Examining the effect of intellectual capital efficiency on microfinance institutions’ performance: the case of a developing economy Open Access

The discourse pertaining to the relevance of microfinance institutions (MFIs) in economies, particularly in developing economies, has been well and clearly espoused in literature. MFIs are financial institutions whose business model focuses on the poor and less income earners, who are usually found in rural and peri-urban areas (Koveos and Randhawa, 2004). They provide financial services and products including savings, loans and financial education to their clients. Essentially, in overall terms, MFIs are regarded as a developmental tool which ensures poverty alleviation, empowerment of micro, small and medium enterprises (MSMEs), educational empowerment, women empowerment, financial inclusion and economic growth and development (Okibo and Makanga, 2014; Koveos and Randhawa, 2004).

Given the aforementioned pros advanced in relation to MFIs, it is not surprising that the sector is conspicuous and thriving in most economies particularly in developing ones, albeit with some challenges. For these MFIs to continue to provide their services/products and ultimately be sustainable to produce economic growth and development, it is imperative that they have positive performance outcomes from time to time. In this regard, there have been extensive discourse pertaining to those factors that have the ability to affect the performance of MFIs. Evidence has it that issues – such as corporate governance (Gohar and Batool, 2015; Gupta and Mirchandani, 2020; Badu, 2022), ownership (Khan et al., 2021), management team gender diversity and board gender diversity (Gudjonsson et al., 2020), organizational structure and variations in legal systems (Mumi et al., 2020), start-up microenterprise financing (Adusei and Adeleye, 2021), competition (Wondirad, 2020) and diversification (Ben Salem and Ben Abdelkader, 2023) – have implications for performance of MFIs.

Notwithstanding the extensive and interesting insight provided by the body of work pertaining to factors that drive MFIs’ performance, empirical studies that show the role an intangible asset or knowledge-based factor such as intellectual capital efficiency (ICE) plays in the MFIs’ performance discourse is limited, if not lacking, especially considering the developing context of Africa and by extension Ghana. Indeed, it is worth acknowledging that some few studies exist on the subject matter of ICE and MFI performance mainly from the Asian context (Barpanda and Bontis, 2021; Mahaputra et al., 2021; Hashim et al., 2018) and a notable one by Kamukama et al. (2010) from Uganda. There is also evidence of studies that focused on global sample (Ahamad et al., 2023; Githaiga et al., 2023). Departing from this aforementioned stream of empirical works, the purpose of this study is to contribute to extant literature in the area of intellectual capital and microfinance by investigating whether ICE affects the performance of MFIs in Ghana.

One of the key factors acknowledged as key in the creation of firm value is intangible asset (i.e. intellectual capital). The intellectual capital among other things consists of the unique and valuable knowledge, expertise, experience and training of the human capital that translates into structural capital. That is, through the deployment of experience, expertise and skills of the human capital, organizations are able to achieve competitive advantage which leads to superior performance as resource efficiency and firm structural and procedural integrity are achieved. From the preceding narrative, it is undoubtedly the case that intangible resources/assets (i.e. intellectual capital) have a role to play in the performance hence success of organizations, although empirical research is yet to confirm this in the case of firms operating in the MFI sector of Ghana. The study therefore seeks to provide answer to the research question “What is the role of intellectual capital efficiency in the performance discourse of firms in Ghana’s MFI sector?” In providing answer to the research question, the study employs data of 49 firms within Ghana’s MFI sector between the period 2009 and 2023.

This research is relevant and timely for the peculiar reason that though the MFI sector is thriving in developing economies, in recent times, the MFI sector in Ghana faced crisis within the period 2017–2019. This crisis resulted in the revoking of the license and collapse of a number of firms in the MFI sub-sector (Affum and Obiri, 2020). Key among the reasons raised by the Bank of Ghana (Banking sector regulator in Ghana) include deficiency in the operations of the MFIs and regulatory violations. These issues per the regulator led to a decline in the financial position of MFIs and consequently resulted in the insolvency of some of the MFIs. Essentially, the collapse of these firms raised doubts in the minds of different stakeholders regarding how well these firms were performing. Thus, by investigating how ICE affects MFI performance in Ghana’s MFI sector, practitioners are informed on the relevance that should be attached to the issue of ICE as far as the discourse pertaining to performance in MFIs is concerned. More so, through this study, the expectation is that the case for investing in intellectual capital for purposes of ensuring ICE will be reinforced, particularly with respect to knowledge-based sectors such as the financial sector in developing economies, to which MFIs belong. The main assumption underpinning the study is that if MFIs are able to invest in and properly manage their intellectual capital, the efficiency of these intellectual capitals can be achieved as things are better done which leads to competitive advantage and translates into more desirable firm outcomes, particularly financial performance.

