Human Capital, Income Diversification, and Bank Performance. An Empirical Study of East African Banks
ABSTRACT
Purpose- This paper aims to examine whether income diversification moderates the relationship between human capital and bank performance.
Design/methodology/approach: The study uses a sample of 53 banks and panel data for the years 2010-2018. The hypotheses are tested through hierarchical multiple regression and the choice between fixed effect and random effect estimation is based on the results of the Hausman test.
Findings- The study finds that human capital and income diversification significantly influence bank performance, however, the direction of the causality varies. While human capital has a positive effect, income diversification has a negative effect. Additionally, the interaction term has a negative and significant effect on bank performance, inferring that income diversification has an antagonistic effect on the human capital and bank performance relationship. For the control variable; liquidity and asset quality negatively affects bank performance while capitalization has a positive effect.
Practical implications- The results of this paper provide valuable insights. Bank managers can get a better understanding on the impact of human capital on bank performance, and the need to invest more in human capital development. Further, the study cautions managers that engaging in non-lending activities might destroy the economic value of human capital and ultimately lower performance. The study also recommends that policymaker should address the obstacles to banks income diversification, for instance relaxing regulations restricting diversification; this might enable banks to leverage on related financial service activities for optimal utilization of human capital and improve banks profitability.
Originality/value – While a good number of previous studies investigated the direct effect of human capital and income diversification on performance of banks, this study examines the moderating role of income diversification on the relationship between human capital and performance of banks in East Africa.
Keywords East Africa, human capital, income diversification, banks, performance
Paper type Research paper
Introduction
Firms depend on both tangible and intangible assets for sustained competitive advantage and long-run survival. However, in the present era of information and knowledge based economies scholar argue that intangible assets, particularly intellectual capital (IC), are gradually replacing physical capital both as critical factors of production and as drivers of sustained long-term profitability (Drucker, 1993; Clarke & Gholamshahi, 2018). This is true for knowledge-intensive service organizations, particularly banks since they are highly innovative and due prudential requirements maintain minimal physical capital. While, all the dimensions of IC (human capital, structural capital and customer capital) are valuable to an organization, human capital is considered the most important to service industries as it not only impacts the quality of services in the short-term, but also influences a wide range of organizational outcomes in the long-term (Aryee, Walumbwa, Seidu, & Otaye, 2016; Seleim & Bontis, 2013; Mahoney & Kor, 2015). Furthermore, some researchers claim that human capital is the primary component of intellectual capital because it supports the development and application of the other IC components (Kim, Kim, Park, Lee, & Jee, 2012; Wang & Chang,2005; Feraru & Mironescu, 2012). Therefore, human capital is at the heart of successful firms owing to its direct and an indirect effect on firm performance. This assertion is supported by the resource-based view (RBV), which attributes superior performance and competitive advantage to firm’s specific stock of resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991; Prahalad & Hamel, 1990). Accordingly, the difference in performance among firms, within and across industries, can be explained by differences in human capital management strategies; such as recruitment, deployment and training. From a similar standpoint, studies have also linked human capital with the attainment of national goals and wealth of nations, which further explains why nations allocate huge resources on education and healthcare for socio-economic development (Gennaioli, La Porta, Lopez-de-Silanes, & Shleifer, 2012; Stewart, 1997).
The human capital and performance relationship has been examined extensively over time, however the existing literature shows mixed findings. While, one strand of studies suggests a direct relationship that is either positive or negative (Firer, & Williams, 2003; Chowdhury, Rana, Azim, 2019), other claims an indirect causality (Bontis, Keow, & Richardson, 2000; Scafarto, Ricci, & Scafarto, 2016; Wang, & Chang, 2005), yet a good number of studies asserts there is no relationship (Razafindrambinina, & Anggreni, 2017; Soewarno, & Tjahjadi, 2020). These findings suggest the existence of contingent factors that moderate the human capital and performance relationship. Whereas the resource-based view theory attributes competitive advantage to firms’ stock of human capital, some scholars assert that firms build competitive advantage and economic rent by leveraging their firm-specific resources, such as human capital, through diversification (Merino, Grandval, Upson, & Vergnaud, 2014; Chang & Wang, 2007; Neffke & Henning, 2013). Penrose (1959) also mentioned that firms diversify across business units and industries to exploit alternative uses that are latent in idle resources and core competencies. In the same line of research, Tidd and Taurins (1999) concluded that diversification not only leverages existing competencies but also develops new ones.
