Examining the Role of Rural Entrepreneurship in Enhancing Economic Resilience in Mnquma Local Municipality, Eastern Cape

1. Introduction

Economic resilience in rural areas of developing economies depends largely on adaptive strategies that address persistent challenges, such as poverty, unemployment, and dependence on traditional sectors like agriculture. Rural entrepreneurship has increasingly been recognised as a strategy, as it promotes the utilisation of local resources, innovation, and small-scale enterprise development to stimulate economic activity in non-urban settings (Asmit, Simatupang, Rudito & Novani, 2024). In South Africa, particularly within the Eastern Cape Province, rural entrepreneurship is viewed as a mechanism for employment creation, income diversification, and community empowerment in regions characterised by severe socio-economic inequalities (Makhoba, 2024; Rulashe & Ramolobe, 2024a). Entrepreneurial activities in sectors such as agriculture, retail, tourism, and food services contribute to local economic participation and may strengthen communities’ ability to withstand economic shocks and structural instability (Tshikovhi, More & Cele, 2023a; Thukral, 2025).

Despite these potential benefits, a major concern in the literature is whether rural entrepreneurship can meaningfully enhance economic resilience within structurally disadvantaged municipalities that continue to experience weak infrastructure, limited market access, financial exclusion, and inadequate institutional support (Högberg & Mitchell, 2023; Ngumbela, 2023). This challenge is particularly evident in Mnquma Local Municipality in the Eastern Cape, where high unemployment, low industrial development, and persistent poverty continue to constrain local economic growth and sustainable livelihoods (Giwu, Mdoda & Ntlanga, 2024; Kativhu, Mpongwana & Cishe, 2024). Although entrepreneurial initiatives exist within township and rural economies, many enterprises remain vulnerable to business instability and limited economic integration (Medina, Garfias & Tellez, 2023). Consequently, uncertainty remains regarding the extent to which rural entrepreneurship contributes to long-term economic resilience in rural municipalities.

The consequences of this problem are significant for both policy and practice. Weak entrepreneurial sustainability contributes to continued poverty, youth migration, low household income, economic dependency, and limited local economic diversification (Hasan, Shahid, Sultana & Siddiqui, 2023; Thukral, 2025). Furthermore, rural enterprises’ inability to withstand economic shocks undermines municipal efforts to create jobs and alleviate poverty. Although previous studies have broadly explored rural entrepreneurship and economic resilience, limited empirical research has examined the multidimensional relationship among employment generation, innovation and diversification, community engagement, local supply chain development, and economic resilience within rural municipalities of the Eastern Cape (Mbukanma, Sithole & Hosu, 2025). In addition, existing studies have paid limited theoretical attention to integrating Entrepreneurship Development Theory and Resilience Theory to explain how rural entrepreneurship strengthens economic resilience in underdeveloped rural economies. This creates both an empirical and theoretical gap in understanding the mechanisms through which rural entrepreneurship contributes to sustainable local economic resilience.

Against this background, the present study investigates the role of rural entrepreneurship in enhancing economic resilience in Mnquma Local Municipality. The study is justified by the need to generate localised empirical evidence to inform policymakers, development agencies, and rural stakeholders in designing targeted interventions that strengthen entrepreneurial ecosystems and promote sustainable rural development. By examining key entrepreneurial dimensions such as employment generation, innovation, community engagement, and local supply chain development, the study contributes to academic discourse and policy development relating to rural economic resilience in South Africa.

2. Literature Review

2.1. Challenges Faced by Rural Entrepreneurship in Enhancing Economic Resilience

Rural entrepreneurship has increasingly been recognised as an important mechanism for promoting economic development and enhancing economic resilience in developing economies. The existing literature indicates that rural enterprises contribute to employment creation, innovation, local economic participation, and income diversification, thereby reducing dependence on traditional sectors such as subsistence agriculture (Tshikovhi, Shokane & Mashamaite, 2023b; Chidakwa & Khanare, 2024; Sugiardi, 2024). Through entrepreneurial activities, rural communities are better positioned to adapt to economic shocks, including market instability, climate-related disruptions, and broader macroeconomic uncertainties (Habib, Ariyawardana & Aziz, 2023; Mbukanma et al., 2025). Furthermore, the adoption of digital technologies and innovative business models has improved the ability of some rural enterprises to access wider markets, enhance efficiency, and remain competitive in changing economic environments (Ediagbonya & Tioluwani, 2023). Despite these positive contributions, literature highlights an important contradiction: the developmental potential of rural entrepreneurship is often undermined by persistent structural and institutional constraints within rural economies.