The rest of the paper is structured into literature review, and hypothesis development, methods and data, results discussion and conclusion and implications.

Intellectual capital represents a key knowledge-based and intangible asset that when fully developed and well managed can deliver great value for firms, and translate into firm success. Theoretically, the conversation has been that the human capital develops systems and procedures for organizations which propel the firm to desirable outcomes as competitive edge is ensured leading to superior performance. Empirically, the consequence of ICE has been confirmed in different contexts. To proxy ICE for purposes of empirical research, the ICE framework/model credited to Pulic (2000) known as the value-added intellectual coefficient (VAIC) model has been widely acknowledged and accepted. The model provides that the ICE of a firm is the sum of human capital efficiency, structural capital efficiency and capital employed efficiency. In recent times, there have been some additions to the original model of Pulic, where the additions are relational capital efficiency and innovation capital efficiency (also known as research and development capital efficiency, and the resultant being the modified value added intellectual coefficient (Luh, 2025; Xu and Liu, 2020). That is, the indication is that efficiency of these various elements, namely human capital, structural capital, capital employed (financial capital), relational capital and research and development capital, contribute to total ICE. In other words, the computation of these various elements considers their contribution to value added in the firm (Chowdhury et al., 2019).

The resource-based view (RBV) theory provides that a key source of a firm’s competitive edge hence superior performance is its resources (Rehman et al., 2022; Barney, 1991). The overall position of the theory is that a firm requires both tangible and intangible resources to survive and for these resources to produce the desired outcome, they have to be unique and valuable (Barney, 1991). One key intangible resource of a firm is its intellectual capital. The ability of a firm to make its intellectual capital more efficient through appropriate investment by means of training, education and skills acquisition has a substantial role to play in its ultimate success. In line with the RBV theory, the study argues that the ability of MFIs to make appropriate investment in intellectual capital to ensure their uniqueness and efficiency will enhance the competitive edge of a MFI leading to desirable outcomes such as improved financial performance.

Clearly, in spite of the argument that intellectual capital has positive implications for firm outcomes, empirical studies have produced divergent outcomes but with majority of studies confirming the performance enhancing effect of ICE, albeit skewed to contexts other than Africa and by extension Ghana. More so, few studies relate to the microfinance sector and majority of these pertain to contexts which are largely different from Ghana in terms of structure and other governance and institutional issues. In this regard, within the scope of MFIs, Kamukama and Sulait (2017) studied the contribution of intellectual capital elements to competitive edge in MFIs in Uganda and confirmed that three elements of intellectual capital, namely structural capital, human capital and relational capital, are strong predictors of competitive advantage in the MFI industry in Uganda. Still within the context of Uganda, Kamukama et al. (2010) examined whether competitive advantage mediates the relationship between intellectual capital and MFI performance and found that competitive advantage is a significant mediator in the relationship between intellectual capital and performance and that competitive advantage boosts the relationship by 22.4%. Additionally, Kamukama (2013) focused on intellectual capital and service quality in the MFI sector of Uganda and provides evidence that apart from relational capital, other elements of intellectual capital, namely structural capital and human capital, are strong predictors of service quality.

With specific reference to the context of Malaysia, Hashim et al. (2018) used survey data collected from 153 managers of MFIs across 22 countries and reported that intellectual capital collectively positively affects the performance of MFIs but no significant effect was found of human capital and structural capital on performance. Ahamad et al. (2023) also employed data of 661 MFIs across 86 countries for the period 2010–2018 and reported that three elements of intellectual capital have a significant effect on MFIs financial efficiency, and external governance was found to significantly moderate the relationship between the value of capital employed and financial efficiency only. Furthermore, Githaiga et al. (2023) using a global sample empirically confirmed that while human capital efficiency and capital employed efficiency enhance financial sustainability of MFIs, structural capital efficiency lowers financial sustainability of MFIs.