Thus, based on the empirical literature and human capital theories there seem to be an important interconnection between human capital, diversification, and performance that is not clear in the literature. Accordingly, this study contributes to the literature by assessing whether income diversification moderates the relationship between human capital and bank performance among East African banks. The study is motivated by the declining interest-based income which has been caused by financial liberalization deregulation; as a countermeasure banks are gradually engaging in non-traditional banking services compensate for the deteriorating interest revenue. This study will enable researchers, practitioners, and policy-makers to have a more definite and direct understanding of the implication of income diversification on the human capital and bank performance relationship. Besides, the findings will offer more explanation on how banks can leverage their human capital through nontraditional activities for competitive advantage and improved profitability.
To execute the research objective, this paper is divided into five sections. In the introduction, the research problem is stated. In the literature review section, the definition and the empirical link between the research constructs; human capital, income diversification, and bank performance are discussed and the hypotheses developed. The methodology section articulates the ways how the aim of this paper is achieved; that is, the data, the sample, measurement of variables, and the research model. In the findings section, the paper shows the empirical relationships between the variables, and tests the hypotheses. Finally, the discussion and conclusion sections summarize the findings, the study’s limitations, puts forward recommendations for practitioners and policy-makers, and suggest possible lines of future research.
Literature Review
Human capital represents the most important asset to service organizations such as banks and professional firms since the main output is created and delivered by human resources, and the output varies from one employee to another (Namasivayam & Denizci, 2006). Bontis et al., (2000) suggest that human capital represents the individual knowledge stock of an organization as represented by its employees. While, Maditinos, Chatzoudes, Tsairidis, and Theriou (2011) view human capital as the “brainpower of the employee inside the company.” In a broader sense, all these definitions suggest that human capital represents learning related concepts such as education, training, work experience, competences, and skills in addition to psychological and social ideas for instance ability, attitudes, and motivation.
Extant literature shows that human capital plays a critical role in firm innovativeness and customer satisfaction (Bornay-Barrachina, López-Cabrales, & Valle-Cabrera, 2017; Aryee et al., 2016). While some studies have also linked an organizations investment in human capital to improved firm value and profitability (Hejazi, Ghanbari, & Alipour, 2016; Özer, & Çam, 2016; Li, Qian, Gong & Tao, 2014). Therefore, a firm’s expenditures on employees should be viewed as an investment rather than costs since expenses on employees’ education and training improves human capital rather than physical or financial capital (Bontis et al., 2000; Scafarto et al., 2016); which eventually leads to greater employee efficiency and subsequently improved organizational performance. Furthermore, firms with quality human capital are likely to have a competitive edge in the marketplace since a knowledgeable workforce is likely to develop new and innovative products (Delery, & Roumpi, 2017). Considering the importance of human capital, organizations invest massive resources on employee programs such as recruitment, training, and development aimed at equipping its human resources with firm-specific skills and capabilities for competitive advantage and superior performance. Macro-level studies further show that human capital account for over three-quarters of the developed nation’s wealth thus depicting it as a necessary ingredient for economic development and sustainable development (Becker, 2009; Pelinescu 2015). In the same line of argument, a number of studies have also shown that human capital fosters innovations and diffusion of technologies hence stimulating total factor productivity and economic growth (Akhvlediani, & Cieślik, 2019; Männasoo, Hein, & Ruubel, 2018).
