Advancing Rural Entrepreneurship: Does Microfinancing Matter?

Introduction

Small businesses in rural communities are the primary driving force of employment, poverty alleviation, income equality, stability and resiliency, and sustainable economic development (Achua & Lussier, 2014; Crawford & Barber, 2020; Eddleston & Mulki, 2021; Gyimah & Lussier, 2021; Hasan et al., 2023; Peake et al., 2019). Small businesses in rural communities contribute 50% to the gross domestic growth and 60% to employment in developing countries (Borchardt et al., 2018; Gyimah & Lussier, 2021). Although small businesses in rural communities play a significant role in developing and developed countries, access to financial support remains a significant obstacle (Moscalu et al., 2020). A major reason is that small firms do not have the collateral required by banks that large businesses provide, which constrains their sustainability, productivity, performance, or growth (Galli-Debicella, 2020; Gyimah & Boachie, 2018a; Inekwe, 2019). Thus, they relied on non-formal deposited financial firms including microfinance institutions (MFIs) that provide credit at high-interest rates (Blanco-Oliver et al., 2021; Lent, 2020). Nevertheless, the sustainability or performance of rural small businesses depends on the financial assistance offered by microfinance institutions (Parvin et al., 2020).

Microfinance services providing financial aid to low-income people or micro-businesses reduce poverty (Nyanzu et al., 2019; Pollinger et al., 2007). However, there is a need for further research to assess the relationship between the impact of multiple MFI services or products on small firms’ performance. Existing literature findings on the relationship among MFIs and small firms’ performance in developing and developed economies are uncertain (Appiah et al., 2009; Gyimah & Boachie, 2018a). Empirical evidence on the nature, magnitude, and balance of microfinance’s impact on small businesses is still scarce and inconclusive (Addai, 2017). There are limited and inconclusive outcomes of studies exploring whether MFIs have a positive or inverse relationship with the financial performance of firms. Also, most studies focus on urban enterprises and large firms that have access to traditional banks, neglecting rural firms where most firms in every sector depend on them for raw materials (Nukpezah & Blankson, 2017). Few studies have investigated the relationship between microfinancing products and rural firms’ performance (Galli-Debicella, 2020; Garcia et al., 2022). Therefore, it is imperative to examine the relationship between microfinance and small rural businesses’ performance in a developing country, where most rural businesses depend on MFIs for survival. Specifically, this study considers multiple MFI products or services including microloans, micro-savings, insurance, and education; and other variables gender, managerial skills, owner’s age, nature of the industry, and business size to examine its effects on small business performance in the rural communities of a developing country, where 80% of companies depend on MFIs for growth.

This study makes practical and theoretical contributions. Practically, the findings can assist microfinance institutions in evaluating the products or services that affect rural small business performance. The study can help microfinance firms assess the effectiveness of their services or develop new ones that can improve firm performance. Also, the study provides a benchmark to utilize existing scarce resources that would contribute to the sustainability and performance of rural businesses. Government agencies can use the findings to educate rural business entrepreneurs on the MFIs factors that affect their performance. In addition, prospective and existing rural entrepreneurs can use the findings when making investment decisions to aid their financial performance. There is also a lack of published financial data on small businesses and the challenges informal firms face in assessing finance options in developing countries (Adeosun et al., 2021). Thus, this study extends resource dependency theory by examining the MFI drivers of businesses in rural communities in emerging markets. This study expands theory as it includes additional variables that contribute to an understanding of the relationship among MFIs and small rural business variables, improves the explanation of firm performance, and can aid in the prediction of rural business performance in emerging economies.

Literature Review

Rural Entrepreneurship

Rural entrepreneurship (RE) has no unified definition due to its distinctive multidimensional perspective (Crawford & Barber, 2020; Hasan et al., 2023; Saadatmand & Barber, 2019). Thus, RE has different meanings and definitions (Barber et al., 2021; Gyimah & Lussier, 2021; Paynter et al., 2021). Wortman (1990, p. 222) defined RE as “the creation of a new organization that introduces a new product, serves or creates a new market, or utilizes new technology in a rural environment.” Pato and Teixeira (2018) similarly defined RE as the development of a new value from local resources by relating to the rareness and features of the indigenous to create goods or services with value. The study focuses on small firms in rural communities that use indigenous resources to produce goods and services (Gyimah & Lussier, 2021; Yu et al., 2013). Thus, this study defines RE as community-based firms with nine or fewer employees and non-current assets not exceeding US$10,000.