One of the most significant challenges identified in the literature is limited access to finance, as many rural entrepreneurs struggle to secure funding due to inadequate collateral, poor credit histories, and exclusion from formal financial systems (Ediagbonya & Tioluwani, 2023). Inadequate infrastructure, including poor road networks, unreliable electricity supply, and weak digital connectivity, further increases operational costs and limits market access, thereby reducing the competitiveness and sustainability of rural enterprises (Gigaba, Niyitunga & Uwizeyimana, 2025; Mbukanma et al., 2025). Rural entrepreneurs also face restricted access to broader markets due to geographical isolation, weak distribution networks, and limited market information (Popa, Grasu Cadis & Popp, 2024). In addition, weak institutional support, ineffective implementation of entrepreneurial policies, skills shortages, and the persistence of informal business practices continue to constrain entrepreneurial growth and resilience (Thukral, 2025). Contextual and socio-cultural factors, including low social capital and fragmented community networks, also influence entrepreneurial behaviour and limit innovation and business expansion (Naguib & Barbar, 2025). Collectively, these challenges demonstrate that although rural entrepreneurship has considerable potential to strengthen economic resilience, its effectiveness remains constrained by complex interactions among financial, infrastructural, institutional, and socio-cultural barriers.

2.2. Opportunities Toward Advancing Rural Entrepreneurship in Enhancing Economic Resilience

Building on the challenges outlined in the literature, it indicates that although rural entrepreneurship is constrained by structural, institutional, and contextual barriers, it also offers significant opportunities to enhance economic resilience when supported by appropriate interventions. Rural entrepreneurship provides an important pathway for employment creation, livelihood diversification, and local economic participation, particularly in areas characterised by high unemployment and limited formal economic opportunities (Tshikovhi et al., 2023b; Ahmad, Audi & Ahmad, 2025). By creating alternative income-generating activities, rural enterprises reduce dependence on single income sources and strengthen household resilience to economic shocks and economic volatility (Habib, Ariyawardana & Aziz, 2023). Furthermore, strengthening local production systems and promoting value addition can stimulate local economic growth, reduce economic leakage and improve market participation within rural communities (Anene & Clement, 2024). Innovation and digital transformation also present important opportunities to overcome geographical isolation and infrastructural limitations, as technologies such as mobile platforms and e-commerce enable rural entrepreneurs to access broader markets, improve efficiency, and diversify into sectors such as agro-processing, tourism, and service industries (Nipo, Lily, Fabeil & Jamil, 2024). These developments enhance the adaptability and competitiveness of rural enterprises, thereby strengthening economic resilience.

In addition, the literature highlights that strong community networks, stakeholder collaboration, and effective institutional support can significantly enhance rural entrepreneurship’s contribution to sustainable development. Leveraging social capital through cooperative models, partnerships, and community-driven initiatives improves resource sharing, knowledge exchange, and entrepreneurial performance (Chen, Lin & Chen, 2025). Similarly, strengthening local supply chains and integrating rural enterprises into regional and national value chains can improve efficiency, reduce transaction costs, and expand market access (He, Fan & Fan, 2024). Governments and development agencies also play a critical role in unlocking these opportunities through targeted interventions such as improved access to finance, infrastructure development, entrepreneurship training, and public–private partnerships (Rashid, Anser, Shah, Nabi, Ahmad & Zaman, 2025). However, the literature emphasises that these opportunities are not automatic and depend on the extent to which barriers such as financial exclusion, infrastructural deficiencies, weak institutional support, and skills shortages are effectively addressed (Omweri, 2024). Consequently, coordinated and context-specific interventions are essential for strengthening the entrepreneurial ecosystem and enabling rural enterprises to contribute meaningfully to long-term economic resilience and sustainable rural development (Almeida & Daniel, 2025).

2.3. Theoretical Foundation

Building on the challenges identified and the opportunities outlined, this study adopts Entrepreneurship Development Theory and Resilience Theory as complementary frameworks to explain how rural entrepreneurship can enhance economic resilience despite existing structural and institutional constraints. These theories provide a multidimensional perspective on the limitations and transformative potential of rural enterprises in developing economies.