Contrary to the assertions of the resource based view theory, that attributes firm’s competitive advantage and performance emanates to its stock of human capital, the empirical literature shows mixed findings (Firer, & Williams, 2003; Wang, & Chang, 2005; Razafindrambinina, & Anggreni, 2017; Soewarno, & Tjahjadi, 2020) and present studies are now exploring factors that either mediate or moderate the relationship. Similarly, scholars on organizational resource management claim that firms can deploy excess firm-specific resources such as human capital through diversification since they are non-tradeable (Penrose, 1957; Chiambaretto, & Wassmer, 2019; Fisch & Schmeisser, 2020; Chung, Kim, & Kang, 2019). Similarly, some authors have observed that by diversifying firms develop new core competencies and capabilities that assist in improving the current business (Lin, Hsu, Hsu, & Chung, 2020). Studies have also revealed that firms prefer to diversify into business and industries that require human capital similar to that of their current business, thereby enabling them to leverage excess managerial capital (Fitjar, & Timmermans, 2017; Lee, Mauer, & Xu, 2018; Neffke & Henning, 2013; Lu, Liu, Filatotchev & Wright, 2014). A study by Chen, Gao, and Ma (2018) shows that firms with greater human capital and whose employees have better ex-ante employment mobility have a higher likelihood of being acquired. While Lee, Mauer, and Xu (2018) observed that human capital complementarities motivated mergers and acquisitions and led to improved post-merger financial performance. Similarly, Mengistu (2009), found that human capital influenced vertical and horizontal export diversification. By and large, these studies emphasize that knowledge-based resources can be leveraged through diversification for economic rent. Moreover, a numbers of studies suggest that superior performance stems from matching firm’s resources to market opportunities (Carnes, Chirico, Hitt, & Pisano, 2017; Feng, Morgan, & Rego, 2017).
Scholars have recommended different typologies of diversification (Ansoff, 1965; Ye, Lu, Flanagan & Ye, 2018), however, in the context of the banking industry income (revenue) diversification is the most important due to the stringent regulations and supervisory oversight that limits the freedom of banks to engage in diversification. Income diversification means increasing the number of revenue streams by engaging in non-core (secondary) activities. With regard to banking sector, income diversification means engaging in nontraditional banking activities, such as securities trading, property management, venture capital and underwriting, that generate noninterest income. Borrowing from Markowitz’s (1952) modern portfolio theory, income diversification is a valuable strategy of managing credit risks and stabilizing income. Similarly, studies have also shown that income diversification is associated with improved bank performance (Duho, Onumah, & Owodo, 2019; Chiorazzo, Milani & Salvini, 2008), increased bank efficiency (Nguyen, 2018), greater bank market power (Lin, Shi, & Zheng, 2020) and lower interest margins (Trinugroho, Risfandy, & Ariefianto, 2018). Moreover, banks are likely to benefit from synergies and economies of scale arising from shared production in the delivery of related financial services.
Based on theoretical propositions of resource based view and modern portfolio is merges that in an era of knowledge-based economy and financial liberalization, a bank’s competitive advantage and performance is largely influenced by its stock of human capital and leveraging on income diversification. However, there is far from enough empirical studies investigating the link. Most of the previous studies focused on the direct effect of human capital on bank performance ignoring that income diversification may moderate the relationship. Income diversification is likely to moderate the human capital and bank performance relationship in several ways. First, by engaging in nontraditional activities banks are likely to invest more resources in equipping their workforce with necessary skills to handle the new business thus improving the value of human capital and ultimately performance. Again, engaging in related financial services presents managers with challenging and exciting opportunities for learning how to handle complex decisions. Second, income diversification generates additional financial resources which can be re-invested in improving the current stock of human capital and innovation, and ultimately improve bank performance. Third, with the noticeable decline in lending activities, diversifying into non-lending activities offers an opportunity for banks to deploy their excess human capital, implying that income diversifications allows for optimal utilization of human capital. Finally, banks also enjoy the synergies and economies that arise from related diversification such as cross selling and cross subsidization. Thus, the study hypothesized as follows
H1: Human capital has a positive and significant effect on bank performance.
H2: Income diversification has a positive and significant effect on bank performance.
H3: Income diversification significantly moderate the relationship between human capital and bank performance.