Focusing on Indonesia, Mahaputra et al. (2021) used cross-sectional data and confirmed that intellectual capital improves the performance of MFIs. With specific reference to India, Barpanda and Bontis (2021) employed cross-sectional data of 252 MFIs and confirmed that intellectual capital and its facets are positively linked with financial performance. Finally, using cross-sectional data collected from 66 MFIs in Uganda, Kabuye et al. (2021) provided empirical evidence that intellectual capital and isomorphic forces positively and significantly contribute to the strength of internal controls over financial reporting (ICFR) in MFIs.

In relation to other firms other than MFIs, Isola et al. (2020) confirmed that bank performance responds positively to ICE of commercial banks in Nigeria. Onumah and Duho (2020) also studied ICE (VAIC) and bank efficiency in Ghana and confirmed that ICE instigates bank efficiency. It is also the case that firm efficiency can be enhanced by ICE as empirically evidenced in Gupta and Raman (2021). Lastly, using data of firms from Saudi Arabia and Bahrain, Hamdan (2018) confirmed that ICE positively impacts accounting-based performance but not in the case of market-based performance. Some other recent works (Xu et al., 2023; Naushad and Faisal, 2023; Asutay and Ubaidillah, 2024; Habib and Dalwai, 2024; Majumder et al., 2023; Githaiga, 2023; Faruq et al., 2023) revealed that IC has important implications for performance in sectors other than microfinance. For instance, Xu et al. (2023) highlights that IC has a positive impact on financial performance across different life cycle stages of firms. Naushad and Faisal (2023) also report that IC impacts profitability and productivity of SMEs positively, while Asutay and Ubaidillah (2024) found that IC positively impacts profitability in Islamic banks. Habib and Dalwai (2024) confirm the position of the preceding studies by establishing a positive causal relationship between IC and firm performance of firms in the GCC setting, while Majumder et al. (2023) and Faruq et al. (2023) focusing on banks in Bangladesh established empirically that IC has positive effect on bank performance. Focusing on East African banks, Githaiga (2023) revealed that IC has a positive significant effect on bank performance.

Synthesizing the literature, both theoretical and empirics, there is no doubt that investment in intellectual capital and, by extension, ICE has implications for firm value and hence performance. Despite the body of work that exist on the subject matter, limited if not nonexistent studies have provided empirical knowledge on how ICE affects performance of firms christened as microfinance entities, particularly with respect to developing economies like Ghana where such firms form the majority of firms in its financial sector, though they control a smaller share of total assets in the sector. Thus, given the empirical evidence and theoretical arguments, the study argues that intellectual capital is an important resource which if properly managed can be the game changer for microfinance firms as it enhances competitive edge, enhance productivity and leads to superior performance outcomes (e.g. financial performance). Based on the narrative above and drawing on the RBV theory, the study proposes the following hypothesis;

Secondary data for the study were sourced from audited annual reports of 49 MFIs in Ghana over the period 2009–2023. The inclusion of a firm’s data for analysis was based on data availability. The study employed a correlational and non-experimental quantitative design (Gill and Biger, 2013) and used panel estimation techniques for empirical analysis. Panel data methods were chosen due to their advantages over cross-sectional and time-series techniques, such as controlling for omitted variables and addressing endogeneity issues (Wooldridge, 2009; Hsiao, 2007). These techniques enhance the accuracy, validity and reliability of econometric estimates (Hsiao, 2007).

The dynamic generalized method of moments (GMM) was the specific panel estimator utilized. GMM is particularly suitable when the dependent variable exhibits high persistence and the number of entities exceeds the time periods. Additionally, GMM minimizes challenges like reverse causality by generating internal instruments, eliminating the need for independently identified instruments that meet econometric, theoretical and intuitive criteria (Arellano and Bond, 1991; Arellano and Bover, 1995). This approach effectively addresses endogeneity issues.

The empirical model used to test the study’s hypothesis was formulated based on prior studies (Rehman et al., 2022; Chowdhury et al., 2019; Meles et al., 2016) and is presented in alignment with established research methodologies.

