Abstract
Purpose
This study aims to investigate whether social capital mediates the impact of financial capital on business performance in Cameroon.
Design/methodology/approach
The study uses quantitative data collected from 370 small businesses in Yaoundé and Douala in Cameroon. All businesses in the sample are formally registered and are in the services sector. A structural equation modelling (SEM) approach is used for the analysis.
Findings
Structural and relational capital constraints are significant mediators of formal and informal finance. The magnitude effects of relational capital are the largest, underlining information's importance in resolving small and medium enterprises’ (SMEs') financial constraints. In addition, the effect of informal finance constraints on business performance is larger in magnitude, confirming the substantial impact of informal finance on SME operations.
Research limitations/implications
The paper confirms that relational and structural social capital are vital in business. However, the study did not investigate the disaggregated effects of these dimensions of social capital. Furthermore, how SMEs transition between formal and informal finance could provide further understanding of the role of social capital. A disaggregated and panel data set would help to provide additional insights.
Practical implications
Social capital emerges as a pivotal factor in enhancing SME access to finance. The results, therefore, confirm the relevance of a holistic approach to easing financial capital constraints for SMEs and enabling small businesses to connect more to various stakeholders to amplify business performance. In addition, the findings identified some intervention points for the governments in Cameroon as it seeks to use SMEs as its pivot for development and to catapult itself to emerging economy status in its Cameroon 2035 vision.
Originality/value
The value of the study lies in assessing the mediating effect of cognitive, relational and structural social capital constraints on business performance and comparing the effect of formal and informal financial constraints on business performance.
Keywords
Citation
Kabange, M.M. and Simatele, M. (2022), "Constraints to formal small business performance in the service sector in Cameroon", African Journal of Economic and Management Studies, Vol. 13 No. 3, pp. 344-365. https://doi.org/10.1108/AJEMS-07-2021-0331
Publisher
:Emerald Publishing Limited
Copyright © 2022, Martin Mulunda Kabange and Munacinga Simatele
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Numerous scholars have recognised the vital role of small and medium enterprises (SMEs) in creating employment opportunities, innovation and alleviating poverty, especially in developing countries (Djeudja and Kongnyuy, 2018; Sebikari, 2019; Faal, 2020). At the same time, the literature notes that SMEs have low survival rates with limited potential for expansion (Lo et al., 2016; Bakhtiari et al., 2020). A key constraint cited in numerous studies for this poor performance is limited access to appropriate forms of finance at various stages of a business's lifecycle (Beck and Demirguc-Kunt, 2006; Wu et al., 2016; Quartey et al., 2017).
In particular, the literature shows that limited access to external finance can limit the ability of SMEs to seize growth opportunities, stifle expansion and innovation and thereby negatively affect business performance (Woldie et al., 2018; Eyiah et al., 2018). This recognition has led many countries to take deliberate steps to increase SME access to external finance. Policy efforts have included the development of public credit programs, promoting special purpose vehicles such as development finance institutions, and stimulating private sector financing, which is SME friendly, including microfinance institutions. These efforts have been mainly a response to the ubiquity of information asymmetries and agency problems common in developing country financial markets, including SME information opacity, lack of collateral, lack of managerial skills and transaction costs (Beck, 2013; Shohibul et al., 2019). Despite this focus, the literature indicates that not much substantial improvement has been made in SME access to finance.
Social capital is emerging as a crucial resource in alleviating SMEs' financial constraints (Javakhadze et al., 2016; Fogel et al., 2018). One primary reason for this is that relationships are central to mitigating information asymmetries in various financial markets (Boot, 2000). Relationships with stakeholders such as employees, customers, suppliers, government and investors can result in the collection of crucial soft information, which reduces the information opacity of the SMEs, in turn leading to the amelioration of information asymmetry and agency problems typical of financial markets.
The extant literature on African countries highlights the importance of social capital for both business performance and access to credit (Boohene, 2018; Islam et al., 2018). Finding ways of enhancing social capital development can improve access to financial capital for SMEs and overall SME performance.
Studies by Kabange and Simatele (2021) and Djoutsa Wamba et al. (2017) indicate that social capital presents such an opportunity for SMEs in Cameroon. Djoutsa Wamba et al. (2017) suggest that entrepreneurs use social capital to convince bankers of the viability of their businesses while bankers use social capital to decide on future financing structures. Related, Kabange and Simatele (2021) suggest that improved social capital in Cameroon could do more to alleviate financial constraints and improve business performance than some credit access programs that have been put in place. SMEs in Cameroon contribute 60% of total employment and 40% of GDP (Nkaku Policy Institute, 2019). In its Cameroon 2035 vision, the Cameroonian government has set its sights on SMEs as the core driver for its achievement of emerging country status (Ndam et al., 2020). A significant obstacle to this achievement is access to credit. In response, the government created the Cameroon Bank for SMEs. Despite this, the financing gap for SMEs has persisted (Nkaku Policy Institute, 2019).
Therefore, the present study examines the possibility that social capital can enhance the contribution of financial capital to SME performance. We make three contributions to the literature. First, we test for social capital as a moderator for the impact of financial capital on SME performance. Zhao et al. (2010) argue that not including mediators in the analysis can mask the actual effects of variables. For example, many government programs, such as those cited above, could be ineffective because of a focus on the direct provision of finance rather than stimulating factors that alleviate information asymmetries if such factors moderate the influence. Second, we use a multidimensional measure of social capital to capture cognitive, relational and structural aspects that are likely to affect both access to finance and business performance diversely. The effect of these dimensions of social capital is not the same (Guo et al., 2021). Evidence shows the persistent reliance of SMEs on informal finance due to ease of accessibility (Naegels et al., 2018). Mukete et al. (2021)'s study shows that nearly 96% of SMEs in Cameroon get their finance from Informal sources. In line with this, the paper makes a third contribution by investigating the finance–social capital nexus at a disaggregated level with a separation of finance into formal and informal finance. The literature suggests that the effect of these two forms of finance on SME performance is likely to be different. For instance, informal finance seems to have a U shaped effect; there is an initial positive impact, but high reliance on informal finance may be deleterious over time (Nguyen and Canh, 2021).