Gcume & Mohapeloa (2025) highlight barriers such as limited access to finance, weak infrastructure, and institutional inefficiencies. Guzman, Murray, Stern & Williams (2024) demonstrate that these challenges can be mitigated through innovation, policy support, and strengthened entrepreneurial ecosystems. The selected theoretical frameworks for this research, therefore, explain not only how entrepreneurial activities generate economic value but also how they enable rural communities to adapt, survive, and grow in uncertain environments.

2.3.1. Entrepreneurship Development Theory

Entrepreneurship Development Theory explains how entrepreneurial activities contribute to economic growth through innovation, opportunity recognition, and resource mobilisation (Fañanás-Biescas, Ključnikov, Bargoni & Ferraris, 2026). The theory highlights that constraints such as limited access to finance, skills shortages, inadequate infrastructure and weak institutional support hinder entrepreneurial development and restrict the sustainability and growth potential of rural enterprises. Without adequate human capital, financial resources, and enabling policy frameworks, entrepreneurial activities are likely to remain informal, small-scale and vulnerable to economic instability. At the same time, the theory emphasises that strategic investments in entrepreneurship training, financial inclusion, infrastructure development, and supportive institutional systems can create enabling environments that allow rural entrepreneurs to innovate, diversify, and expand their businesses (Olalekan, 2024; Thukral, 2025). In the context of this study, Entrepreneurship Development Theory provides a relevant framework for understanding how strengthening entrepreneurial capacity and addressing systemic barriers can enhance employment creation, value addition, market expansion, and ultimately economic resilience within Mnquma Local Municipality.

2.3.2. Resilience Theory

Resilience Theory explains the ability of systems, communities, and enterprises to absorb shocks, adapt to changing conditions, and recover from disruptions such as economic instability, market uncertainty, and environmental risks (Cui, Wang & Zheng, 2025). The theory emphasises that resilience is strengthened through adaptive strategies such as diversification, innovation, collaboration, and continuous learning, which enable rural economies to sustain development under changing conditions (Nosike, Ojobor & Nosike, 2024; Capoani, Fantinelli & Giordano, 2025). In this study, these resilience dimensions are directly reflected in the examined entrepreneurial constructs. Adaptation is reflected through innovation and diversification, which enables rural enterprises to explore new markets, products, and income opportunities. Economic diversification and employment generation strengthen household and community income stability, thereby reducing vulnerability to economic shocks. Collective action is reflected through community engagement and local supply chain development, where collaboration among entrepreneurs, communities, and local stakeholders enhances resource sharing, market access, and local economic coordination (Saputra & Havlíček, 2024).

Together with Entrepreneurship Development Theory, Resilience Theory provides a comprehensive framework for explaining how rural entrepreneurship contributes to economic resilience in Mnquma Local Municipality. While Entrepreneurship Development Theory focuses on value creation through entrepreneurial activity, Resilience Theory explains how entrepreneurial processes enhance the adaptability, sustainability, and recovery capacity of rural economies. Collectively, the theories suggest that strengthening entrepreneurial capacity alongside institutional support, infrastructure, finance and policy interventions is essential for promoting sustainable economic resilience in rural municipalities.

3. Methodology

This study employed a quantitative research methodology to examine the role of rural entrepreneurship in enhancing economic resilience in Mnquma Local Municipality, Eastern Cape. Guided by the theoretical foundations underpinning the study, a causal (explanatory) research design was adopted to empirically test the relationships between key entrepreneurial variables and economic resilience (Sturm, Hohenstein & Hartmann, 2023). This design was considered appropriate for examining how entrepreneurial challenges and opportunities influence economic outcomes within rural contexts. A pilot study involving 24 rural entrepreneurs was conducted to assess the clarity, reliability, and validity of the research instrument. Feedback from the pilot phase was used to refine the questionnaire and ensure the instruments adequately captured constructs related to entrepreneurial constraints, innovation, community engagement, and economic resilience.

The target population consisted of registered rural entrepreneurs within Mnquma Local Municipality, identified through municipal SMME databases and records from the Department of Small Business Development and the Eastern Cape Department of Rural Development and Agrarian Reform (2024). A multi-stage sampling approach combining purposive and snowball sampling techniques was employed to access both formal and informal entrepreneurs, particularly those operating within hard-to-reach rural areas. Using the Raosoft sample size calculator, a sample of 349 respondents was determined to be statistically adequate at a 95% confidence level and a 5% margin of error. Data were collected using structured questionnaires on a five-point Likert scale from strongly disagree to strongly agree. The instrument measured key constructs derived from the literature, including employment generation, economic contribution, innovation and diversification, community engagement, local supply chain development, and economic resilience.