In Model 1, Pfmance represents performance and the study’s explained variable, while $Pfmancei,t−1$ represents the lag of performance. It is proxied using return on assets (ROA), but return on equity (ROE) and net interest margin (NIM) were considered for result robustness testing. ICE stands for intellectual capital efficiency and proxied by the MVAIC. The MVAIC comprises four components (see Equation (2) below for description of the four components). Furthermore, the control variables considered include firm size (BusSize), financial leverage (leverage), board size (SoD), board gender diversity (RoWoB), operational self-sufficiency (OSS), liquidity (LIQU) and gross domestic product growth rate (GDPgrwth).

To proxy performance, three indicators were used. These include ROA, ROE and NIM. While ROA considers how efficient a firm has been in generating profit using its total assets, ROE considers how efficient a firm has been in utilizing shareholders equity. Finally, net interest margin measures how well the MFI has been in the generation of interest income from their financial intermediation activities. These performance indicators are used following prior research (Luh et al., 2022).

To proxy ICE, the modified value-added intellectual coefficient framework was adopted (Ulum et al., 2017; Mohammad and Bujang, 2019). The disaggregated MVAIC model is presented as follows:

In Equation (2), CEE represents capital employed efficiency; SCE stands for structural capital efficiency. Finally, RCE stands for relational capital efficiency, and HCE stands for human capital efficiency. The measurements for the individual components of the MVAIC are given as follows:

In the Equations (3)–(6) above, VA stands for value added and is the summation of personnel costs, operating profit, amortization and depreciation and marketing and advertising costs (i.e. VA=Operating profit + Personnel costs + Amortization + Depreciation + Marketing/Advertisement cost), CE stands for capital employed, RC is relational capital (i.e. marketing and advertisement expenses), and PC represents personal costs and wages of employees.

In addition to the primary independent variable, control variables identified in the literature as influencing performance were included to address omitted-variable bias and enhance the explanatory power of the independent variable of interest. Prior research highlights that factors such as firm size, credit risk exposure, indebtedness and a country’s economic wellbeing significantly impact performance. Furthermore, governance indicators like board size, board gender diversity and firm-specific factors such as liquidity and operational self-sufficiency (OSS) also play a role. Following prior studies (Meles et al., 2016; Weqar et al., 2021; Rehman et al., 2022; Luh and Kusi, 2023), the study incorporates board size, board gender diversity, OSS, liquidity, firm size, financial leverage and GDP growth rate as control variables. Table 1 summarizes these variables and their measurement methods.

The results of the study include the descriptive statistics, the correlation matrix and the regression results. Table 2 shows the descriptive statistics, while Table 3 shows the correlation matrix. Tables 4–6 shows the regression results which helps to test the research hypothesis. The descriptive statistics in Table 2 reveals that on average, the financial performance indicators of the MFIs considered for the period under consideration was positive as revealed in ROE of 5.3%, 1.7% ROA and 14.9% NIM. In relation to the main independent variables, capital employed efficiency reported an average of 1.03, human capital efficiency reported 1.552, structural capital efficiency reported average of −0.487, relational capital efficiency had average of 0.04 and value-added intellectual coefficient had average of 1.486. Higher positive MVAIC depicts higher levels of ICE.

Table 3, on the other hand, reveals the relationship between the study’s variables. In addition to showing the relationship, the matrix is able to reveal the presence of multicollinearity between predictor variables. According to Kennedy (2008), multicollinearity is said to be present between two predictor variables when their coefficient is above 0.70. Given that the coefficients between all the explanatory variables are below 0.70, the study concludes that there is no presence of multicollinearity among the independent variables used. As part of other diagnostic tests, the variance inflation factor (VIF) test and the Shapiro tests (SWILK) were carried out. The VIF tests for the presence of multicollinearity. VIFs below 10 are indications of no presence of multicollinearity between the explanatory variables (see Table 2 for VIF and result of Shapiro test). Thus, since the VIFs for the variables were less than 10, the indication is that multicollinearity is not a problem in terms of the regression results. On the other hand, the Shapiro test is for ascertaining whether the data are normally distributed or not. Given that the p-values of the variables were less than 0.05 in the test, the conclusion is that the data are normally distributed.