The paper is organised into five sections: The first section presents a brief literature review on constraints to financial capital and how those constraints can be eased through improved social capital. This section is followed by a discussion of the theoretical framework and hypotheses. Next, the methodology and data are presented. This section is followed by the presentation and discussion of the results. The paper ends with a conclusion and some policy implications.
Literature review
Constraints to financial capital in developing economy contexts
Many formal lenders rely on information about the SME to make lending decisions. However, the literature is replete with evidence that shows the opaqueness of SMEs (Viswanadham, 2017). Many SMEs are not astute in financial statement preparation and therefore struggle to convey their business performance to potential lenders. As a result, many are rationed out of credit markets. Critical to the resolution of information asymmetry is the development of lending technologies that can either increase the availability of information about the SME or circumvent those limitations without increasing the default risk.
Relationship lending is a common approach to dealing with information opacity documented in the literature. Relationship lending requires the lender and borrower to have a relationship that enables the gathering of proprietary information about the borrower, enabling the lender to assess the borrower's capacity to make repayments (Berger and Udell, 1994; Uchida and Udell, 2006). Evidence from African countries shows that relationship lending has increased access to credit for SMEs (Sahar and Anis, 2016). In addition, developments in technology have identified possible tracking of SME cash flow and credit history through mobile money transactions and social media interactions. For example, some African countries have attempted to improve information availability for small businesses by establishing information bureaus akin to those of more advanced economies.
An example is Rwanda's collaboration with American based company Kountable. Countable uses entrepreneurs' business and social media connections to calculate a credit score used to make lending decisions (van Klyton and Rutabayiro-Ngoga, 2018). In addition, Beck et al. (2015b) show that the use of M-Pesa [1] in Kenya is positively correlated with access to credit for SMEs. These results are confirmed for mobile money in general in a cross-country study by Gosavi (2017).
In the absence of relevant information on SMEs, lenders can resort to collateral to mitigate risk. However, most African SMEs do not have adequate collateral (Amoako-Adu and Eshun, 2018). In the absence of adequate collateral, lenders tend to charge very high-interest rates. Furthermore, the use of collateral can only be effective in the presence of appropriate enforcement mechanisms. Evidence suggests, however, that enforcement mechanisms are weak in many developing countries (Atiase et al., 2018; Kuada, 2021). In some cases, lenders may substitute collateral requirements with third party guarantees, which can take the form of both public and private guarantees (Menkhoff et al., 2012; Abraham and Schmukler, 2017). Guarantees play a significant part in substituting for collateral and may, in some cases, be more important than relationship lending (Menkhoff et al., 2012). This difference could arise because guarantees are less costly than the gathering and processing of information through relationships with borrowers.
Moreover, to some extent, the monitoring function is transferred to the guarantor, who has personal wealth embedded in the contract (De Haas and Millone, 2020). Consequently, guarantees are particularly useful in ameliorating moral hazards. De Haas and Millone (2020) find that guarantees are common amongst first-time borrowers but decline as the lender–borrower relationship matures.
Financing from friends and family plays a more significant role in developing countries. Beck and Demirguc-Kunt (2006) argue that SMEs in such countries circumvent market failures associated with financial markets by developing long-term business relationships and ethnicity-based business networks. Such networks help overcome the problems of asymmetric information and weak contract enforcement systems prevalent in developing countries. Informal sources of finance are essential for SMEs (Beck and Demirguc-Kunt, 2006; Nguyen and Canh, 2021). Informal debt, for instance, allows SMEs to access credit relatively quickly and with low initial transaction costs (Nguyen and Canh, 2021). The disadvantage, however, is that the effective interest rates are very high, and credit is not always readily available. For example, finance reliant on ethnic relationships is discriminatory and will only benefit a specific group of SMEs. Even when the borrower is an insider, the available quantities of credit may not be adequate for growth. Consequently, other factors need to be considered in addressing the access to finance for SMEs (Kuada, 2021). This paper posits that social capital is one of the essential factors in improving access to credit in line with a growing literature.
Why social capital can allow SMEs to overcome financial constraints
Research that looks at the impact of capital on business has shown that social capital positively affects the likelihood of getting a loan for business ventures (Fogel et al., 2018; Lee et al., 2019). The main driver identified for this effect is social capital's role in the flow of information. Improved information flow can benefit financial markets since they are fraught with information asymmetries but highly dependent on information to allocate resources. The literature identifies two main categories of lending techniques: transactions lending and relationship lending (Berger and Udell, 1995). In both cases, information plays a significant role in evaluating potential borrower risk. Transaction lending relies on hard factual and publicly available information independent of the relationship between the borrower and the lender. On the other hand, relationship lending relies mainly on private information obtained through relationships between the lender and the borrower.
As indicated above, SMEs are highly informationally opaque, with very little publicly available information about their cash flows, transactions and performance. As a result, the scale and scope for hard information in SMEs are limited. Therefore, transaction lending to SMEs results in high transaction costs (due to high screening costs) and a high level of failed contracts (Boot and Thakor, 2000; Jackowicz et al., 2021). The literature suggests that relationship-based lending, which relies mainly on economically meaningful social networks, can reduce the information opacity of SMEs and result in the amelioration of inefficiencies caused by information asymmetry (Javakhadze et al., 2016). This effect can be channelled through the collection of soft information, trust, the creation of social collateral and affecting the risk attitudes of SME managers.
