Financial Capital and MSME Going Concern in Banjarmasin: The Mediating Role of Business Mentoring

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in emerging economies, yet their sustainability is frequently constrained by limited capital access and inadequate business support. While prior studies have examined these factors separately, empirical evidence on how business mentoring mediates the relationship between capital loans and MSME going concern remains limited. This study addresses that gap by examining the direct and indirect effects of capital loans and business mentoring on MSME sustainability in Banjarmasin, Indonesia. A quantitative associative causal design was employed, with survey data collected from 92 KUR participants selected through purposive sampling. PLS-SEM was selected given the exploratory model structure, small sample size, and the need to assess measurement and structural relationships simultaneously.

Findings indicate that capital loans exert a positive and significant effect on going concern (path coefficient = 0.679; p < 0.05), explaining 45.3% of its variance alongside mentoring. In contrast, business mentoring shows no significant direct or mediating effect, with a low mean score of 2.70 suggesting limited program intensity across the sample. These results are bounded to the Banjarmasin context and do not support broader causal generalizations. They nonetheless highlight the need for more structured and context-responsive mentoring programs integrated with existing capital support schemes.

Keywords: loan capital; business mentoring; going concern; MSMEs Banjarmasin; human capital theory.

Introduction

Micro, Small, and Medium Enterprises (MSMEs) in Indonesia are vital to the economy and generate many jobs. According to the Ministry of MSMEs (July 2025), MSMEs produced about 61.9% of Gross Domestic Product (GDP) and employed nearly 119 million people, covering 97% of the workforce (Aprionis, 2025). The Ministry of Cooperatives and SMEs reported similar figures for early 2025: MSMEs contributed 62.3% to GDP and employed over 117 million people, about 97% of all jobs (Puspa, 2025). MSMEs face ongoing sustainability challenges, despite their strong contributions. Limited access to financial capital blocks their growth and development (Suswanto, 2024). Poor business mentoring also limits entrepreneurs’ ability to manage resources, making them more vulnerable to problems in today’s competitive market. The sustainability of MSMEs is closely linked to the managerial capabilities of business owners.

In practice, however, many MSMEs continue to face limitations in managerial competence, which often leads to business discontinuity or even bankruptcy (Hikmahwati & Irwansyah, 2023; Meilan, 2024). In the context of Banjarmasin City, although the number of MSMEs has grown rapidly, maintaining business continuity remains a significant challenge. A considerable proportion of MSMEs operate primarily as a means of supplementing household income or ensuring subsistence, which constrains their ability to expand and develop. Data on the distribution of People’s Business Credit (KUR) indicate that Banjarmasin records the highest number of beneficiaries, with 12,920 recipients, and also represents the largest KUR disbursement area in South Kalimantan. This situation is associated with relatively higher levels of financial literacy among MSME actors and better access to formal financial institutions compared to other regions (Derapjurnalis, 2024).

Nevertheless, access to capital alone is insufficient; it requires complementary support in the form of effective mentoring to ensure its optimal utilization (Kalimantanpost.com, 2024). Financing plays a fundamental role in sustaining business operations, particularly for micro and small enterprises that rely on additional capital to meet operational needs and address financial constraints (Hasan, 2018; Heller et al., 2025; Manalu et al., 2022). Empirical studies have demonstrated that access to bank credit contributes positively to the growth and development of small businesses (Dewanti et al., 2020). Similarly, financing through the KUR program has been shown to enhance MSME capacity (Khoiriah et al., 2024). However, capital availability alone does not guarantee business sustainability. Effective utilization of financial resources requires structured and continuous mentoring, including training in financial management, marketing strategies, and product innovation.

