fostering a supportive environment for entrepreneurship and the growth of small-, medium-, and micro-enterprises (SMMEs), as this plays a crucial role in job creation. Beyond financial assistance, such as loans, the government should extend its support to include training programs and opportunities for entrepreneurs to build valuable networks.

This study also calls for a multi-stakeholder implementation model. The public sector, private sector, academia and SMMEs must adopt a shared accountability mechanism, such as performance-linked memorandum of understanding where each party’s contribution is defined and reviewed against clear developmental targets. This will eliminate duplication, encourage innovation, and ensure that resources are directed to where they are needed most.

5.5. Policy Relevance and International Alignment

The policy recommendations arising from this study—specifically the integration of the P-O-L-C model with RBT and HCT—find strong resonance in international best practices. The concept of adaptive, phase-based intervention has already been operationalized in parts of Europe and Latin America. For instance, the adaptive model proposed by Landjohou (2025) prioritizes business diagnostics and growth stage profiling before any public support is deployed. Similarly, Battistella et al. (2023) emphasize the importance of the intermediary customization of services based on firm development stages, reinforcing this study’s call for context-aware support ecosystems. Incorporating these models offers South African policymakers a comparative framework to re-engineer SMME programs with empirical grounding and global validation.

5.6. Areas of Future Research and Limitations of the Study

Future studies may focus on the effectiveness of managerial support within specific industries which allows research to identify tailored strategies that can improve the sustainability and competitiveness of SMMEs. We also need studies that will explore the role of collaborative models between government, academia, and the private sector in providing holistic and scalable managerial support structures. This study was limited to the Eastern Cape province in South Africa and the results cannot be generalized. This study relied on self-assessments by respondents, which may have been subject to biases such as overestimation of managerial capabilities or underreporting of deficiencies in the support received.