Soft information is gathered through repeated interaction between the lender and borrower. This interaction results in the development of economically meaningful information, which aids the lender in distinguishing between bad and good borrowers. In addition, soft information such as knowledge about the management skills and character of the owners or managers can give the SME better bargaining power. Grunert and Norden (2012) argue that as a result, soft information not only positively affects the rating levels of SMEs but also affects the loan terms that an SME can obtain. Moreover, Agarwal et al. (2011) demonstrate that negotiations between the borrower and lender can affect ex-post default risk and loan pricing. Related, soft information enables banks to provide intertemporal smoothing of contract terms (Lončarski and Marinč, 2020). The lender can use this information to respond to changes in the lifecycle of the borrower.
In addition to this, social capital helps to promote trust and reciprocity. In an environment where uncertainty and dependence are standard features of exchange relationships, as with SME lending, especially in developing countries, trust can be a governance mechanism that minimises opportunism (Hasan et al., 2020; Jackowicz et al., 2021). Furthermore, Costa E Silva et al. (2012) argue that trust can encourage business partners to explore the advantages of cooperation. The resulting reciprocity promotes a high trust environment conducive to accumulating more reliable soft information and better risk hedging (Postelnicu et al., 2014). As a result, the problems associated with moral hazards can be minimised.
Social capital can also affect an entrepreneur's risk attitude. Carolis and Saparito (2006) argue that repeated interactions directly with others and indirectly through networks can make entrepreneurs more willing to take risks in an exchange. The repeated exchange allows for the development of mutual trust, which makes an entrepreneur seek out external financing and make themselves vulnerable to the provision of information about themselves and the business (Carolis and Saparito, 2006; Lončarski and Marinč, 2020). A borrower must trust that the lender will not use the provided information to the borrower's disadvantage or share such information with competitors (Lončarski and Marinč, 2020). An empirical study by Jackowicz et al. (2021) finds that SMEs attach significant importance to trust-related factors when making borrowing choices. They are more likely to apply for a loan to a bank where they have a relationship with the bank employees. In addition, they are more likely to cooperate with lenders.
Consequently, information and agency problems of moral hazard and adverse selection are minimised. Nguyen and Canh (2021) and Fraser et al. (2015) argued that cognitive social capital constraints could induce a conservative mindset, a risk-averse attitude and low motivation for development. Therefore, removing such barriers will improve the probability of entrepreneurs seeking out financial capital and using it more effectively.
Additionally, social capital can act as loan collateral. The group lending model popularised by the Grameen bank is built on this premise. In such cases, network members can use this information for peer mentoring, resulting in decreased ex-post moral hazard. Moreover, members are likely to behave in a way that preserves their social capital because defaulting can result in significant sanctions such as loss of networks and reputation. The self-reinforcing nature of networks means that these adverse effects can be transmitted through networks and hurt present prospects of accessing finance and future borrowing (Postelnicu et al., 2014). For instance, Simatele and Dlamini (2020) show that microfinance institutions in Eswatini use social ties within chieftainships called Umphakatsi to screen potential borrowers and enforce repayments. Defaulters are not recommended for further loans and can also be excluded from group borrowing. Liu et al. (2020) also demonstrate that the emergence of peer to peer (P2P) lending can allow SMEs to substitute collateral requirements with social capital.
Theoretical framework and hypotheses testing
Building on the discussions above, it is important to draw attention to two predominant theories that explain the use of capital structure in small businesses and therefore inform the hypotheses in this study. These include the financial lifecycle theory (Weston and Brigham, 1970) and the pecking order theory (Myers and Majluf, 1984). The financial life cycle theory suggests that a firm's financing choices mirror the different stages of its development; a small firm will primarily rely on owner funding in the early stages. On the other hand, the pecking order theory emphasises the role of information asymmetries. As noted earlier, the information opacity of SMEs results in high transaction costs, and therefore, SMEs will prefer, in the first instance, to resort to retained earnings in preference to debt. Berger and Udell (1998) combined these two theories to explain SMEs' capital structure. They argue that lenders use various instruments such as collateral, covenants and guarantees to circumvent the lack of information. The result is high transaction costs, so SMEs would prefer to use their funds first. As the SME grows, the preference is for retained earnings. When the firm becomes established, the accumulated assets that can be used as collateral and the increased information availability on the SME make debt more preferred.
The Berger and Udell (1998) approach is most relevant for small businesses in an African country like Cameroon. The reason is that information asymmetries in financial markets are ubiquitous, and the screening methods used by banks tend primarily to ration small businesses out of the credit markets. Hence, many SMEs rely on their savings, family, and close friends or cooperatives to access funding. As firms grow, they should be less opaque and have more resources to use as collateral and, therefore, externally access finance. The paper proposes that social capital works as information enhancing resource, allowing SMEs to be more transparent to potential lenders and a consequent increase in access to external funding. In line with the literature, we recognise that social capital is multifaceted (Nahapiet and Ghoshal, 1998; Putman, 2001). The literature draws a distinction between cognitive, structural and relational social capital. These differences are explained below and have guided our hypotheses formulation and the empirical investigation.
Cognitive social capital
Cognitive social capital is the value created by shared representations, interpretations and systems of meaning between individuals and businesses (Nahapiet and Ghoshal, 1998). This dimension of social capital focusses on how network members exchange information and discuss business, resulting in the creation of shared values, attitudes and underlying subtleties which create legitimacy. Exposure to stakeholders such as microfinance institutions and banks allows SMEs to develop relationships and share economically meaningful information. In this way, uncertainty is reduced, intellectual capital is enhanced and SME performance is likely to improve. In addition, cognitive social capital allows for better communication skills, leading to improved information flows, problems and conflict management identification, and improved business performance. Conversely, if an SME is constrained in cognitive social capital, its access to financial capital and related business performance will be limited. We, therefore, posit five hypotheses related to cognitive capital.