Despite the significant contribution of RE in eradicating poverty, creating employment, and contributing to sustainable development in developing countries, researchers pay little attention to microfinance institutions (MFIs) as a financier in enhancing rural businesses (Nukpezah & Blankson, 2017). MFIs are a significant international innovation since they have increased financial accessibility for the deprived in indigenous societies towards sustainable development (Gyimah & Lussier, 2021). However, Jones et al. (2018) argued that MFIs cause financial distress because they demand high interest on short-term loans. Therefore, it is crucial to investigate the relationship between MFIs’ product or services and the financial performance of small businesses in rural ecosystem or environment in Ghana that depends on MFIs for sustainability.

The rural entrepreneurship ecosystem in Ghana is a dynamic and multifaceted environment that fosters economic growth and empowers individuals in rural areas (Gyimah & Lussier, 2021). One key aspect of the rural entrepreneurship ecosystem is access to finance. MFIs have been instrumental in providing financial services to rural entrepreneurs, enabling them to start and expand their businesses (Nyanzu et al., 2019). These institutions offer small loans, savings accounts, financial literacy programs, and other financial products tailored to the needs of rural entrepreneurs (Gyimah & Lussier, 2021). By providing access to capital, interest on savings and insurance, MFIs empower individuals to turn their business ideas into reality (Majeed et al., 2023). Again, through financial literacy and educational programs from MFIs, rural entrepreneurs gain valuable knowledge on budgeting, record-keeping, and financial management that enables them to make informed decisions, optimize their resources, and improve their business performance (Gyimah & Boachie, 2018b).

Theory and Variables

This study is based on resource dependency theory (RDT), which proposes that businesses can survive by acquiring and maintaining resources from their environment (Hafiz et al., 2022). Many small businesses fail due to resource deficiency (Borchardt et al., 2018; Gyimah et al., 2023; Gyimah & Lussier, 2021; Lussier & Corman, 1996; Postma & Zwart, 2001). Barber III et al. (2019), Salder et al. (2020), and Townsend et al. (2010) asserted that insufficient resources are some of the reasons some small firms do not survive. Hillman et al. (2009) found that insufficient necessary resources can hinder the performance of small businesses, whereas sufficient resources are vital for ventures to change and grow. This study uses a resource dependency theoretical lens to determine which resources provided by MFIs affect the performance of rural small businesses. The four variables, defined below, include: microloans, micro-savings, MFIs insurance, and micro-education.

The terms microloans, microcredit, microfinancing, and microlending often are used interchangeably by the financial services industry. Most banks and traditional financial service providers do not operate on a micro-level. It is most often referring to international development by giving small loans (usually between $100 and $500) to people who live in developing countries around the world (Atiase et al., 2020). We use the term microloans and define a loan as borrowing a small amount of money and repaying it with interest.

Micro-savings is another microfinance product. Micro-savings accounts operate similarly to a standard savings account but are structured around smaller amounts to provide low-income people with an opportunity to store funds for potential use, such as for unexpected expenses, to start a business or for educational expenses. Micro-saving is popularly described as the “susu” in Ghana. Appietu et al. (2021) argue that there is a need for microfinance savings facilities for small businesses in emerging economies. Most traditional banks do not provide funding for small rural firms, and thus small firms depend on MFIs for survival (Gyimah & Boachie, 2018a). Therefore, the MFIs are the only option, especially for underprivileged women in the rural economy that do daily savings known as “Susu” for an accumulated higher amount for investment and other profitable ventures (Gyimah & Boachie, 2018a; Khan & Bhat, 2022).

In terms of MFI insurance, it is a low-fee form of a premium provided to entrepreneurs in the case of misfortune or disaster (Oscar & Abor, 2013). Finally, micro-education includes financial literacy, workshops, and seminars provided by MFIs, usually within one hour, to focus on significant business operation issues (Sarpong-Danquah et al., 2018). The financial literacy, educational seminars, and training provided by MFIs, such as bookkeeping and financial management, increase the performance of businesses (Lensink et al., 2018).