Data analysis was conducted using SPSS and SmartPLS 4.0. Analytical techniques included confirmatory factor analysis, Partial Least Squares Structural Equation Modeling (PLS-SEM), and path analysis to test the hypothesised relationships among the study variables. PLS-SEM was selected over covariance-based SEM because the study is prediction-oriented and exploratory, involving complex relationships among multiple latent constructs (Hair et al., 2024). Furthermore, PLS-SEM is suitable for studies with relatively small sample sizes and does not require strict assumptions of multivariate normality, making it appropriate for analysing rural entrepreneurship data characterised by heterogeneous responses and varying enterprise conditions. Reliability and validity were assessed using Cronbach’s alpha, composite reliability, and average variance extracted (AVE), confirming the robustness of the measurement model. Ethical clearance was obtained from the Walter Sisulu University Senate Research Ethics Committee (Protocol No. 28/2025/MBM/BME/4786), ensuring compliance with institutional and national research ethics standards.

4. Data Analysis and Discussion of Results

This section presents the analysis and discussion of the empirical findings concerning the role of rural entrepreneurship in enhancing economic resilience within Mnquma Local Municipality. Quantitative data collected from 349 respondents, comprising rural entrepreneurs, SME owners and managers, agricultural cooperatives, and local business associations, were analysed using SmartPLS version 4. In line with the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the analysis assessed reliability, convergent validity, discriminant validity, latent variable correlations, structural relationships, and model quality criteria, including R-square and F-square values. According to Hair et al. (2024), PLS-SEM is appropriate for exploratory studies that examine complex relationships among multiple constructs and prioritise prediction-oriented analysis. The findings are discussed in relation to the study objectives, existing empirical literature, and the theoretical foundations of Entrepreneurship Development Theory and Resilience Theory to determine how employment generation, economic contributions, innovation and diversification, community engagement, and local supply chain development influence economic resilience in rural communities.

4.1. Measurement of Reliability and Validity

Reliability refers to the extent to which a research instrument produces consistent, stable, and reproducible results across time, across different respondents, and within the instrument itself (Simkus, Coolen-Maturi, Coolen & Bendtsen, 2025). Consistent with the methodological approach outlined, the measurement model’s reliability and validity were assessed to ensure the robustness of the constructs derived from the literature. In accordance with established statistical standards, reliability was evaluated using Cronbach’s alpha and composite reliability, while convergent validity was assessed using the average variance extracted (AVE). Threshold values of 0.70 for composite reliability and 0.50 for AVE were used to determine adequacy and acceptance.

The results indicated excellent internal consistency across all constructs. Cronbach’s alpha values ranged from 0.915 for Local Supply Chain Development to 0.979 for Employment Generation, reflecting a high level of reliability. These findings suggest that the measurement items used to capture the key constructs associated with the opportunities and challenges discussed are both consistent and reliable. Notably, Employment Generation and Economic Resilience exhibited particularly strong reliability, underscoring their central significance within the study’s conceptual framework.

Composite reliability values, ranging from 0.917 to 0.983, further confirmed the internal consistency of all constructs and indicated minimal measurement error. In addition, AVE values ranging from 0.752 to 0.922 demonstrated strong convergent validity, confirming that the indicators effectively represented their respective constructs. Collectively, these results validate the robustness of the measurement model and support its suitability for subsequent structural analysis.

Table 1. Construct Reliability Test.

	Cronbach’s alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
Employment Generation	0.979	0.981	0.983	0.922
Economic Contributions	0.941	0.957	0.955	0.809
Innovation and Diversification	0.936	0.957	0.951	0.798
Community Engagement	0.934	0.939	0.950	0.792
Local Supply Chain Development	0.915	0.917	0.938	0.752
Economic Resilience	0.946	0.948	0.956	0.756

Note: All values exceed thresholds (α>0.9 excellent; ρ>0.7 acceptable; AVE>0.5 construct validity).

Table 1. Construct Reliability Test.