The integration of adequate capital and high-quality mentoring is therefore essential to enable MSMEs to navigate challenges and achieve sustainable growth (Atmawidjaja et al., 2023). Previous studies further indicate that mentoring— covering areas such as product management, finance, human resources, and digital marketing—can significantly improve business performance (Ningtyas & Kusuma, 2024). In addition, mentoring initiatives contribute to broader economic outcomes by enhancing business capacity, strengthening competitiveness, and increasing productivity and employment (Enuh et al., 2023). Prior research has predominantly highlighted the role of financial capital in improving MSME performance. Studies by Hermawan et al. (2024) and Chaedar et al. (2023) report that bank financing positively affects MSME income, while Iqnatia et al (2021) and Nisa & Lindananty (2024) separately emphasize the importance of business mentoring in fostering MSME development.

Despite these contributions, existing studies tend to examine capital and mentoring as isolated factors, leaving unaddressed the question of how these two resources interact within a coherent going concern framework. In particular, the conditional mechanism through which mentoring may amplify or constrain the effect of capital access on business sustainability has received limited theoretical and empirical attention. This study addresses that gap not merely by combining two previously separate variables, but by theorizing and testing a specific mediating mechanism: the proposition that business mentoring functions as an intermediary process through which the productive impact of capital loans on going concern is realized or inhibited. This framing draws on the Resource-Based View, which holds that external resources such as capital only generate sustainable outcomes when complemented by internal organizational capabilities, and on Human Capital Theory, which situates managerial knowledge and skills as essential conditions for translating financial inputs into performance gains.

By embedding this mechanism within the specific context of Banjarmasin, recognized as the largest recipient and distributor of KUR (Kredit Usaha Rakyat) in South Kalimantan, the study also generates contextsensitive insights into why mentoring programs may underperform even when capital access is relatively high. This dual theoretical and contextual contribution distinguishes the present study from prior work and offers a more precise basis for designing integrated MSME support interventions. This study is expected to contribute to the academic literature by providing a more comprehensive understanding of the determinants of MSME sustainability. From a practical perspective, the findings may inform policymakers, financial institutions, and other stakeholders in designing more effective MSME development strategies. Such strategies should not only expand access to financing but also strengthen mentoring programs that focus on managerial, financial, and marketing competencies.

Ultimately, the results are anticipated to support MSMEs in Banjarmasin in responding to market competition and economic uncertainty, while also contributing to sustainable economic development at the national level. Theoretical Framework and Hypothesis Human Capital Theory (HCT) Human Capital Theory (HCT), as advanced by Becker (2009), posits that skills, knowledge, and experience constitute critical assets that enhance productivity and support the sustainability of business activities (Burhanudin, 2021; Timothy, 2022). Within the context of MSMEs, HCT suggests that business continuity is not solely determined by financial resources but is strongly influenced by the quality of human capital, including managerial competence, accounting literacy, and the ability to adapt to changing environments (Erawati et al., 2024). While alternative frameworks offer related insights, HCT provides the most analytically appropriate lens for this study.

The Resource-Based View (RBV) emphasizes firm-level competitive advantage through valuable and difficult-to-imitate resources, but does not sufficiently address the individual-level knowledge development that underpins MSME owner-operator performance (Barney, 1991). Dynamic Capabilities Theory, meanwhile, focuses on organizational capacity to reconfigure competencies in response to environmental change, which is less applicable to micro-enterprises where the owner and manager are typically the same individual and formal capabilitybuilding processes remain nascent (Teece et al., 1997). HCT, by contrast, directly addresses how investment in individual knowledge and skills through education, training, and mentoring translates into improved decision-making, resource utilization, and business sustainability, making it particularly well-suited to the present study's focus and context.

Building on this theoretical foundation, access to capital loans alone is insufficient unless supported by adequate financial knowledge and managerial capabilities (Sailendra & Tampubolon, 2020; Yakob et al., 2021). From an HCT perspective, financial capital functions primarily as an enabling factor, whereas business performance and continuity are fundamentally shaped by the quality of human resources. Mentoring can accordingly be conceptualized as a structured form of human capital investment, contributing to the development of knowledge, skills, and entrepreneurial motivation among MSME actors (Leiwakabessy et al., 2020). Through sustained mentoring processes, business owners are better equipped to utilize capital loans more effectively and productively, thereby strengthening their prospects for longterm business sustainability.