Informal financial constraints are positively associated with higher levels of constraints to cognitive social capital.
Formal financial constraints are positively associated with higher levels of constraints to cognitive social capital.
Constraints to cognitive social capital moderate the effect of informal financial capital on business performance.
Constraints to cognitive social capital moderates the effect of formal financial capital on business performance.
Constraints to cognitive social capital are negatively correlated with SME performance.
Structural social capital
Structural social capital describes the configuration of networks between individuals and firms, including the presence or absence of such networks and the overall pattern of connections between actors (Lee et al., 2019; Nahapiet and Ghoshal, 1998). This type of social capital affects how actors in a network participate in knowledge exchange and knowledge activities. When networks are wholesome, knowledge diffusion is efficient. On the other hand, information flow is disturbed when networks are weak and disconnected. SMEs whose relationships with potential funders are porous will have limited ability to gather knowledge about funding opportunities. Therefore, porosity in networks will likely negatively affect performance. As such, we propose the following:
Informal financial constraints are positively associated with higher levels of constraints to structural social capital.
Formal financial constraints are positively associated with higher levels of constraints to structural social capital.
Constraints to structural social capital moderate the effect of informal financial capital on business performance.
Constraints to structural social capital moderates the effect of formal financial capital on business performance.
Constraints to structural social capital are negatively correlated with SME performance.
Relational social capital
Lastly, relational social capital is developed from repeated interaction with others. This repeated interaction creates trust and respect. Increased trust means that the different actors will be more willing to cooperate and share resources (Gulati, 1995; Nahapiet and Ghoshal, 1998; Kheng and Minai, 2016), resulting in reduced transaction costs, enhanced knowledge sharing and more competitive advantages (Zhang et al., 2010). Additionally, repeated interaction with funders will reduce uncertainty, and SMEs will be more likely to disclose information about their businesses. The reduced uncertainty will mitigate information asymmetry and agency problems leading to better SME performance. For this reason, we propose additional hypotheses.
Informal financial constraints are positively associated with higher levels of constraints to relational social capital.
Formal financial constraints are positively associated with higher levels of constraints to relational social capital.
Constraints to relational social capital moderate the effect of informal financial capital on business performance.
Constraints to relational capital moderates the effect of formal financial capital on business performance.
Constraints to relational social capital are negatively correlated with SME performance.
Methods and data
The data were collected from formal sector small businesses in Yaoundé and Douala. Formal small businesses or traders in the Cameroonian definition include those registered businesses which employ between 6 and 20 people and have a turnover of between 15 million and 250 million Franc de Coopération Financière en Afrique centrale (FCFA) [2] (Nkaku Policy Institute, 2019). All the respondents in the sample were from the service industry. The service industry houses the bulk of the SMEs in Cameroon. It is estimated that there are between 5,000 and 16,000 small businesses in the tertiary sector. The average national SME population of 10,500 formal small businesses in the tertiary sector was used to calculate the sample size of 370 businesses, with 185 respondents sampled from each location. Data were collected in October 2017 using a structured questionnaire. The questionnaire was administered in French as most respondents in Yaoundé and Douala were French-speaking. However, an English version of the questionnaire was also made available. The data used in this paper were collected using Likert-style questions (see below for further discussion). Figure 1 shows the number of respondents per category. The majority of the respondents were in the restaurant business, followed by pubs and livestock [3].
Characteristics of the respondents
The demographic profile in Table 1 indicates that about 64% of the surveyed respondents were males, and 36% were females. The majority of them were aged between 35 and 55 years. Nearly 70% of them had at least some secondary school education, with 41.4% of the respondents having a bachelor's degree. Less than 20% of the respondents had more than 12 years of work experience in the area they were operating.
Measurement of variables
The variables are measured using a Likert scale ranging from 1, indicating a strong disagreement, to 7, reflecting the strongest agreement. The table of items included in the measurement of each scale is shown in Appendix 1.
Business performance
Business performance (BP) is measured using three items. Firstly, it is measured by the growth in employment numbers (BP1) over the two years 2016–2017. Secondly, it is measured by the growth in sales revenue over the same period (BP2). Lastly, it is measured by the growth in profits over the same period (BP3). The BP construct was reliable, with an alpha coefficient of 0.791.
Social capital
The value of different types of social ties is not the same (Guo et al., 2021; Jackowicz and Kozłowski, 2019). Therefore, as discussed above, it is essential to disentangle the different types of social capital embodied in SMEs' different relationships. Firstly, relational constraints are measured to capture constraints related to any resources involved in the relationship between the SME and its stakeholders. In line with the literature on social capital and SME performance, the relational capital measures focussed on the importance of relationships related to financing (Putman, 2001). Six items were used to measure relational capital, including a relationship with government (SCC1), relationship with friends and family (SCC2), relationship with banks (SCC3), internal communication (SCC7), relationship with microfinance institutions (SCC9) and access to information (SCC13). SCC7 and SCC13 were dropped in the analysis due to low coefficient loadings.
Four items were used to measure social cognitive constraints (ScoC). Two items (SCC10 and SCC6) capture the existence of perceptual tools; the extent to which SMEs believe they receive care and acceptance from relevant networks. These perceptions form the frame of reference from which SMEs observe and interpret their environment to assess whether the interactions have any benefits (Nahapiet and Ghoshal, 1998). The other two items (SCC5 and SCC12) measure the extent to which SMEs believe that they are part of a group of business networks with shared beliefs and values. SCC10 and SCC12 were dropped in the analysis due to low coefficient loadings. The social structural constraints (SSC) are measured in line with the literature (Lee et al., 2019) through network density (SSC4), diversity (SCC8) and network size (SCC11).