Empirical Review

Existing literature findings on the relationship among microloans, micro-savings, insurance, and education effect on the performance of small businesses in developing and developed economies are mixed. Nilsson (2010) found that MFIs improved the standards of living for low-income people and improved small business performance. Idowu (2010) revealed that small businesses benefited from MFI’s microfinance loans in Nigeria, but few small businesses could get the amount they requested. Thio (2006) found a positive relation between MFIs and the firm’s performance; however, microloan adversely affects the performance of small businesses. Kisaka and Mwewu (2014) reported significant positive effects on the performance of small firms in Kenya, but that education had no impact on small firms’ performance. Additionally, Appiah et al. (2009) explored the roles of microfinance institutions on businesses and concluded that MFIs reduce poverty in developing markets.

Extant works in Ghana typically focus on the relationship between MFIs loans and small firms’ performance (See Boateng & Poku, 2019), neglecting other services or products provided by MFIs. Most of the existing literature in Ghana that has examined the relationship between MFIs and small businesses’ performance reports a positive correlation. Fauster (2014) reported a positive relationship between microfinance and small businesses’ performance in terms of sales revenue. Specifically, Fauster (2014) found a positive relationship between sales revenue (the performance indicator) and microloan and training (predictors indicators). Also, studies by Alhassan et al. (2016), Gyimah and Boachie (2021), and Owusu-Dankwa and Adoley (2014) argued that MFIs products or services strengthen the small firm’s performance. Aladejebi (2019) reports a positive impact between microloans, micro-savings, training, and the performance of small businesses. However, prior researchers only focused on the relationship among access to microloans, savings, training, and small business performance. This study extends the literature to better understand MFIs products or services and other variables’ effects on the performance of businesses in rural communities.

Methods

Design, Sample, and Model

Unlike previous studies, this study uses data from rural communities to assess the effect of MFI products on small business performance with primary survey data using prior research questionnaires validated by Atiase et al. (2020), Gyimah and Boachie (2018), Salojärvi, Furu, and Sveiby (2005), and Lussier and Halabi (2010). The simple random sampling technique is used to select 500 rural small businesses owners across 16 regions of Ghana. We received 228 completed surveys with a high 45.6% response rate.

Multiple regressions are used to test the relationship between dependent and independent variables, including control variables. Following existing literature studies on the performance measures of small businesses in developing countries, two criteria are used to measure the dependent variable: profitability levels and sales growth (Lussier & Halabi, 2010; Salojärvi et al., 2005). The independent variables are microloans, savings, insurance, and education. The control variables are gender, managerial skills, owner’s age, nature of the industry, and business size). The models are as follows:

$$\small\begin{aligned} \text { Profitability Levels }=&\beta_0+\beta_1 \text { Loans }+\beta_2 \text { Savings }\\&+\beta_3 \text { Insurance }+\beta_4 \text { Education }\\&+e\end{aligned}\tag{1}$$

$$\small\begin{aligned} \text { Profitability Levels }=&\alpha_0+\alpha_1 \text { Loans }+\alpha_2 \text { Savings }\\&+\alpha_3 \text { Insurance }+\alpha_4 \text { Education }\\&+\alpha_5 \text { Gender }\\&+\alpha_6 \text { Managerial Skills }\\&+\alpha_7 \text { Age }+\alpha_8 \text { Industry }+\alpha_8 \text { Size }\\&+e\end{aligned}\tag{2}$$

$$\small\begin{aligned} \text { Sales Growth }=&\mu_0+\mu_1 \text { Loans }+\mu_2 \text { Savings }\\&+\mu_3 \text { Insurance }+\mu_4 \text { Education }+e\end{aligned}\tag{3}$$

$$\small\begin{aligned} \text { Sales Growth }=&\lambda_0+\lambda_1 \text { Loans }+\lambda_2 \text { Savings }\\&+\lambda_3 \text { Insurance }+\lambda_4 \text { Education }\\&+\lambda_5 \text { Gender }+\lambda_6 \text { Managerial Skills }\\&+\lambda_7 \text { Age }+\lambda_8 \text { Industry }+\lambda_8 \text { Size }\\&+e\end{aligned}\tag{4}$$

Measurement of Variables

Performance measures of small businesses such as profitability and sales growth are difficult to access, and perception measures are appropriate as in the case of this study (Berrone et al., 2014). The dependent variables are measured using perception statements on a 7-point Likert scale. For profitability, the study used Lussier and Halabi’s (2010) profitability variable survey constructs. Based on a 7-point scale (1-low to 7-high profit), respondents were to choose the correct profit levels of their business after assessing MFI products. For the sales growth variable, the study uses Salojärvi et al. (2005) 4-point scale (1-low to 4-high sales growth). Entrepreneurs are asked to assess the sales growth of their business in the past three years.