Theoretical Framework and Hypotheses

Human Capital Theory (HCT)

Going Concern

The concept of going concern refers to a firm’s ability to sustain its operations and maintain profitability over time. A business is generally assumed to continue operating unless the owner decides to discontinue its activities or external conditions necessitate liquidation. This assumption is grounded in the premise that the entity possesses the capacity to operate indefinitely (Widayanto et al., 2020). In the context of micro, small, and medium enterprises (MSMEs), going concern reflects the long-term viability and resilience of the business. The sustainability of an enterprise is influenced by several key factors, including effective financial management, access to capital, and the availability of mentoring support. These factors contribute to business growth, which can be observed through improvements in capital structure, business scale, profitability, and managerial capability (Eni et al., 2020).

Furthermore, business sustainability can be evaluated using several performance indicators, such as sales turnover, profit levels, production capacity, product innovation, and asset growth (Widayanto et al., 2020; Zarte et al., 2019).

Capital Loan

Equity loans represent an additional source of financing that can be utilized to support business expansion, including increasing sales capacity, adopting new technologies, and opening new branches (Marita & Permatasari, 2019). In the context of small and medium-sized enterprises (SMEs), access to working capital loans has been shown to positively influence business continuity. When internal funds are insufficient, external financing—particularly credit—often becomes a primary mechanism to overcome capital constraints (Isti, 2016; Marita & Permatasari, 2019). The availability of credit enables businesses to sustain their operations and maintain continuity in their production and service activities. However, the effectiveness of capital loans depends on how efficiently these funds are allocated and managed. Capital loans can be assessed through several indicators, including loan maturity period, increases in production or inventory levels, growth in sales performance, and the extent to which credit financing supports the acquisition of production equipment and other operational needs (Muhamad, 2022).

Business Mentoring Business mentoring plays a significant role in enhancing the effectiveness of strategies aimed at fostering the growth of micro-enterprises (Khalid et al., 2017). It encompasses a range of structured activities, including training and interactive guidance provided by mentoring teams to both existing and prospective MSME entrepreneurs (Ariyanto et al., 2021). Such initiatives are essential for strengthening the capacity of MSMEs and improving their ability to compete in dynamic market environments. To support the development of MSME products within the community, mentoring programs require strong collaboration among key stakeholders, including government institutions, the private sector, stateowned enterprises (SOEs), and academic institutions. This collaborative approach facilitates synergy and is considered instrumental in accelerating economic recovery and promoting inclusive growth (Istiqomah et al., 2022).

Furthermore, business mentoring includes various forms of support, such as financial management training, marketing strategies, and capacity-building initiatives (Atmawidjaja et al., 2023). When implemented effectively, mentoring enhances the ability of MSMEs to manage and allocate financial resources more efficiently, thereby enabling the optimal utilization of capital and strengthening long-term business sustainability. Research Hypothesis The Influence of Capital Loans on The Going Concern of MSMEs Prior studies suggest that when business entities encounter capital constraints in supporting their operational activities, external financing, particularly in the form of credit, becomes a primary mechanism to address such limitations (Isti, 2016; Marita & Permatasari, 2019). Funds obtained through credit facilities serve as an important source of supplementary capital, enabling firms to maintain stable cash flows, procure raw materials, and finance other essential operational expenditures.

In the absence of adequate access to credit, many enterprises, especially MSMEs, are vulnerable to operational inefficiencies and disruptions that may ultimately threaten their long-term viability. Therefore, access to capital loans is considered a critical factor in sustaining business operations and ensuring continuity. Based on these arguments, the following hypothesis is proposed: H1: Capital loans have a direct impact on the going concern of MSMEs. Business Mentoring Mediates The Influence of Capital Loans on the Sustainability of MSME Businesses. Business sustainability, reflected in the concept of going concern, represents a key indicator of MSME performance, particularly in relation to the use of capital loans to support operational and financing needs. However, the effectiveness of capital utilization is not determined solely by the availability of financial resources, but also by the capability of MSMEs to manage these resources through appropriate managerial and strategic practices.