Methodology
Research design, sample and data
The study was anchored on positivism paradigm and adopted explanatory and descriptive research designs and was longitudinal in nature. The study employed panel data for the period between 2010 and 2018 that was extracted from the annual reports of registered banking firms within East Africa region. The population consisted of commercial banks drawn from six countries which comprise of Uganda (24), Tanzania (41), Rwanda (11), Kenya (42), South Sudan (30) and Burundi (10). The inclusion and exclusion criteria was based on; accessibility of data and the banks had to have being in operation in the entire period, the banking firm should not have undergone substantial corporate restructuring to ensure consistency of data. Thus, the final sample consisted of 53 commercial banks which yielded a total of 477 firm-year observations.
Measurement of variables
The study had a total of six variables: one dependent variables (bank performance), the independent variable (human capital), a moderator (income diversification) and control three variables (bank capitalization, asset quality, and liquidity). A brief description of the variables and their measurement is as follow.
Bank performance
Bank performance was measured by the return on assets (ROA) as used in previous studies (Duho et al., 2019; Berger, Hasan & Zhou, 2010). A higher return on assets shows better utilization of banks asset to generate profits, while lower ROA indicate inefficient use of assets.
Human capital
The value-added intellectual capital (VAICTM), devised by Pulic (2000), formed the basis of measuring human capital. The main coefficient of VAICTM is Value Added (VA). VAICTM is a composite sum of three indicators; Human Capital Efficiency (HCE), that measures the efficiency of human capital resources employed; Structural Capital Efficiency (SCE), an indicator of the efficiency of structural capital (innovation capital, process capital and customer capital); and the Capital Employed Efficiency (CEE), which shows the value created for every monetary unit invested in financial or physical capital.
The VAICTM model is formalized as:
Where; VAICTM = VA is the intellectual capital coefficient; HCE = human capital efficiency; SCE = structural capital efficiency, and CEE = capital employed efficiency. The Value-added (VA) is obtained by subtracting operating expenses from total revenue. HCE is calculated by dividing the VA by the total employee costs or payroll expenditure (staff salaries, pension, insurance, and related expenses). SCE is calculated by dividing the total expenses on structural capital by the firm’s VA. CEE is obtained by dividing its VA by the book value of the net assets. A high coefficient indicates higher value creation using the firm’s resources including IC.
Income diversification is used as the moderating variable. Banks’ income comprises of interest income (generated from lending activities) and noninterest income (earned from non-lending activities). These two revenue streams are used to construct the Herfindahl-Hirschman Index (HHI) of income specialization (Nepali, 2018; Chiorazzo, Milani & Salvini, 2008). HHI is computed as shown below.
Where; NON is the non-interest income, NET is the net interest income and NETOP denotes net operating revenue, which equals to non-interest income (NONE) plus net-interest income (NET). As the HHI rises, the bank becomes more concentrated and less diversified, HHI varies between 0 and 1.0 (Stiroh & Rumble, 2006; Mercieca, Schaeck, & Wolfe, 2007). Therefore, the study measures income diversification as:
Control variables
Based on the empirical literature, the study incorporated several control variables into the estimation model; bank capitalization, asset quality, and liquidity.
Bank capitalization. An increase in bank capital reduces the expected costs of financial distress and bankruptcy results in a lower cost of capital (Frigerio, & Vandone, 2018). Besides, lenders consider adequately capitalized banks as capable of dealing with adverse macro-economic factors. Similarly, to pay dividends to shareholders, bank managers are constrained to operate efficiently by minimizing costs so as to increase profits. Thus, the study hypothesis that well capitalized banks have a greater capacity and are likely to report higher performance. This study measures bank capitalization as the ratio of equity to total assets (Ekinci & Poyraz, 2019).
Asset quality. Berger and DeYoung (1997) contend that asset quality is a significant predictor of bank insolvency. Thus, bank asset quality reflects bank monitoring since poor asset quality is likely to have a negative impact on a bank’s risk-adjusted-performance. Furthermore, when the asset quality of a bank is lower, it may pursue more diversified revenue streams to compensate for the losses of the deteriorated loan quality, and hence reduce income volatility (Ahamed, 2017). Asset quality is measured as the ratio of non-performing loans (NPLs) to gross loans and asdvances (Lin & Zhang, 2008; Ahamed, 2017).