The independent variables include microloans, savings, insurance, and education. The study adopts the perceived statements measured on a 7-point scale (1 – strongly disagree to 7 – strongly agree) for each of the four independent variables, as validated by Aladejebi and Oladimeji (2019), Atiase et al. (2020), and Gyimah and Boachie (2018b).

Measuring the control variables, gender is a nominal variable (0 = female and 1 = male). Managerial skills are measured on a 7-point scale (1-low to 7-high skilled), and the owner’s age is a ratio measure equal to the owner’s age in years. Firm size is also a ratio measure of the number of employees (Gyimah et al., 2020). Based on Gyimah et al.'s (2020) findings that retail businesses do not perform well in terms of sales growth and profitability, the industry variable is also a dummy variable (0 = retail/services businesses and 1 = manufacturing and other business classification).

Results

Descriptive Statistics

Table 1 shows the summary descriptive statistics (mean and standard deviation) of the independent variables, including the control variables for 228 small businesses in rural communities. From Table 1, the descriptive statistics indicate that micro-education has the highest mean of 5.96 and standard deviation (SD) of 0.29. The result implies that entrepreneurs agree that educational services contribute to performance or growth. Microloans follow micro education with a mean (SD) of 4.74 (1.55), indicating that most rural business owners agree that business growth depends on loans provided by MFIs. Micro savings recorded a mean (SD) of 4.22 (1.38), indicating a neutral position where some business owners believe that savings help them accumulate funds that are put back into business operations, affecting their performance. Micro-insurance records the lowest mean (SD) of 3.41 (1.27) indicating that business owners disagree that insurance services provided by MFIs affect their performance.

Descriptive control variable statistics include: 75% (n = 171) of the sample are males. Most of the sample have some managerial skills (mean = 4.29, SD = 1.36) to conduct the operation of the business. The age range was 21 to 62 years of age, with an average age of 43. The average number of employees is 5. Over 50% of the companies are manufacturing or trade but not retail or services.

Correlation and Validity Tests

Table 2 reports the Pearson correlations, Cronbach Alpha, Variable Inflation Factor (VIF), and Tolerance levels to ascertain data validity and multicollinearity issues. Table 2 reports 45 correlations, and 25 (56%) are significant correlations (p < 5%) between variables. Lussier (2005) argues that the greater the sample size, the more likely the r-value is significant, and vice versa. Also, when the R-values of the correlations are greater than 0.70, there is an issue of collinearity or multicollinearity (Adeola et al., 2021; Appiah, Gyimah, & Abdul-Razak, 2020; Appiah, Gyimah, & Adom, 2020; Gyimah et al., 2019, 2020; Gyimah & Adeola, 2021; Gyimah & Lussier, 2021; Jalloh et al., 2019; Lussier, 2005; Nkukpornu et al., 2020; Sakyiwaa et al., 2020). For the Pearson correlation matrix (Table 2), none of the correlations are greater than 0.70. Therefore, collinearity and multicollinearity should not be problematic.

Another important consideration is data validity and reliability. Grønmo (2019) and Ghauri et al. (2020) argue that a study’s data is valid and reliable when the Cronbach Alpha is more than 0.60, the tolerance level is less than 2, and VIF is less than 4. The study reports the Cronbach Alpha, VIF, and Tolerance levels to test the validity and reliability of 228 responses. From Table 2, all the variables are within the standard measures suggesting that the data is valid and reliable. The result supports no data bias, multicollinearity, or collinearity issues.