In this context, business mentoring plays a pivotal role by guiding financial management, marketing strategies, and capacity development. Such support enables MSMEs to optimize the use of capital, improve operational efficiency, and enhance their competitive position. Therefore, mentoring is expected to strengthen the relationship between capital loans and business sustainability. Based on these arguments, the following hypothesis is proposed: H2: Business mentoring mediates the relationship between capital loans and the going concern of MSMEs.

Business Mentoring

Criteria for Questions Asked Avera ge score Z.1.1 Participating in several training sessions related to business financial management. 2.72 Z.1.2 Understanding how to prepare financial reports. 2.67 Z.1.3 Implementing sound financial management in my business. 2.75 Z.2.1 Receiving guidance on business marketing strategies. 2.70 Z.2.2 Ability to use digital media for promotions. 2.80 Z.2.3 Expanding market reach. 2.76 Z.3.1 Participating in technical or business skills training. 2.72 Z.3.2 Improving skills in managing business operations. 2.70 Z.3.3 Ability to develop new products or services. 2.71 N=92 Total Average Score 2.7 Source: Processed Data (2025) structural model, as it suggests that going concern in this context is driven more by operational resilience than by financial growth. Consequently, factors that influence revenue generation and asset development beyond capital access alone merit closer examination in subsequent analyses.

The descriptive results for the Business Mentoring variable are presented in

Research Hypotheses

Methods

This research was conducted in Banjarmasin City over the period from January to September 2025 and employed a quantitative approach with an associative (causal) design. The primary objective was to examine causal relationships by analyzing the influence of independent variables on the dependent variable. The study utilized both primary and secondary data sources. The population comprised all MSMEs that received People's Business Credit (KUR) funds in Banjarmasin, totaling 12,920 business units. The sample was selected using a purposive sampling technique, with two primary criteria: (1) the MSME had been operating for a minimum of three years, and (2) the MSME had actively participated in the KUR program for more than three years, ensuring that respondents possessed sufficient experience to meaningfully assess the study variables. To determine the initial sample size, the Slovin formula was applied with a margin of error of 10%, as follows: n = N 1 + N (e)2 n = 12,920 1 + 12920 (0,10)2 n = 12,920 1 + 12,920x 0,01 n = 12,920 130,2 = 99,3 = 99 A total of 99 questionnaires were subsequently distributed; however, only 92 were returned and deemed complete and suitable for further analysis.

This final sample of 92 respondents is considered adequate for the analytical method employed, as PLS-SEM can yield stable and reliable estimates with a minimum of 30 to 100 observations, particularly when the structural model contains a limited number of constructs and indicators. This study examines three main variables: capital loans (X), business mentoring (Z), and the going concern of MSMEs in Banjarmasin (Y). Capital loans are defined as financial resources obtained by business actors to support their operations, either for routine activities or for expanding production capacity. This variable is measured using several indicators, including the loan maturity period, changes in production or sales volume, sales growth following the receipt of the loan, and the extent to which the loan fulfills business operational needs (Enuh et al., 2023).

Business mentoring is defined as structured guidance and coaching provided to Figure 1. Research Framework their managerial, financial, and operational capabilities. This variable is measured using indicators such as financial management training, marketing guidance, and capacitybuilding initiatives designed to improve overall business performance (Khalid et al., 2017). The going concern variable reflects the ability of a business to sustain its operations over time and is assessed through indicators such as sales turnover, profitability, production capacity, product development, and asset growth (Isti, 2016). Data were collected using a survey method through the distribution of questionnaires in both printed (hardcopy) and online formats via Google Forms. This approach was adopted to facilitate respondent participation and to increase the likelihood of obtaining complete and usable responses.