Financial capital
As noted above, the impact of formal finance on performance differs from the impact of informal finance. Therefore, financial capital constraints (FinC) are disaggregated into formal and informal finance. Informal financial constraints include lack of savings (FC1), poor access to loans from friends and families (FC2) and poor access to loans from cooperatives (FC5). Formal financial constraints are measured by poor access to government loans (FC3) and bank loans (FC4).
Results and discussion
Model reliability
The reliability and validity of the constructs were verified using confirmatory factor analysis. Reliability and internal consistency were measured using Cronbach's alpha coefficient and the composite reliability (CR) index. Table 2 shows the factor loadings for the five constructs (see Appendix 3) and the reliability and validity statistics. A Cronbach's Alpha coefficient and CR index of 0.7 are generally accepted as indicating internal consistency. Both the Cronbach Alpha coefficients and CR measures are above 0.7 [4]. Validity is measured by average variance extracted (AVE). The Fornell and Larcker (1981) criterion was used to calculate the AVE. All AVE coefficients were above the 0.5 threshold (Bagozzi and Yi, 1988; Fornell and Larcker, 1981). In addition, (Hair et al., 2014) argues that if the CR is above the AVE, then convergent validity is confirmed. All the CR indices are above the AVE, indicating a good level of convergent validity.
Structural model
Before conducting the structural equation modelling (SEM), diagnostic tests, such as multicollinearity and heteroscedasticity, were checked. No multicollinearity problems were detected as all social capital and financial items had very low correlation coefficients (See Appendix 2). In addition, no heteroscedasticity problem was found. As shown in the scatter plot (See Appendix 2), the line designed was relatively straight, which indicates that the dataset is homoscedastic.
The fit of the overall model was assessed using standard goodness of fit measures. The chi-square test is used as a measure of absolute fit. The literature suggests that a value lower than 5.0 is a good indicator of overall fit (Hair et al., 2014). The value of the chi-square of the model is 3.34, which is less than five, indicating a good overall fit. Additionally, relative fit indices were used to evaluate the model. The Comparative Fit Index (CFI) and the Tucker–Lewis Index in the model were above the 0.95 thresholds indicating a good fit. The root mean square error of approximation (RMSEA) was used as a parsimony measure. The value in the model is 0.046, which falls below the threshold values of 0.05 and 0.08 suggested in the literature (Hair et al., 2014).
The hypothesised relationships are tested within a structural equation model. The estimated model is shown in Figure 2. Of the fifteen hypotheses examined, nine referred to the path estimates shown in Table 3. The other six hypotheses (H1c, H1d, H2c, H2d, H3c and H3d), which refer to the mediating relationship, are discussed below. Seven out of the nine hypotheses are supported, two only at the 10% level. Structural social capital is positively and significantly correlated with informal finance but only weakly with informal finance. On the other hand, relational capital is significant for both informal and formal finance.
Cognitive social capital is only correlated with formal finance with a coefficient of 0.04 at 10% significance. Since cognitive social capital promotes homogeneity due to its emphasis on shared knowledge, norms and values, it will affect entrepreneurial orientation. The result will be a positive attitude towards business. Therefore, SMEs will be more self-motivated and more likely to seek out formal finance than informal finance. The limited impact of cognitive social capital on resource accumulation has also been noted in the literature. For example, Lee et al. (2019) and Eiteneyer et al. (2019) show that cognitive social capital has a limited impact on resource acquisition.
Hypotheses H2a and H2b are supported, showing a strong relationship between structural social capital and informal and formal finance. However, the relationship between informal finance and structural capital is only significant at the 10% level. Structural social capital is positively related to financial capital, in line with the literature. Strong and intense social interaction can promote access to formal and informal finance. SMEs tend to operate in dense network environments. Therefore, large networks promote access to finance.
Moreover, structural dimensions of social capital promote networks' stability, which is important for accessing informal finance. Both hypotheses that refer to relational capital are supported. The coefficients between relational capital and formal and informal finance are positive and significant at the 5% significance level. This strong relationship is evidenced in the literature. For instance (Lee et al., 2019) find that relational capital has the most substantial and most consistent effect on resource acquisition.
Mediation effect of social capital constraints
The central argument of this paper is that the various dimensions of social capital moderate the effect of financial capital on business performance. The results in Table 4 essentially confirm this. Overall, the impact of formal financial constraints on business performance is more significant than that of informal finance constraints.
The mediation of financial capital is only significant through structural and relational capital. The effect of cognitive capital is insignificant. As measured in this study, structural social capital emphasises the density, diversity and size of the networks that SMEs have. Our results show that constraints to structural social capital mediate the impact of informal finance and formal finance on business performance by a factor of −0.004 and −0.01, respectively and on business performance directly by a factor of −0.055. Therefore, SMEs with low network density (i.e. porous networks with very little connectivity) cannot draw any economically meaningful information to enhance access to financial resources and, ultimately, business performance. The literature supports this view. For instance, Ojong and Simba (2019) indicate that increasing the number of group meetings in a group lending model enriches network density and bridges informational gaps. The result is an increased number of appropriable relationships and better access to information related to finance and markets.
The relational capital variable is significant as a mediator of finance. Relational capital captures the trust, reciprocity and obligations or expectations that develop in a network. The path estimates in Table 3 show that of the three dimensions of social capital, relational social capital has the most significant effect on both formal and informal finance and business performance. The path estimate to business performance for relational capital is −0.413 relative to −0.055 for structural social capital, while cognitive social capital is insignificant.
Similarly, the mediation effect is larger in magnitude with an estimate of 0.004 for structural capital relative to 0.009 for relational capital through informal finance and 0.01 for structural social capital relative to −0.105 for relational capital. Our results emphasise the importance of access to information and relationships with government, banks and family and friends and the role of the level of trust and confidence that SMEs have in such relationships. Where SMEs cannot meaningfully appropriate these relationships, either because they do not trust them or because they are too risk-averse to engage, access to financial resources and its ultimate impact on performance will be reduced. Our results are aligned with those in the literature, which indicate, for instance, that both relational and structural capital are positively associated with the acquisition of resources (Lee et al., 2019). These findings are also in line with the literature that shows that these dimensions of capital influence lending outcomes through borrower credibility, group inclusiveness and trust (Chen et al., 2016; Lin and Prabhala, 2013).