Banks liquidity. A good number of studies posit a positive relationship between a banks’ liquidity and their financial performance (Athanasoglou et al., 2008). Nonetheless, there are counterarguments: excess liquidity is accompanied by high storage costs (Staikouras, Mamatzakis & Koutsomanoli-Filippaki, 2008) and lower returns (Kosmidou, Pasiouras & Tsaklanganos, 2007). Although liquid assets might decrease liquidity risk they may well carry high costs that negatively affect bank performance. Arguably, banks with higher liquidity perform better than banks with lower levels of liquid assets, while banks with lower liquidity would underperform banks with more liquid assets while trying to raise their liquidity levels, a phenomenon referred to as ‘bad luck hypothesis’ (Berger & De-Young, 1997). In contrast, liquid assets are generally associated with lower rates of return and consequently higher liquidity would lead to lower financial performance. Liquidity is measured as the ratio of total loans and advances to total assets, which is an indicator of the percentage of bank assets that are tied up in loans and other advances (Kosmidou et al., 2007; Bunda & Desquilbet, 2008).
Research Model
This study used hierarchical regression analysis to test the three research hypotheses. Considering that the main objective of the current study was to analyze the moderating effect of income diversification on the human capital and bank performance relationship, the following set of multiple regression equations were developed:
Model 1: Testing the effect of the control variables on the dependent variable.
BP = β0+ β1CAP + β2AQ+ β3LQ + ε
Model 2: Testing the effect of the independent variable on the dependent variable.
BP = β0+ β1CAP + β2AQ+ β3LQ + β4HC + ε
Model 3: Testing the effect of the predictor variable and the moderator on the dependent variable.
BP = β0+ β1CAP + β2AQ+ β3LQ + β4HC + β5 IND + ε
Model 4: Testing for the moderating effect.
BP = β0+ β1CAP + β2AQ+ β3LQ + β4HC + β5 IND + β6 HC X IND + ε
Where; BP = Bank Performance, HC= Human Capital, IND = Income Diversification, CAP= Capitalization, AQ= Asset Quality, LQ= Liquidity
Results and discussions
Descriptive statistics
Table 2. It summarizes the descriptive statistics of the dependent variables, related to firms’ FP and MV, and independent variables used in this study.
Table I – Descriptive statistics of the variables
Variable
Obs
Mean
SD
Min
Max
Bank performance
477
0.03
0.02
0.01
0.19
Human capital
477
2.87
0.82
1.73
7.45
Income diversification
477
0.41
0.07
0.21
0.50
Liquidity
477
0.57
0.12
0.04
0.85
Asset quality
477
0.09
0.09
0.00
0.51
Capitalization
477
0.16
0.04
0.06
0.29
Bank performance presented a mean of 0.03 suggesting a low level of profitability of commercial banks in East Africa. The high standard deviation of 0.02 suggests that the banking firms had a high variation. The mean score of human capital was 2.87, suggesting that East African banks create an average of 2.87 monetary units for every 1 monetary unit invested in human resources. Income diversification had a mean score of 0.41 suggesting that banks in East Africa engaged moderately in nontraditional banking activities. the banks’ liquidity level was moderate as shown by the mean score of 0.57, The average asset quality was 0.09 inferring a low level of nonperforming assets. Bank capitalization had a mean score of 0.16 which indicates low levels of debt among banks. Further,
Correlation analysis
The correlation and magnitude of the variables are presented in the correlation matrix (see Table 3).
Pearson correlations test is applied to test the relationship between the variables. The findings in Table II indicate that human capital (r = 0.4158; ρ< 0:01) and capitalization (r = 0.1264; ρ < 0:01) are significantly and positively correlated with bank performance. While liquidity (r = – 0.0907; ρ < 0:01) and asset quality (r= 0.4141; ρ < 0:01) are significantly and negatively correlated with bank performance. However, income diversification is the only explanatory variables which is negative and not significantly associated with bank performance (r =-0.099; ρ >0.05).