	Cronbach’s alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
Employment Generation	0.979	0.981	0.983	0.922
Economic Contributions	0.941	0.957	0.955	0.809
Innovation and Diversification	0.936	0.957	0.951	0.798
Community Engagement	0.934	0.939	0.950	0.792
Local Supply Chain Development	0.915	0.917	0.938	0.752
Economic Resilience	0.946	0.948	0.956	0.756

Note: All values exceed thresholds (α>0.9 excellent; ρ>0.7 acceptable; AVE>0.5 construct validity).

To achieve the objectives of this study, quantitative data were collected from 349 participants registered as rural entrepreneurs, SME owners and managers, local business associations, and agricultural cooperatives, of whom 63% were female (220 respondents) and 37% were male (129 respondents), with no missing data and perfectly aligned percentages. The predominance of female respondents is consistent with patterns observed in South African rural entrepreneurship, where women frequently dominate informal and small-scale enterprises due to limited formal employment opportunities and culturally embedded roles within household economies. SmartPLS version 4 software was employed to analyse the data, generating key statistical outputs including reliability and validity assessments, inter-construct correlations, structural equation modeling (SEM), path analysis, and model quality criteria such as R-square and F-square.

4.2. Discriminant Validity: Heterotrait–Monotrait Ratio (HTMT)

Discriminant validity was assessed using the HTMT criterion to ensure that the constructs identified, such as innovation, community engagement, and supply chain development, are empirically distinct. The results indicated that all HTMT values were below the recommended threshold of 0.85, confirming that each construct measures a unique dimension of rural entrepreneurship. These results are consistent with the theoretical distinctions outlined, in which Entrepreneurship Development Theory and Resilience Theory emphasise different yet complementary dimensions of economic resilience, as illustrated in Table 2.

Discriminant validity was assessed using the Heterotrait–Monotrait ratio (HTMT). As shown in Table 2, most HTMT values were within or close to the recommended threshold of 0.85, indicating acceptable discriminant validity among the constructs. However, a few relationships exceeded the threshold, particularly Employment Generation and Community Engagement (HTMT = 1.046), indicating a strong conceptual association between these constructs. Similarly, Economic Contributions and Community Engagement (HTMT = 0.912), Innovation and Diversification and Local Supply Chain Development (HTMT = 0.919), and Economic Resilience and Employment Generation (HTMT = 0.931) showed relatively high associations. In contrast, Economic Contributions and Local Supply Chain Development (HTMT = 0.599) and Economic Resilience and Local Supply Chain Development (HTMT = 0.625) demonstrated clearer discriminant validity. Overall, the HTMT results confirm that the measurement model achieved acceptable discriminant validity for further structural analysis.

4.3. Fornell–Larcker Criterion

The Fornell–Larcker criterion was applied to assess discriminant validity within the structural equation model (Rasoolimanesh, 2022). According to this criterion, the square root of the Average Variance Extracted (AVE) for each construct should be higher than its correlations with other constructs (Haji-Othman & Yusuff, 2022). As shown in Table 3, most constructs met this requirement, indicating acceptable discriminant validity. Innovation and Diversification (0.960), Economic Contributions (0.899), and Local Supply Chain Development (0.893) showed strong discriminant validity. However, Community Engagement had a higher correlation with Employment Generation (0.984) than its square root of AVE (0.869), suggesting some conceptual overlap between these constructs. Despite this, the overall results confirm that the measurement model demonstrated acceptable discriminant validity for further structural analysis.

4.4. Latent Variable Correlation Results

To determine the correlation between latent variables, you typically use Structural Equation Modeling (SEM) or Confirmatory Factor Analysis (CFA) to estimate the relationship between unobserved constructs based on their observed indicators (Steenkamp & Maydeu-Olivares, 2023). The correlation matrix revealed positive relationships among all constructs, indicating that variables such as employment generation, innovation, and community engagement are positively associated with economic resilience. These findings align with the opportunities explained, which highlight the role of these variables in strengthening rural economies. The absence of excessively high correlations suggests that multicollinearity is not a concern, thereby supporting the reliability of subsequent structural analysis.

Table 4. Correlation Matrix Results.

	Employment Generation	Economic Contributions	Innovation and Diversification	Community Engagement	Local Supply Community Development	Economic Resilience
Employment Generation	1.000	0.640	0.893	0.756	0.676	0.794
Economic Contribution	0.640	1.000	0.590	0.829	0.620	0.871
Innovation and Diversification	0.893	0.590	1.000	0.743	0.601	0.793
Community Engagement	0.756	0.829	0.743	1.000	0.863	0.985
Local Supply Community Development	0.676	0.620	0.601	0.863	1.000	0.837
Economic Resilience	0.794	0.871	0.793	0.985	0.837	1.000

Table 4. Correlation Matrix Results.