Regression and Discussion

Tables 3 and 4 represent the results of econometric models used to achieve the study’s objectives. The R-squared and Adjusted R-squared for all models are greater than 70%, with a significance level of 0.000. Thus, we are over 99% confident that there is no Type I error, that the model is a valid predictor of success, and that 70% of the variance in performance is based on the independent variables while adjusting results using control variables. Compared to many studies in the Journal of Small Business Strategy (JSBS), Academy of Management Journal (AMJ) and among others that are significant, but only report the R-square that is in the 0.2 range (only 20%), but do not report the adjusted R-square values that are lowered to adjust for their large sample sizes (i.e., Gyimah & Lussier, 2021; Welsh et al., 2022; Xu et al., 2022). This result supports that the econometric models are valid in predicting the MFI products or services that increase rural business performance.

From Model 1 in Table 3, without the control variables, the study reports a positive and significant relationship between MFI products/services (savings, insurance, and education) and rural businesses’ profitability (profit levels). However, for Model 2 (with the control variables), the study finds that savings, insurance, education, managerial skills, owner’s age, and business size increase the profitability of rural businesses.

From Model 3 (without the controls) in Table 4, where the dependent variable is sales growth, the result indicates only loans and education affect the sales growth of rural businesses. Again, loans, education, managerial skills, owner’s age, and business size have a significant positive relationship with the rural businesses’ sales growth with the inclusion of control variables The insignificant positive relationship of micro-savings and insurance on the performance of rural businesses supports Alhassan et al. (2016) but refutes Gyimah and Boachie’s (2018a) study that argues that savings and insurance of MFIs contribute to the performance of small firms.

The result for the relationship between education and performance of rural businesses supports Fauster (2014), which found a significant positive relationship between MFIs education and a small firm’s performance. However, the study refutes Kisaka and Mwewu’s (2014) conclusion that education provided by MFIs does not contribute to small business performance. Moreover, the insurance from MFIs affects the profitability of small businesses in rural communities; however, it has no significant influence on the sales growth of rural businesses. Lastly, education from MFIs increases both the sales and profit levels of small businesses in rural communities. The result clearly shows that financial literacy, educational seminars, and training provided by MFIs, such as bookkeeping, financial management, etc., positively affect the performance of small businesses.

Conclusion

The study examines the effect of microfinance products or services on the profitability and sales growth performance of small businesses in rural communities. The analysis reveals that MFI’s education increases sales growth and profit levels. Thus, education from MFIs can boost sales growth and profits by equipping individuals with valuable knowledge and skills in areas such as financial management, marketing, and business strategy. It empowers entrepreneurs to make informed decisions, identify growth opportunities, and optimize their operations which can lead to increased customer satisfaction, expanded market reach, and improved financial performance. Moreover, insurance from MFIs increases profit levels but not sales growth of rural firms. Insurance from MFIs can help increase profit levels by protecting against unexpected losses, but it does not directly impact sales growth because it is more about managing risks and ensuring financial stability. Additionally, the regression analysis indicates that microloans increase sales but not profit levels in rural firms; micro-savings increase profit levels but not the sales growth of rural businesses. Despite the apparent contradictions, micro-loans and micro-savings provide additional benefits when used together. Microloans can increase sales for rural entrepreneurs by providing them with the capital they need to invest in their businesses, such as buying inventory or equipment. However, since microloans often come with interest and repayment obligations, they can also increase expenses and reduce overall profit levels. So, while sales may increase, profit levels may not necessarily see the same growth. Also, micro-savings can increase profit levels for rural entrepreneurs by helping them build a financial cushion and manage their expenses more effectively. By saving money, rural entrepreneurs can reduce costs and increase their overall profitability. However, micro-savings alone may not directly contribute to sales growth, as they primarily focus on improving financial stability and cost-cutting efficiency rather than generating new revenue streams.

Microloans and micro-savings both benefit RE, but in different ways, which work well in combination. Microloans provide entrepreneurs with the necessary capital to invest in their businesses, which can lead to increased sales growth. On the other hand, micro-savings help entrepreneurs build financial stability and manage expenses, leading to improved profitability. Education can provide the requisite knowledge and skills to advance the success of rural firms, and insurance helps to mitigate unexpected risks. In conclusion, the optimal strategy is to get micro-loans, insurance and education, and have micro-savings. By combining these products and services, entrepreneurs can access MFI funds for growth while also maintaining financial security.