Table II – Pearson correlation matrix
Variable
BP
HC
ID
LQ
AQ
CAP
Bank Performance(FP)
1.0000
Human Capital (HC)
0.4158*
1.0000
Income Diversification (ID)
-0.099
-0.0357
1.0000
Liquidity (LQ)
-0.0907*
-0.0282
-0.2121*
1.0000
Asset quality (AQ)
-0.4141*
-0.2393*
-0.0483
0.0849
1.0000
Capitalization (CAP)
0.1264*
0.0893
-0.0100
-0.0585
0.1019*
1.0000
Notes: *significant at 5%
Multiple regression analysis
The study employed hierarchical multiple linear regression analysis to determine the moderating effect of income diversification on the relationship between human capital performance of banking firms in East Africa. The results of the hypotheses test are shown in Table III as follows.
In column 1 the regression results of bank performance and the control variables (Model 1) are reported. Based on the results, capitalization had a positive and significant effect on bank performance and the findings are consistent with those of previous studies (Ekinci & Poyraz, 2019; Belkhaoui, Lakhal, Lakhal, & Hellara, 2014; Frigerio, & Vandone, 2018). This study therefore argues that well capitalized banks are more profitable since they are less reliant on external funding, inferring have the advantage of a lower cost of capital.
As regards assets quality, the study found that asset quality had a negative and significant effect on bank performance. The results supported by previous studies (Alharbi, 2017; Salike, & Ao, 2018). A possible explanation of these findings is the existence of weak credit management practices (borrowers screening, monitoring and collection polices) and macro-economic factors that deteriorates the quality of loan portfolio hence exposing banks to higher credit risks. Accordingly, this study argues that banks with a high non-performing loans to total loans ratio are less profitable and are likely to suffer from financial distress due to the increased level of provisions for nonperforming loan.
Liquidity had a negative and statistically insignificant effect on bank performance. The association suggests that highly liquid banks are less profitable. Although, a high ratio of the loan- to -assets ratio is an indicator of the availability of sufficient assets that can be converted into cash, holding too much idle cash indicates low efficiency in assets utilization since the banks lose the opportunity cost of earning profit through lending, which will ultimately lead to low profitability. Therefore, the results confirm that high liquidity can be as undesirable as a low one and managers must maintain an optimal level of liquidity.
Column 2 presents the results of Model 2 that sought to examine the effect of human capital on bank performance. Based on the findings, human capital had a positive and significant effect on bank performance, therefore, hypothesis H1 is supported. The results are also consistent with previous studies (Li et al., 2014; Kor & Leblebici, 2005; Maditinos et al., 2011) and the resource based view theory. Therefore, an efficient utilization of human capital will positively impact on bank performance which further stresses the importance of knowledge-based resources to a firm’s competitive advantage, particularly service organizations like banks. However, the low beta coefficient (β= 0.009), provides additional empirical evidence that human capital has not yet been optimally used by banks in East Africa to generate profits. Although banks are highly innovative, the findings demonstrate that banks are more dependent on physical capital instead of knowledge-based assets such as human capital. These results emphasize the importance of knowledge resources as a source of competitive advantage and ultimately firm performance. Therefore, an organization’s investment in human resources has a favorable impact on performance,
In contrast to H1, the results of model 3 presented in Column 3 shows that income diversification had a negative and significant effect on bank performance (β= -0.022; ρ<0.05), thus H2 is not supported. The finding is supported by previous studies (Stiroh & Rumble, 2006; Berger et al., 2010).) and contrary to modern portfolio theory. The negative relationship between income diversification and bank performance provide further evidence on the dark-side of diversification which can be attributed to several factors; first, lack of managerial expertise in managing nontraditional banking activities. Second, gain from revenue diversification gains could be eroded easily by the costs associated with nontraditional activities. Third, bank managers might easily fall prey to the newness trap thus neglect the mainstream banking activities. Hence, bank managers should focus on a strategy that focuses on increasing their interest income to avoid potential losses from diversifying into non-lending activities.