Both formal and informal finance constraints have a negative and significant impact on business performance for SMEs in Cameroon. The impact of formal finance is only significant at the 10% level. The magnitude of the overall impact is larger for informal finance than it is for formal finance. This result could reflect the significant reliance of SMEs on informal finance in Cameroon. The indirect or mediation effects are larger for formal finance than informal finance.
Conclusion and implications
The paper investigated the role of social capital as a mediator for the influence of financial capital on business performance using a sample from Cameroon. We find that structural and relational capital constraints negatively correlate with business performance. Notably, the effects of relational capital emerge as the most significant mediator for formal and informal finance and its direct effect on business performance.
From a practical viewpoint, the study confirms the relevance of a holistic approach to easing financial capital constraints for SMEs. The study sample is drawn from SMEs in the service sector. SMEs in the service sector are more likely to rely on dense networks, amplified through multiple connections sometimes passed on by word of mouth. In this case, transparent connections between appropriable relationships will likely improve access to funding and, ultimately, business performance. Moreover, SME information opacity can be reduced with better relational capital. Trust increases, and risk-averseness decreases, resulting in better information and access to finance. When relational social capital is constrained, on the other hand, access to finance can be hindered, resulting in poor overall business performance. The item estimates in the results suggest that relationships with the public sector and access to information are the most important aspects of relational capital.
Therefore, the government needs to find ways to encourage appropriable relationships. Government can create opportunities for small businesses to interact, akin to business chambers common amongst larger enterprises. Rotating savings and credit associations, popularly known as djanggi or tontines in Cameroon, provide a basic framework that can be developed. The advantage of tontines is that they almost entirely rely on social capital for entry, circumventing the information asymmetry problems. Secondly, tontines provide a very good environment for developing bonds and shared values and norms that can reinforce the network effects. Third, tontines typically provide interest-free financing, which can benefit SMEs. In addition, the government can improve the development of social capital through increasing education about funding opportunities available to SMEs. Our results show that network density has the largest effect on structural social capital. This result underpins the critical nature of interpersonal connections in business. SMEs typically operate in high-density environments with similar businesses in very close proximity. However, it is not clear whether SMEs take advantage of such proximity to maximise community learning. Most of them operate in competition with one another. Therefore, intervention by the government would be important to highlight the advantage of networks through training and group support. The resulting bonds could help SMEs shift away from traditional funding sources, which tend to ration them out of the credit market.
From a research perspective, understanding the role of specific items of structural and relational capital as mediators of access to finance and its ultimate impact on business performance needs to be understood further. Moreover, disaggregating the items that compose the different dimensions of social capital might also highlight some aspects of cognitive capital that might be important in the Cameroonian context. The results would help identify intervention points for the governments in Cameroon as it seeks to use SMEs as its pivot for development and to catapult itself to emerging economy status in its Cameroon 2035 vision.
The data used in the study included only two regions (Yaounde and Douala) of Cameroon. There may be important regional nuances that could be captured by including more regions. Moreover, the static analysis of the paper means that the dynamic effects of transitioning from informal to formal finance could not be captured. Our results show that informal finance has a more significant impact on performance than formal finance. This result could explain findings such as those of Beck et al. (2015) and Ann et al. (2016), which show that many SMEs tend to use both formal and informal finance concurrently, given the specific efforts that the Cameroonian government is making to increase the formalisation of This missed were considered due to resource and time constraints. Other regions would have added more value to the survey if they had been included. As a result, caution is required, as the results cannot be generalised to the entire Cameroonian service sector. Furthermore, the study was limited to only formal traders who employed between 6 and 20 people operating in the service sector. In addition, a panel data or time-series approach could not be considered due to the paucity of data on small businesses in Cameroon. Hence, the paper was limited to a cross-sectional approach, providing a foundation for further research.