Following data collection, the analysis was conducted using the Partial Least Squares–Structural Equation Modeling (PLSSEM) technique, supported by SmartPLS version 4. The analytical procedure was carried out in several stages. First, the measurement model (outer model) was evaluated to ensure the adequacy of the research instruments. Convergent validity was assessed based on factor loadings and Average Variance Extracted (AVE) values. Discriminant validity was examined using cross-loadings, the Fornell–Larcker criterion, and the Heterotrait–Monotrait ratio (HTMT). In addition, construct reliability was evaluated using Cronbach’s Alpha and Composite Reliability to confirm the internal consistency of the measures. Subsequently, the structural model (inner model) was assessed to examine the predictive capability and explanatory power of the model.

The coefficient of determination (R²) was used to evaluate predictive accuracy, while the Stone–Geisser Q² value was employed to assess predictive relevance. The Goodness of Fit (GoF) index was also considered to provide an overall evaluation of model adequacy. Furthermore, a bootstrapping procedure was performed to generate tstatistics and p-values, which were used to test the significance of both direct and indirect relationships among variables. In the final stage, mediation analysis was conducted to examine the role of Business Mentoring in mediating the effect of Capital Loans on the going concern of MSMEs in Banjarmasin. This analysis utilized the indirect effect procedure available in SmartPLS. Based on this analytical framework, a conceptual model was developed to illustrate the relationships among the research variables, as presented in the following figure.

MSME actors through formal programs aimed at strengthening The Mediating Role of Business Mentoring in the Relationship between Capital Loans and the Going Concern of MSMEs in Banjarmasin (see Figure 1)

Result and Discussion

Description of Respondents’ Responses to the Research Variables

Based on the results of data processing, a descriptive analysis of respondents’ responses was obtained for the Capital Loan variable (X), as presented in

Outer Model Evaluation (Measurement Model)

Convergent Validity

Reliability

Construct reliability evaluates the consistency of measurement instruments in capturing the underlying variables (see Table 5). This assessment is typically conducted using Cronbach’s Alpha and Composite Reliability, with a minimum acceptable threshold of 0.70. Values exceeding this threshold indicate a high level of internal consistency among the indicators, suggesting that the measurement results are reliable and stable. Construct reliability in this study was assessed using Cronbach's Alpha, Composite Reliability (ρc), rho_a (ρa), and Average Variance Extracted (AVE), with a minimum threshold of 0.70 for reliability indices and 0.50 for AVE. The results indicate that the Capital Loan variable (X) reports a Cronbach's Alpha of 0.907, ρa of 0.916, ρc of 0.926, and an Factor AVE Composite Reliability Description 0,907 0,989 0,612 0,926 0,582 0,932 Composite reliability (rho_c) Average variance AVE of 0.612.

The Going Concern variable (Y) shows a Cronbach's Alpha of 0.919, ρa of 0.928, ρc of 0.932, and an AVE of 0.582. Both constructs satisfy all established thresholds, confirming adequate internal consistency and convergent validity. Regarding the Business Mentoring variable (Z), the Cronbach's Alpha and ρc values of 0.989 indicate a very high level of internal consistency. However, the ρa value for this construct was found to exceed 1.00, which is mathematically atypical and warrants careful interpretation. In PLS-SEM, ρa values above 1.00 can occur when indicators are highly intercorrelated, leading to negative error variance estimates during the weighting process, a phenomenon sometimes referred to as a Heywood case (Dijkstra & Henseler, 2015). This outcome is likely attributable to the exceptionally high interindicator correlations within the Business Mentoring construct, as evidenced by its very high outer loadings (ranging from 0.935 to 0.966) and AVE of 0.907.