	Employment Generation	Economic Contributions	Innovation and Diversification	Community Engagement	Local Supply Community Development	Economic Resilience
Employment Generation	1.000	0.640	0.893	0.756	0.676	0.794
Economic Contribution	0.640	1.000	0.590	0.829	0.620	0.871
Innovation and Diversification	0.893	0.590	1.000	0.743	0.601	0.793
Community Engagement	0.756	0.829	0.743	1.000	0.863	0.985
Local Supply Community Development	0.676	0.620	0.601	0.863	1.000	0.837
Economic Resilience	0.794	0.871	0.793	0.985	0.837	1.000

4.5. Structural Equation Modeling (SEM)

Structural Equation Modeling (SEM) is a powerful, comprehensive statistical framework for testing and estimating complex causal relationships among variables (Ghaleb & Yaslioglu, 2024). SEM is widely used in social sciences, psychology, and education to analyse multivariate data by combining elements of factor analysis and multiple regression (El Jihaoui, Abra & Mansouri, 2025). Structural Equation Modeling (SEM) was employed to test the hypothesised relationships between rural entrepreneurship variables and economic resilience. SEM enables the simultaneous examination of multiple relationships, providing a comprehensive understanding of how the challenges and opportunities interact within the theoretical framework. Figure 1 indicates that the proposed model adequately explains the role of rural entrepreneurship in enhancing economic resilience, confirming the relevance of the selected variables and theoretical assumptions. A similar study by Bratha, Suardana & Arismayanti (2025) produced the same results, with PLS-SEM confirming that the model provided a strong explanation of economic resilience (R² = 0.991), indicating that approximately 99.1% of the variance in economic resilience was accounted for by the predictor variables. Among the constructs, community engagement emerged as the strongest predictor of economic resilience (β = 0.660), highlighting the critical role of community-driven initiatives in strengthening local economies. Local supply chain development also showed a positive, though weaker, effect (β = 0.045), suggesting a supportive but less dominant influence.

Economic contributions (β = 0.221) and innovation and diversification (β = 0.172) demonstrated moderate positive effects, indicating that both financial activities and innovative practices contributed meaningfully to resilience outcomes. In comparison, employment generation had a small negative effect on economic resilience (- 0.032), suggesting that economic outcomes alone may not directly enhance resilience without the support of other factors, such as community engagement and innovation. Also, strong relationships were observed among the independent constructs, particularly between community engagement and local supply chain development (β = 0.866) and between economic contributions and employment generation (β = 0.642). These interconnections highlight the integrated nature of rural entrepreneurship, in which multiple dimensions interact to shape overall economic resilience (Shao, Jiang & Xie, 2024). The findings confirmed the multidimensional impact of rural entrepreneurship on economic resilience. The results emphasised that community engagement and economic contributions were key drivers, while innovation played a complementary role. These insights provided strong empirical support for the study’s conceptual framework and highlighted important policy areas for promoting sustainable rural development (Gidage & Bhide, 2025).

4.6. Model Fit

Model fit refers to the extent to which the model-implied covariance matrix matches the observed data (West, Wu, McNeish & Savord, 2023). Common indices include SRMR (≤ 0.08 for good fit, ≤ 0.10 acceptable), NFI (≥ 0.90), and d_ULS, which should be below the HI95 or HI99 bootstrap threshold.

As shown in Table 5, SRMR values (0.113; 0.227) exceed acceptable thresholds and d_ULS values are high, indicating poor fitness. The chi-square statistic is infinite, and NFI is unavailable, limiting further assessment. Overall, the model shows limited fit and may require refinement. Hair et al. (2024) explore that this is justified, as PLS-SEM is prediction-oriented and less dependent on global fit measures, especially in exploratory studies. The small sample size and model complexity may also affect fit indices. Despite this, the model demonstrates acceptable reliability, validity, and predictive relevance, supporting the key relationships of the study.

Table 5. Model fit.

	Saturated model	Estimated model
SRMR	0.113	0.227
d_ULS	6.737	27.264
d_G	n/a	n/a
Chi-square	∞	∞
NFI	n/a	n/a

Table 5. Model fit.