The tests to ascertain the appropriateness of the GMM model include the Arellano-Bond test for AR (2) in first differences and the Hansen test. The AR (2) has the null hypothesis that there is no second order autocorrelation. Given that the p-value is greater than 0.05, the study concludes that there is no second order autocorrelation, hence the model is fit. On the other hand, the Hansen test tests for the validity of the instruments used in the model. The null hypothesis of the test is that the instruments are valid (i.e. instruments are uncorrelated with the error term and correctly excluded from the estimated equation), and p-values greater than 0.05 for this test means instruments are valid and the model is correctly specified (Ramírez et al., 2021). Since the p-values obtained for each model are greater than 0.05, the study concludes that instruments are valid, and models well specified (see Tables 4–6 for AR (2), number of instruments and Hansen test).

The empirical output reveals that performance in MFIs responds positively to ICE. That is, a positive and significant causal relationship was established between ICE (represented by the MVAIC) and performance (ROE, NIM and ROA) (see column 5 in Tables 4–6). Interestingly, at the individual component level, CEE was found to have a positive significant effect on ROE and NIM but a negative significant effect on ROA (see column 2 in Tables 4–6). In addition, SCE has a significant positive effect on ROA, ROE and NIM (see column 3 in Tables 4–6). Furthermore, the result shows that HCE positively affects performance (ROA, ROE and NIM) (see column Tables 4–6). Lastly, RCE has a negative effect on ROA and ROE but a positive significant effect on NIM (see column 1 in Tables 4–6). In overall terms, the results are indicative of the fact that as ICE improves in MFIs, there is a corresponding improvement in MFIs’ performance.

There is no shortage of arguments that provide that intellectual capital, otherwise known as an intangible resource, plays a key role in firm value creation, hence success (Murale et al., 2010; Salvi et al., 2020). The results are supportive of the argument that intellectual capital is a critical resource that is key in producing desirable outcomes and business growth (Huang and Liu, 2005). More so, the result supports the argument that the ability of firms (including firms based in knowledge-based economies such as MFIs) to survive depends largely on their ability to leverage intellectual capital for purposes of innovation, competitive edge and sustainability (Alvino et al., 2020). Overall, the study confirms that investment in issues pertaining to intellectual capital and the subsequent efficient management of same will ensure that processes are carried out more efficiently, while quality organizational systems and procedures are instituted helping to ensure efficient MFIs, and bolster competitive edge which culminates in better or superior financial performance and other successes (Kamukama et al., 2010) as supported the empirical result of the study. Essentially, it is valid to argue that appropriate investment in human capital, structural and supportive organizational systems as well as efficient allocation of financial resources by MFIs has the potential to ensure better performance outcomes for MFIs as demonstrated by the empirical output. Supporting the empirical outcome of this study is that of Meles et al. (2016) who confirmed that ICE improves performance of commercial banks operating in the US. Additionally, consistent with the findings is that empirical result by Sardo and Serrasqueiro (2018) who found that ICE improves the financial performance of high, medium and low-tech firms in Europe.

On the issue of the impact of the individual components on performance, the negative impact of CEE on ROA and RCE on performance (i.e. ROA and ROE) are worthy of discussion. Specifically, the inverse relationship between CEE and ROA can be attributed to firms with high CEE investing heavily in tangible and intangible assets, such as property, plant, equipment, advanced technology, and research and development. While these investments enhance operational capacity and revenue, the profits generated may not increase proportionally due to factors like depreciation, maintenance costs, high operational expenses and inefficiencies. These issues collectively may lead to a lower ROA.

For the inverse relationship between relational capital efficiency and performance (i.e. ROE and ROA), a key potential reason is the high maintenance cost of building and sustaining strong relationships. Relational capital involves fostering trust, loyalty and collaboration with stakeholders like customers and suppliers, requiring significant financial and operational investment. These expenditures often reduce net income in the short term since the immediate benefits may not match the investments made. Consequently, an increase in relational capital could lower short-term returns such as ROE and ROA. However, over time, these investments may yield significant advantages, including customer loyalty and reputational gains, driving a competitive edge and superior financial performance. This result is congruent with Rehman et al. (2022) who found that relational capital efficiency is inversely linked with ROE.

In sum, while ICE (MVAIC) in overall terms positively drives performance, some of the individual components demonstrate counter effect on performance, particularly CEE and RCE. This result is supported by Rehman et al. (2022) who highlight that although ICE produces desirable performance in overall terms for banks, its individual components could have varying effect on firm outcome. Also supporting the findings is that of Onumah and Duho (2019) who highlight that although ICE in overall terms has a positive effect on bank performance, the individual components were observed to have varying impact on performance. These varying effects essentially require that management of firms pay critical attention to the issue of ICE wholistically and prioritize each component to derive the best of them.