While this does not invalidate the construct, it suggests potential indicator redundancy. Researchers are advised to interpret the ρa value for this construct with caution and to consider refining the indicator set in future studies to reduce multicollinearity among Figure 2. Results of the Structural Model Analysis of Capital Loans, Business Mentoring, and Going Concern

Inner Model Evaluation (Structural Model)

The evaluation of the structural model (inner model) aims to examine the relationships among latent variables within the study. This assessment is conducted using several key indicators. The coefficient of determination (R²) is used to evaluate the extent to which the independent variables explain the variance in the dependent variable. The predictive relevance of the model is assessed using the Q² (Stone– Geisser) value, while the overall model fit is evaluated through the Goodness of Fit (GoF) index. A higher R² value indicates stronger explanatory power of the model. A positive Q² value suggests that the model has adequate predictive relevance. Meanwhile, the GoF index is used to classify the overall model fit into categories such as small, medium, or large, reflecting the degree to which the model adequately represents the observed data.

Table 6 presents the results of the R-Square analysis, which evaluates the extent to which the independent variables explain the variance in the dependent constructs. For the Business Mentoring variable, the R-Square value is 0.030 (Adjusted R-Square = 0.020), indicating that only 3.0% of its variance is explained by the independent variable, while T statistics (O/STDEV|) the remaining 97% is attributed to factors outside the model. This result reflects a very weak level of explanatory power for this construct. It should be noted that conclusions regarding predictive relevance cannot be drawn solely from the R-Square value; such an assessment requires additional evaluation through the Q-Square (predictive relevance) statistic. In contrast, the Going Concern variable shows an R-Square value of 0.453 (Adjusted R-Square = 0.441), meaning that approximately 45.3% of its variance is explained by Capital Loans and Business Mentoring, with the remaining proportion attributed to variables outside the model.

This reflects a moderate to substantial level of explanatory power for this construct. Nevertheless, the degree to which this explanatory capacity translates into predictive relevance should be further verified through Q-Square analysis and the Goodness of Fit (GoF) index before drawing broader conclusions about model robustness. Overall, these findings indicate that the model demonstrates limited explanatory capacity for Business Mentoring but a relatively stronger ability to explain variance in the Going Concern of MSMEs. A more comprehensive evaluation of the model's predictive relevance and overall fit should incorporate Q-Square and GoF measures in conjunction with the R-Square results reported here.

Bootstrapping (Significance Testing)

The significance of the relationships among variables was assessed using the bootstrapping procedure in SmartPLS. A relationship is considered statistically significant when the tstatistic is greater than or equal to 1.96, and the p-value is less than 0.05. This approach enables the evaluation of both direct and indirect effects among variables, thereby providing empirical support for testing the proposed hypotheses. Based on the results presented in Table 7, the bootstrapping analysis indicates that Business Mentoring (Z) does not have a significant effect on Going Concern (Y), as reflected by a path coefficient of -0.042 and a p-value of 0.659. Therefore, the hypothesis proposing a positive relationship between Business Mentoring and Going Concern is not supported. Similarly, Capital Loans (X) do not exhibit a significant effect on Business Mentoring (Z), with a path coefficient of 0.174 and a p-value of 0.271.

This finding suggests that the availability or magnitude of capital loans does not necessarily enhance the intensity or quality of mentoring received by MSMEs. In contrast, Capital Loans (X) demonstrate a positive and statistically significant effect on Going Concern (Y), with a path coefficient of 0.679 and a pvalue of 0.000. This result confirms that greater access to capital loans is associated with a higher likelihood of sustaining MSME business operations. Overall, these findings highlight that Capital Loans play a critical role in supporting business sustainability, whereas Business Mentoring does not show a significant influence, either as a direct predictor or as a mediating variable in the relationship between Capital Loans and Going Concern.

Mediation Analysis (Intervening Effect)

The final stage of the analysis examined the role of Business Mentoring as a mediating variable in the relationship between Capital Loans and the Going Concern of MSMEs in Banjarmasin. Mediation analysis was conducted using the bootstrapping procedure to evaluate the significance of the indirect effect. A statistically significant indirect effect indicates the presence of a mediating relationship. Through this approach, Business Mentoring is expected to either strengthen or weaken the effect of Capital Loans on business sustainability. The results of the structural model analysis are presented in Figure 2. Figure 2 illustrates the structural relationships among the variables in the model. Capital Loans exhibit a relatively strong positive effect on Going Concern, with a path coefficient of 0.679, indicating that greater access to capital is associated with a higher likelihood of MSME sustainability.