	Saturated model	Estimated model
SRMR	0.113	0.227
d_ULS	6.737	27.264
d_G	n/a	n/a
Chi-square	∞	∞
NFI	n/a	n/a

4.7. Path Coefficients

Path analysis is a statistical technique used to examine causal relationships between variables by estimating direct and indirect effects within a system of equations (Nazir, Gillani & Shafiq, 2023). As a specific application of structural equation modeling (SEM), it uses path diagrams to represent relationships among observed variables. In this study, path analysis was conducted using PLS-SEM with bootstrapping to assess the strength and significance of the hypothesised relationships. The results indicate that all structural paths are statistically significant at the 0.05 level, confirming the model’s robustness. Employment generation shows a strong positive effect on economic contributions (0.642) but a weak negative direct effect on economic resilience (- 0.032), suggesting a possible indirect or mediating effect. Economic contributions significantly influence both innovation (0.593) and diversification (0.221), highlighting their dual role in the model.

Innovation and diversification strongly enhance community engagement 0.746 and moderately contribute to economic resilience (0.172). Community engagement emerges as the most influential predictor, with strong effects on local supply chain development (0.866) and economic resilience (0.660). Consistent with these findings, Opoku (2025) found that local supply chain development has a weak but statistically significant effect on resilience (0.045), indicating a limited direct contribution. Overall, the findings support the proposed model and emphasise the critical role of innovation and community engagement in strengthening economic resilience, while also revealing important indirect dynamics, particularly regarding employment generation.

Table 6. Path analysis results.

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P values
Employment Generation -> Economic Contributions	0.642	0.643	0.032	20.241	0.000
Employment Generation -> Economic Resilience	-0.032	-0.031	0.015	2.166	0.030
Economic Contributions -> Innovation and Diversification	0.593	0.595	0.028	20.854	0.000
Economic Contributions -> Economic Resilience	0.221	0.221	0.011	20.136	0.000
Innovation and Diversification -> Community Engagement	0.746	0.746	0.029	25.982	0.000
Innovation and Diversification -> Economic Resilience	0.172	0.171	0.012	14.562	0.000
Community Engagement -> Local Supply Chain Development	0.866	0.866	0.014	61.780	0.000
Community Engagement -> Economic Resilience	0.660	0.660	0.011	57.488	0.000
Local Supply Chain Development -> Economic Resilience	0.045	0.045	0.011	4.240	0.000

Table 6. Path analysis results.

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P values
Employment Generation -> Economic Contributions	0.642	0.643	0.032	20.241	0.000
Employment Generation -> Economic Resilience	-0.032	-0.031	0.015	2.166	0.030
Economic Contributions -> Innovation and Diversification	0.593	0.595	0.028	20.854	0.000
Economic Contributions -> Economic Resilience	0.221	0.221	0.011	20.136	0.000
Innovation and Diversification -> Community Engagement	0.746	0.746	0.029	25.982	0.000
Innovation and Diversification -> Economic Resilience	0.172	0.171	0.012	14.562	0.000
Community Engagement -> Local Supply Chain Development	0.866	0.866	0.014	61.780	0.000
Community Engagement -> Economic Resilience	0.660	0.660	0.011	57.488	0.000
Local Supply Chain Development -> Economic Resilience	0.045	0.045	0.011	4.240	0.000

4.8. Quality Criteria R-Square

R-square (R²) values represent the proportion of variance in dependent variables explained by the independent variables in the model, with higher values indicating stronger explanatory power. As shown in Table 7, the model demonstrates substantial explanatory power across all constructs. Rural entrepreneurship predictors collectively explain 99.1% of the variance in economic resilience (R² = 0.991; adjusted R² = 0.991) among the 349 SME respondents in Mnquma Local Municipality. Although this reflects very strong predictive capability, such an exceptionally high R² value should be interpreted with caution, as it may indicate overlapping constructs, shared variance, or redundancy among predictors rather than purely independent effects.

The minimal difference between R² and adjusted R² suggests that the model is not significantly overfitting, supporting its internal consistency and stability (Niazy, Murphy, Nadeem & Ricker 2025). Overall, the findings confirm the significant role of rural entrepreneurship-related factors in explaining economic resilience. However, the unusually high explanatory power underscores the importance of model parsimony and cautious generalisation within the PLS-SEM framework (Padovano & Ivanov, 2025).