The study focused on the role of ICE in the performance discourse of firms in the MFI sector of a developing economy, Ghana. Data of 49 firms operating in Ghana’s microfinance sector for the period 2009–2023 were used to achieve the study objective. ICE was proxied following the model used in Rehman et al. (2022). Empirical results were generated using GMM.

In overall terms, the results support the argument that intangible assets (i.e. intellectual capital) is a necessary element for value creation in firms. That is, the study provided empirical evidence that ICE improves performance of firms in Ghana’s MFI sector. The research question has been duly answered as it is confirmed that ICE plays an enhancing role as far as performance in MFIs in developing economies is concerned.

The findings have implications. The study affirms the cruciality of enhancing ICE for MFIs as it improves their performance (ROA, ROE and NIM). On the basis of the findings, the recommendations for MFIs include investment in human resource training and development, promoting knowledge-sharing and fostering innovative workplace culture which can aid in boosting overall performance.

In relation to the individual components, the result in relation to CEE offers the implication that efficient use of financial resources is crucial for improving profitability for equity holders as well as improves gains/margins from interest-related activities. In this regard, MFIs should prioritize optimizing financial resources, adopting better investment strategies and ensuring efficient capital allocation. Overall, while MFIs can leverage on their capital effectively to benefit shareholders and generate positive margins, it will be in their best interest to reassess their asset management strategies for the purpose of enhancing operational efficiency to ensure sustainable long-term performance.

In terms of SCE, the implication of the findings is that strong organizational processes, systems and infrastructure can enhance returns to assets and equity holders. Given the result, it is proposed that MFIs should invest in improving organizational structures, internal processes, and robust information and management systems to boost structural capital efficiency.

Additionally, in relation to HCE, the implication of the finding is that skilled and knowledgeable employees are essential for driving performance. Based on the result, the recommendation is that MFIs should invest in continuous training and skill development, creating a supportive work environment to maximize workforce potential.

Furthermore, pertaining to RCE, the implication is that relationships with customers, partners and stakeholders are vital for generating interest income, and this is not surprising particularly given the nature of business of microfinance companies. On the basis of the result, it is recommended that MFIs should build and maintain strong relationships with clients but avoid over-reliance on relational capital for enhancing returns on assets and equity.

Based on the findings and implications, the overall recommendation is that MFIs should invest in and develop integrated intellectual capital management strategies and regularly evaluate and align these strategies with organizational goals and targets to ensure sustained performance. The key policy recommendation is that regulators and policy makers should encourage MFIs to invest in appropriate intellectual capital by offering supportive policies and incentives to them.

Socially, the findings have implication. Socially, the implication is that if MFIs can enhance their performance by enhancing ICE, they will be better placed in serving their constituents. Thus, MFIs must work towards enhancing their ICE as that aids in performance improvement which improves the chances of better serving serve low-income constituents and the poor, particularly in rural and peri-urban areas. This will ultimately help to contribute to socio-economic growth and development, especially in developing economies like Ghana.

Overall, the study’s findings align with the RBV theory, which argues that organizations can achieve a competitive advantage through the acquisition and efficient management of valuable, rare, inimitable and non-substitutable resources. Thus, efficient intellectual capital management can help MFIs achieve better performance and competitive edge. What it simply means is that microfinance firms cannot relegate to the background issues of ICE since it has a role to play in their overall financial performance.

The study is based on a limited number of Ghanaian microfinance firms as majority of the firms do not have their annual reports public. The study thus advises caution in generalizing the findings. Future research should use cross-country data from Africa to enhance decision-making regarding intellectual capital in microfinance. Additionally, further studies should explore various factors and mechanisms affecting the performance of MFIs, offering empirical insights into the relationship between ICE and MFI performance. Notable issues worthy of examination include issues of environmental, social and governance (i.e. sustainability) practices within the corporate strategies of MFIs and the financial models and systems adopted by MFIs as far as credit extension is concerned. This broader perspective will better inform sectoral strategies and policies.

The author reports no conflict of interest, and no funding was received in developing the paper.