Figures
Demographic profile of respondents
| Valid percent | ||
|---|---|---|
| Gender | Male | 63.8 |
| Female | 36.2 | |
| Age | 18–25 | 3.2 |
| 26–35 | 16.2 | |
| 36–45 | 49.2 | |
| 46–55 | 27.0 | |
| 56–65 | 4.1 | |
| Above 65 | 0.3 | |
| Education level | Some/all Primary education | 6.2 |
| Some/all Secondary education | 24.6 | |
| Diploma or Certificate | 19.7 | |
| Bachelor's degree | 41.4 | |
| Postgraduate degree | 8.1 | |
| Work experience | No experience | 1.6 |
| Less than one year | 4.3 | |
| From one to less than four years | 26.8 | |
| From four to less than eight years | 35.9 | |
| From eight to less than 12 years | 20.0 | |
| From twelve to less than 16 years | 7.0 | |
| 16+ years | 4.3 |
Source(s): Authors' own compilation
Construct reliability, items and loadings
| Constructs | AVE | CR | Cronbach's alpha | Items | Loadings |
|---|---|---|---|---|---|
| Relational social constraints (RSC) | 0.50 | 0.794 | 0.720 | Relationship with government (SCC1) | 0.811 |
| Relationship with banks (SCC3) | 0.724 | ||||
| Relationship with friends and family (SCC2) | 0.650 | ||||
| Access to information (SCC13) | 0.610 | ||||
| Social cognitive constraints (SCoC) | 0.53 | 0.70 | 0.706 | Lack of self-motivation (SCC5) | 0.751 |
| Mental attitude towards business (SCC6) | 0.704 | ||||
| Social structure constraints (SSC) | 0.50 | 0.745 | 0.710 | Network size (SCC11) | 0.756 |
| Network diversity (SCC8) | 0.722 | ||||
| Network density (SCC4) | 0.627 | ||||
| Informal finance constraints (FinC1) | 0.517 | 0.757 | 0.70 | Access to loans from cooperatives (FC5) | 0.835 |
| Loan from friends and families (FC2) | 0.747 | ||||
| Lack of savings (FC1) | 0.544 | ||||
| Formal financial constraints (FinC2) | 0.765 | 0.867 | 0.723 | Access to loans from government (FC3) | 0.889 |
| Access to loans from banks (FC4) | 0.860 |
Source(s): Authors' own compilation
Path estimates
| Hypothesis | Path specification | Coefficient | Standard error | p-value | Supported |
|---|---|---|---|---|---|
| H1a | FinC1 → (+) SCoC | 0.13 | 0.024 | 0.842 | No |
| H1b | FInC2 → (+) SCoC | 0.04 | 0.029 | 0.089 | Yes |
| H1e | SCoC → (−) BP | −0.75 | 0.369 | 0.134 | No |
| H2a | FinC1 → (+) SSC | 0.08 | 0.011 | 0.084 | Yes |
| H2b | FinC2 → (+) SSC | 0.18 | 0.015 | 0.002 | Yes |
| H2e | SSC → (−) BP | −0.055 | 0.610 | 0.077 | Yes |
| H3a | FinC1 → (+) RSC | 0.021 | 0.037 | 0.023 | Yes |
| H3b | FinC2 → (+) RSC | 0.253 | 0.059 | 0.012 | Yes |
| H3e | RSC → (−) BP | −0.413 | 0.201 | 0.026 | Yes |
Note(s): CFI = 0.951; TLI = 0.982; RMSEA = 0.046
Mediation effects
| FinC1 → SC | Mediation through informal finance (FinC1) FinC1 → BP(−) = –0.261*** | Mediation through formal finance (FinC2) FinC2 → BP(−) = –0.02* | Total effect | ||||
|---|---|---|---|---|---|---|---|
| Hypothesis | Indirect effects | Total effect | Hypothesis | Indirect effects | |||
| Cognitive | −0.75 | H1c | −0.098 | −0.372 | H1d | −0.03 | −0.164 |
| Structural | −0.055* | H2c | −0.004 | H2d | −0.01 | ||
| Relational | −0.415** | H3c | −0.009 | H3d | −0.105 | ||
Note(s): *** = p value significant at 0.001; ** = p value significant at 0.05; * = p value significant at 0.1; SSC = Structural social constraints; SCoC = social-cognitive constraints and RSC = relational social constraints
Description and measurement of variables
| Variables | Description | Measurement |
|---|---|---|
| Relationship with government (SCC1) | The connection between the public sector and SME | Likert scale of 7 (Strongly disagree – SD to strongly agree – SA) |
| Relationship with family and close friends (SCC2) | The connection between family and close friends and SME | Likert scale of 7 (SD–SD) |
| Relationship with banks (SCC3) | The connection between the bank sector and SME | Likert scale of 7 (SD–SD) |
| Network density (SCC4) | The extent to which direct ties connect all members in the network (that is, who is directly connected to whom in the social network structure) | Likert scale of 7 (SD–SD) |
| Lack of self-motivation (SCC5) | The extent to which SMEs struggle to cope with the business affects their confidence | Likert scale of 7 (SD–SD) |
| Attitude towards business (SCC6) | State of mind reflecting feelings, beliefs and values of SMEs towards business | Likert scale of 7 (SD–SD) |
| Internal communication within my business (SCC7) | Dissemination of information, promotion of collaboration within the business | Likert scale of 7 (SD–SD) |
| Network diversity (SCC8) | Looking at the heterogeneity of the network | Likert scale of 7 (SD–SD) |
| Relationship with microfinance (SCC9) | The connection between the microfinance sector and SME | Likert scale of 7 (SD–SD) |
| Emotional support (SCC10) | Extend to which SMEs receive care, reassurance/backing, empathy, comfort and acceptance from others | Likert scale of 7 (SD–SD) |
| Network size (SCC11) | The number of SMEs inside the network. It is also the number of different SMEs engaging with others on business matters when performing business activities | Likert scale of 7 (SD–SD) |
| Level of trust in public institutions (SCC12) - | The extent to which SMEs have faith or confidence in their public institutions | Likert scale of 7 (SD–SD) |