The F-square statistic assesses the effect size of each variable construct by examining the change in R² when a predictor is removed from the model (Suleiman & Abdulkadir, 2022). According to established guidelines, f² values of 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively (Subhaktiyasa, 2024). This measure complements significance testing by indicating the practical contribution of each predictor.

As presented in Table 8, effect sizes vary considerably across the model. Community engagement shows an exceptionally large effect on economic resilience 4.178 and local supply chain development (f² = 3.005). Economic contributions also exert large effects on economic resilience (1.442) and on innovation and diversification (0.544). Similarly, innovation and diversification strongly influence community engagement (1.257) and economic resilience (0.532), while employment generation has a substantial effect on economic contributions (0.702). In contrast, local supply chain development has only a small effect on economic resilience 0.043 and employment generation shows a negligible direct effect (0.018).

5. Key Findings and Policy Implications

The findings of this study present significant policy-relevant insights for addressing structural constraints and harnessing opportunities for sustainable rural entrepreneurship. The key policy implications are articulated as follows:

• Infrastructure Development: Strategic investment in transport networks, reliable energy supply, and digital infrastructure is critical to reducing operational inefficiencies and transaction costs. Improving infrastructure will enhance market access, facilitate information flow, and enable rural enterprises to participate more effectively in broader economic systems.
• Access to Finance: Strengthening financial inclusion through targeted interventions, such as micro finance schemes, government-backed grants, and accessible credit facilities, is essential for enabling rural entrepreneurs to expand and sustain their businesses. Policy frameworks should also promote innovative financing models tailored to the unique risk profiles of rural enterprises.
• Entrepreneurship Training and Skills Development: Capacity-building initiatives should be prioritised to equip entrepreneurs with essential business management, innovation, and digital skills. Such programmes should adopt context-specific and practice-oriented approaches that enhance both entrepreneurial competence and adaptability in dynamic market environments.
• Strengthening Local Supply Chains: Policies should support the development and integration of local value chains to improve productivity, competitiveness and economic resilience. Strengthening linkages between producers, suppliers and markets will foster inclusive growth and reduce dependency on external systems.
• Institutional Support and Community Engagement: Enhancing collaboration among government institutions, the private sector and local communities is vital for building social capital and fostering an enabling entrepreneurial ecosystem. Coordinated institutional support can improve policy implementation, resource mobilization and the sustainability of rural enterprises.

The study concludes that while this study provides valuable insights, it is limited to Mnquma Local Municipality and may not fully capture variations across other rural contexts. Future research should consider comparative studies across different regions to enhance generalisability. Additionally, incorporating qualitative approaches could provide deeper insights into entrepreneurial experiences and contextual dynamics. Longitudinal studies are also recommended to examine how rural entrepreneurship contributes to economic resilience over time, particularly in response to evolving economic and environmental challenges.

6. Conclusions

This study examined the role of rural entrepreneurship in enhancing economic resilience in Mnquma Local Municipality. The findings provide strong evidence that rural entrepreneurship contributes to economic resilience by creating jobs, diversifying incomes, fostering innovation, engaging communities, and developing local supply chains. Innovation and diversification improve entrepreneurs’ ability to adapt to changing economic conditions, while community engagement strengthens social capital and collective economic participation. Furthermore, local supply chain development enhances market access and economic integration within rural communities. However, the study also revealed that persistent structural barriers, including inadequate infrastructure, limited access to finance, weak institutional support, and restricted market opportunities, continue to constrain the sustainability and growth potential of rural enterprises. These findings suggest that although rural entrepreneurship plays a critical role in strengthening economic resilience, its effectiveness depends on the availability of supportive institutional, financial, and infrastructural systems.

Despite its contributions, the study was limited to Mnquma Local Municipality, which restricts the generalisability of the findings to other rural contexts. The cross-sectional quantitative design and reliance on self-reported data also limited deeper contextual understanding and may have introduced response bias. In addition, the exceptionally high R² values suggest possible overlaps among constructs, indicating the need for further theoretical refinement and model validation. Future research should therefore consider longitudinal and mixed methods approaches to examine how entrepreneurial resilience evolves. Comparative studies across different rural municipalities or provinces are also recommended to improve contextual understanding and external validity. Furthermore, future studies could incorporate moderating and mediating variables such as government support, digital inclusion, entrepreneurial orientation, and social capital to provide a more comprehensive explanation of the relationship between rural entrepreneurship and economic resilience.