In contrast, the effect of Capital Loans on Business Mentoring is relatively weak, with a coefficient of 0.174, suggesting a limited contribution. Furthermore, the relationship between Business Mentoring and Going Concern is negative and statistically insignificant, as indicated by a coefficient of -0.042. This result implies that Business Mentoring does not contribute positively to business sustainability. Consequently, Business Mentoring does not function as a mediating variable in the relationship between Capital Loans and Going Concern. This finding is further supported by the R² values, where only 3% of the variance in Business Mentoring is explained by Capital Loans, whereas 45.3% of the variance in Going Concern is explained jointly by Capital Loans and Business Mentoring. The results of the mediation analysis using the bootstrapping procedure are presented in

Conclusion

The results of this study indicate that capital loans exert a significant positive effect on the going concern of MSMEs in Banjarmasin (path coefficient = 0.679; p < 0.05), while business mentoring does not demonstrate a significant contribution, either as a direct predictor or as a mediating variable in the capital–sustainability relationship. Together, both variables explain 45.3% of the variance in going concern, with the remaining 54.7% attributable to factors outside the model. Among the predictors examined, capital access thus emerges as the most substantive driver of operational stability, whereas the effectiveness of mentoring appears constrained by limitations in program quality and the internal capacity of business owners. These findings, however, are bounded by the study's design: the model includes only two predictor variables, the sample is drawn exclusively from KUR participants in a single city, and the cross-sectional design precludes causal inference over time.

Within these boundaries, the results nonetheless offer meaningful insights — the limited effectiveness of business mentoring points to the need for program redesign rather than a conclusion about mentoring's fundamental irrelevance to MSME sustainability. Building on these empirical findings, the study contributes to theoretical discussions on the determinants of going concern by suggesting that business sustainability is shaped not only by external financial access but also by the quality of internal resource management, encompassing managerial capability and financial literacy. While capital loans emerged as the strongest predictor within this model, this should not be interpreted as evidence of capital's dominance across all MSME contexts, given the model's limited predictor set. More broadly, the findings highlight the value of integrating capital support with well-designed mentoring interventions as a foundation for more comprehensive MSME management frameworks.

At the practical level, MSMEs are encouraged to direct capital loans not merely toward short-term operational needs but also toward strategic investments such as product innovation, market expansion, and operational efficiency that contribute to long-term sustainability. Translating these insights into action, mentoring institutions and policymakers are advised to redesign programs with greater operational specificity: incorporating participant readiness assessments to calibrate content to each owner's educational background and business stage, ensuring sustained engagement of at least three to six months with periodic follow-up, and diversifying delivery mechanisms to include peer learning, on-site coaching, and digital platforms. Crucially, mentoring content should be integrated with the capital loan cycle so that financial management training covering budgeting, cash flow, and basic reporting is delivered when MSMEs are actively managing borrowed funds, thereby maximizing its practical impact.

For future research, incorporating additional variables such as financial literacy, innovation capacity, human resource quality, and business ecosystem support, alongside longitudinal designs and multicity samples, would better capture the delayed effects of mentoring and strengthen the generalizability of findings. Such an expanded approach would support the development of more context-specific theoretical models and contribute to evidencebased strategies for the sustainable growth of MSMEs.

Author Contributions

Hikmahwati contributed to the conceptualization and design of the study, performed the analysis, interpreted the results, and participated in writing the manuscript. Noor Romy Rahwani contributed to the conceptualization and design of the study, collected the data, interpreted the results, and participated in writing the manuscript. Muhammad Ali Watoni collected the data, contributed data and analysis tools, performed the analysis, and participated in writing the manuscript. Mark Gabriel Wagan Aguilar contributed to the conceptualization and design of the study, contributed data and analysis tools, performed the analysis, interpreted the

Acknowledgements

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