| Access to information (SCC13) | Ability to get adequate information for running the business | Likert scale of 7 (SD–SD) |
| Lack of savings to run this business (FC1) | The extent to which SMEs lack financial investment or money put aside for the business | Likert scale of 7 (SD–SD) |
| Loans from families and close friends support my business (FC2) | The ability to get funds from families and close friends to support the business | Likert scale of 7 (SD–SD) |
| Access to loans from the government to run this business (FC3) | The ability to get funds from the government to support the business | Likert scale of 7 (SD–SD) |
| Access to loans from banks to run this business (FC4) | The ability to get funds from banks to support the business | Likert scale of 7 (SD–SD) |
| Access to loans from cooperative/microfinance to run this business (FC5) | The ability to get funds from cooperatives/microfinance to support the business | Likert scale of 7 (SD–SD) |
| Business Performance in terms of employment (BP1) | The extent to which a firm can use its resources to generate employment | Number of people |
| Performance in terms of profits (BP2) | The extent to which a firm can use its resources to generate profits | In FCFA, monetary value |
| Performance in terms of sales (BP3) | The extent to which a firm can use its resources to generate sales revenues | In FCFA, monetary value |
Test for Multicollinearity
| Correlation analysis of variables | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SCC1 | SCC11 | SCC3 | SCC2 | SCC5 | SCC6 | SCC4 | SCC8 | SCC13 | SCC7 | SCC9 | SCC10 | SCC12 | FC1 | FC2 | FC3 | FC4 | FC5 | |
| SCC1 | 1.00 | |||||||||||||||||
| SCC11 | 0.53 | 1.00 | ||||||||||||||||
| SCC3 | 0.41 | 0.45 | 1.00 | |||||||||||||||
| SCC2 | 0.23 | 0.26 | 0.36 | 1.00 | ||||||||||||||
| SCC5 | 0.18 | 0.20 | 0.11 | 0.07 | 1.00 | |||||||||||||
| SCC6 | 0.14 | 0.30 | 0.20 | 0.20 | 0.19 | 1.00 | ||||||||||||
| SCC4 | 0.14 | 0.01 | 0.06 | 0.12 | 0.14 | 0.12 | 1.00 | |||||||||||
| SCC8 | 0.22 | 0.30 | 0.25 | 0.15 | 0.18 | 0.24 | 0.22 | 1.00 | ||||||||||
| SCC13 | 0.29 | 0.36 | 0.31 | 0.14 | 0.14 | 0.15 | 0.22 | 0.08 | 1.00 | |||||||||
| SCC7 | 0.09 | 0.03 | 0.07 | 0.07 | 0.08 | 0.07 | 0.18 | 0.07 | 0.18 | 1.00 | ||||||||
| SCC9 | 0.28 | 0.34 | 0.14 | 0.01 | 0.02 | 0.08 | 0.16 | 0.06 | 0.26 | 0.20 | 1.00 | |||||||
| SCC10 | 0.01 | 0.05 | 0.02 | 0.11 | 000 | 0.00 | 0.00 | 0.14 | 0.26 | 0.20 | 0.11 | 1.00 | ||||||
| SCC12 | 0.03 | 0.00 | 0.06 | 0.04 | 0.01 | 0.00 | 0.05 | 0.02 | 0.01 | 0.11 | 0.18 | 0.30 | 1.00 | |||||
| FC1 | 0.03 | 0.00 | 0.08 | 0.11 | 0.01 | 0.01 | 0.01 | 0.06 | 0.00 | 0.19 | 0.26 | 0.30 | 0.67 | 1.00 | ||||
| FC2 | 0.21 | 0.23 | 0.06 | 0.09 | 0.00 | 0.17 | 0.01 | 0.01 | 0.05 | 0.21 | 0.23 | 0.23 | 0.60 | 0.27 | 1.00 | |||
| FC3 | 0.21 | 0.23 | 0.05 | 0.11 | 0.10 | 0.04 | 0.09 | 0.15 | 0.24 | 0.12 | 0.58 | 0.12 | 0.22 | 0.22 | 0.28 | 1.00 | ||
| FC4 | 0.25 | 0.26 | 0.12 | 0.10 | 0.10 | 0.05 | 0.06 | 0.13 | 0.29 | 0.16 | 0.64 | 0.15 | 0.26 | 0.07 | 0.26 | 0.28 | 1.00 | |
| FC5 | 0.25 | 0.26 | 0.12 | 0.10 | 0.05 | 0.06 | 0.05 | 0.08 | 0.10 | 0.16 | 0.24 | 0.17 | 0.27 | 0.20 | 0.39 | 0.30 | 0.34 | 1.00 |
Source(s): Result obtained from SPSS
KMO and Bartlett's test on constraints to small business performance
| KMO and Bartlett's test | ||
|---|---|---|
| Kaiser-Meyer-Olkin measure of sampling Adequacy | 0.729 | |
| Bartlett's test of Sphericity | Approx. Chi-Square | 1310.280 |
| Df | 91 | |
| Sig | 0.000 | |
Source(s): Own computation, results obtained from SPSS
Total variance explained on constraints to small business performance
| Total variance explained | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
| Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
| 1 | 3.374 | 24.101 | 24.101 | 3.374 | 24.101 | 24.101 | 2.525 | 18.036 | 18.036 |
| 2 | 2.134 | 15.245 | 39.346 | 2.134 | 15.245 | 39.346 | 2.252 | 16.085 | 34.122 |
| 3 | 1.242 | 8.874 | 48.220 | 1.242 | 8.874 | 48.220 | 1.882 | 13.446 | 47.567 |
| 4 | 1.087 | 7.556 | 55.776 | 1.087 | 7.556 | 55.776 | 1.149 | 8.209 | 55.776 |
| 5 | 1.058 | 6.767 | 62.543 | 1.058 | 6.767 | 62.543 | 1.129 | 7.209 | 62.543 |
| 6 | 0.841 | 6.006 | 68.549 | ||||||
| 7 | 0.786 | 5.615 | 74.164 | ||||||
| 8 | 0.762 | 5.444 | 79.609 | ||||||
| 9 | 0.692 | 4.941 | 84.550 | ||||||
| 10 | 0.603 | 4.307 | 88.857 | ||||||
| 11 | 0.529 | 3.778 | 92.635 | ||||||
| 12 | 0.470 | 3.358 | 95.993 | ||||||
| 13 | 0.424 | 3.030 | 99.022 | ||||||
| 14 | 0.137 | 0.978 | 100.000 | ||||||
Note(s): Extraction Method: Principal Component Analysis
Source(s): Own computation, results obtained from SPSS
Notes
M-Pesa is a mobile money service mainly operating in east Africa and is jointly operated by Vodacom and Safaricom.
Approximately between 25,000 and 431,000 US dollars at the December 2021 exchange rate
Livestock here refers to the business of helping butchers slaughter animals, cleaning, and cutting the meet on request.
The factor loadings for items SCC7, SCC9, SCC10 and SCC12 were below 0.4. These items were dropped in the final measurement model.
Membership in a cooperative already assumes the existence of shared values and norms and is therefore unlikely to be a significant factor or constrained in accessing finance from a cooperative.
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