Implications

Based on our findings, major practical implications include the following. For small business profit levels to be increased, nascent and prospective entrepreneurs should attain MFIs products or services including savings, insurance, and education (seminars or training from MFIs). However, to increase their sales volume, entrepreneurs should apply for MFI loans and attend educational programs from MFIs.

Secondly, MFIs should expand the insurance packages to business owners to trust and have some cash assurance in any operative or natural disaster. Thirdly, MFIs should continue to offer education to small businesses consistently geared towards small business performance. There are many aspiring and current entrepreneurs who do not know about MFI products and services. Thus, MFIs and the government should promote MFIs so that more people can become successful entrepreneurs and add to community development, income levels, and standards of living in rural communities. For instance, governments should establish regulatory systems that recognize MFIs as legitimate financial institutions in rural areas. Government can also involve the MFIs in developing poverty alleviation strategies and recognize them as one of the main stakeholders contributing to the achievement of United Nations (UN) Sustainable Development Goals (SDGs), such as SDG 1 – end poverty, SDG – zero hunger, SDG 3 – good health and well-being, and SDG 8 – decent work and economic growth.

Moreover, MFIs and government agencies such as Microfinance and Small Loans Centre and National Board for Small Scale Industries should increase loan facilities, including the loan duration and the loan repayment spread over long periods. Entrepreneurs would have greater use of loans over a long period to acquire capital assets and technological facilities/equipment to help them start and grow. Finally, investors and rural entrepreneurs should use the study’s findings in making investment decisions.

Theoretically, the study relies on resource dependency theory (RDT) to open the “black box” of the MFIs drivers, owners, and business characteristics influencing the development of rural businesses in an emerging market context. The study adds to extant literature suggesting that MFI products and services such as insurance and education are the main critical drivers of the performance of rural businesses. Managerial skills, owner’s age, and firm size are other additional factors influencing rural business performance. These predictors advance the rural entrepreneurship and MFI literature that can aid in the prediction of rural firm performance in emerging markets.

Limitations and Further Research

As with all research, this study has limitations, and we present these limitations with suggestions for future studies. As discussed, this study adds to the current literature as it supports and refutes the results of prior studies. With inconsistent findings, there is a lack of a unified theory, and thus further research is needed.

The study uses a sample from Ghana, which has common characteristics with other rural developing countries, but Ghana is a single developing country sample that represents rural small businesses, thus, the results may not be generalized to other developing countries. Thus, future studies should replicate using samples from entrepreneurs in different developing countries to help further validate and generalize the findings. Also, results are not generalizable to developed countries that are not dependent on microfinancing or even offer microfinancing. However, Ghana leads in financial inclusion compared to other emerging and developed markets, and MFIs and rural development are not entirely different from other countries (World Bank, 2016). Thus, results could still be helpful for policymaking in every country.

The study uses subjective survey research measuring instruments that are commonly used in small business research, including rural entrepreneurship, for both the dependent and independent variables instead of financial or objective data to examine the relationship between MFI products and the performance of small businesses. The reason for using subjective constructs is the unavailable data of small businesses because MFIs and entrepreneurs are unwilling to provide their secondary data to conduct the study. Past studies employ a similar approach by Aladejebi and Oladimeji (2019), Fauster (2014), Gyimah and Boachie (2018a), Salojärvi et al. (2005), and Durst et al. (2019). However, we recommend that future studies should conduct longitudinal or experimental study using objective financial or secondary data to examine the microfinance performance of rural businesses to provide triangulation of data.

The study excluded cultural dimensions, regulatory requirements, micro-and-macroeconomic indicators, the firm’s internal and external factors, and other owners’ characteristics that can affect the performance of rural businesses in developing countries. Future studies can include these indicators as control, mediating, and moderating factors to examine their impact on MFIs and rural business performance.

In summary, our study extends our understanding of microfinance products or services advancing rural entrepreneurship. We find that while microloan increases sales, micro-saving increases the profitability of rural businesses. Insurance and education from MFIs are the critical drivers of the performance of rural firms. Since there is no unified theory of microfinance products or services advancing rural entrepreneurship, the findings can serve as a benchmark adding to theory in utilizing scarce resources for the sustainability and performance of rural businesses.