Column 3 presents the results of model 4, which was used to test the hypothesis H3; whether income diversification moderated the relationship between human capital and bank performance. The results that the interaction term of human capital and income diversification had a negative and significant effect on bank performance (β= -0.052; ρ<0.05). Further, after the inclusion of the interaction the results revealed that the overall explanatory power of the model (R-squared) reduced from 0.292 in model 3 to 0.240 in model 4. As a result, the hypothesis H3 is rejected. The negative and significant moderating effect of income diversification on the human capital and bank performance causality can be explained by several factors. First, though firms prefer diversifying into related businesses or industries where they can take advantage of skills sharing, skill-sharing can only translate into competitive advantage if the operational skills are compatible and transferable leading to a synergetic benefit across the firm. Thus, the findings of this study suggests that bank managers and employees might lack the requisite specialized skills and experience to carryout non-lending activities which lowers labour productivity. Second, internal information asymmetries increase with the degree of diversification. According to the bounded rationality theory, internal information asymmetry limits workers’ ability to synthesis heterogeneous information from the diverse lines of business, which ultimately lowers their efficiency. Also, increased level of business activities leads to information overload hence disallowing employees from taking advantage of learning opportunities that may arise from income diversification. Finally, bank manager may allocate too much of their managerial capital in integrating non-lending activities with lending activities leading to inefficient allocation of human capital.
Table III – Results of regression models (1), (2), (3) and (4)
Dependent variable:
Bank Performance(ROA)
Model
Model
Model
Model
1
2
3
4
Asset quality
-0.075**
-0.066**
-0.067**
-0.064**
(-6.63)
(-6.98)
(-7.09)
(-6.60)
Capitalization
0.072**
0.055**
0.056**
0.053**
(2.82)
(2.50)
(2.56)
(2.30)
Liquidity
-0.009
-0.009
-0.012
-0.013
(-1.13)
(-1.26)
(-1.65)
(-1.73)
Human capital
0.009**
0.009**
0.030**
(7.11)
(7.17)
(5.02)
Income diversification
-0.022**
0.118**
(-1.98)
(2.78)
Human capital*income diversification
-0.052**
(-3.51)
_cons
0.029
0.003
0.014
-0.041
Hausman
0.0635
0.514
0.537
0.001
R-squared
0.201
0.292
0.289
0.240
No of observations
477
477
477
477
Notes: **significant at 5%; the standard errors are in parentheses
Conclusion
The importance of knowledge- based capital is gradually increasing particularly in today’s era of knowledge and information based economies. This is so particularly to the banking sector which is highly innovative and dependent on human capital for competitive advantage and survival. Similarly, financial liberalization and deregulation has changed the traditional banking business leading to stiff competition and decline in interest income prompting banks to diversify into nontraditional activities to compensate for the lost revenue. It is from this background that this study sought to examine whether income diversification moderated the relationship between human capital and performance of East African Banks. To reach this objective the study used hierarchical multiple regression to test the hypotheses. The econometric models were tested using results either the fixed effect regression and the random effect estimation. The findings revealed that human capital had a positive and significant impact on bank performance which emphasize the importance of knowledge-based capital to service organizations. On the other hand, income diversification had a negative significant effect on bank performance. Therefore, the study concludes that focused banks are more profitable than diversified ones. Finally, the study found that income diversification had a negative and significant moderating effect on the relationship between human capital and bank performance; implying that leveraging on nontraditional banking activities is destructive to human capital and is likely to lower performance of banks in East Africa.
Practical implication
On the practical side, managers must pay closer attention to human capital and its positive impact on firm performance. Similarly, managers must be cognizant of the latent potential for leveraging their human capital through nontraditional activities without compromising the mainstream activities. In particular, to preserve the value of the accumulated human capital investments and enhance financial performance, banks should diversify into related industries or business to avoid destruction of their human capital.
For policy-makers, it is important to pay attention to regulatory impediments and agency problem that might prevent banks from attaining an optimal level of income diversification. Also, the study noted that human capital had a low impact on bank performance which calls for governments and banks to make additional investments in human capital development.
Limitations of the study
Despite the novelty of the finding, there are several limitations which can be addressed in future research. This study measure human capital as human capital efficiency (HCE), as guided by Pulic’s Value Added Intellectual Capital (VIACTM), which does not allow the use of qualitative measures of human capital such as education and training. Thus, future studies can consider qualitative indicators of human capital. Secondly, the study focused on commercial banks in East Africa, therefore future studies focusing on other regions and industries would shed more insights.
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