Do board characteristics moderate capital adequacy regulation and bank risk-taking nexus in Sub-Saharan Africa?

Asiamah, Sampson; Appiah, Kingsely Opoku; Badu, Ebenezer Agyemang Article Do board characteristics moderate capital adequacy regulation and bank risk-taking nexus in Sub-Saharan Africa? Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Asiamah, Sampson; Appiah, Kingsely Opoku; Badu, Ebenezer Agyemang (2024) : Do board characteristics moderate capital adequacy regulation and bank risk-taking nexus in SubSaharan Africa?, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 8, Iss. 1, pp. 100-120, https://doi.org/10.1108/AJEB-08-2022-0108 This Version is available at: https://hdl.handle.net/10419/334116 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Do board characteristics moderate capital adequacy regulation and bank risk-taking nexus in Sub-Saharan Africa? Sampson Asiamah Department of Business, Prempeh College, Kumasi, Ghana Kingsely Opoku Appiah Department of Accounting and Finance, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, and Ebenezer Agyemang Badu Department of Agricultural Economics and Extension Education, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Asante Mampong Campus, Asante -Mampong, Ghana Abstract Purpose –The purpose of this paper is to examine whether board characteristics moderate the relationship between capital adequacy regulation and bank risk-taking of universal banks in Sub-Saharan Africa (SSA). Design/methodology/approach –The paper uses 700 bank-year observations of universal banks in SSA between 2009 and 2019. The paper further uses the two-step generalized method of moments as the baseline estimator. Findings –The paper finds that capital adequacy regulation is positively related to overall bank and liquidity risks. Nonetheless, capital adequacy regulation increases credit risk in the sampled banks. The paper further reports that board characteristics individually and significantly moderate the relationship between capital adequacy regulation and risk-taking. Practical implications –The findings have implications for regulators of universal banks that board characteristics matter for capital adequacy regulation to impact risk-taking behavior. Originality/value –The paper extends the existing literature on the effect of board characteristics on the capital adequacy regulations and risk-taking behavior nexus of universal banks. Keywords Capital adequacy, Risk-taking, Universal banks, Sub-Saharan Africa (SSA) Paper type Research paper 1. Introduction Banks’risk-taking behavior has received significant investigation in recent years following the collapse of universal banks and other depository institutions in some emerging economies (Dwekat et al., 2020;Nguyen, 2021). Evidence exists to conclude that excessive risk-taking coupled with regulatory failures is partly responsible for the recent financial crisis in financial institutions. Over the years, various theoretical prepositions and interventions have been suggested to reduce the level of various risks for banks and to strengthen and sustain the financial systems of emerging economies. These prepositions include the adoption of capital AJEB 8,1 100 © Sampson Asiamah, Kingsely Opoku Appiah and Ebenezer Agyemang Badu. Published in Asian Journal of Economics and Banking. 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 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Received 16 August 2022 Revised 6 February 2023 14 March 2023 Accepted 24 April 2023 Asian Journal of Economics and Banking Vol. 8 No. 1, 2024 pp. 100-120 Emerald Publishing Limited e-ISSN: 2633-7991 p-ISSN: 2615-9821 DOI 10.1108/AJEB-08-2022-0108 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 adequacy regulation. Following its wide adoption, its effectiveness in reducing the risk-taking behaviors of banks has received a lot of attention. Several empirical studies have shown interest in investigating the relationship between capital adequacy policy and the risk-taking behavior of banks (Dwekat et al., 2020;Guerrero-Villegas et al., 2018). However, the findings of these studies havebeen inconsistent and contradictory. Dwekat et al. (2020) find a positive but insignificant association between bank regulations and supervision on banks’risk-taking. However, other studies (Guerrero-Villegas et al., 2018;Shrieves and Dahl, 1992;Jacques and Nigro, 1997) report a negative relationship between bank regulation and the risk-taking of banks. Given the mixed findings, there have been recent calls (see Nwude and Nwude, 2021;Nguyen et al., 2021;Govindan et al., 2021) for the relationship between capital adequacy regulation and risk-taking to be re-examined to gain additional insight on the capital adequacy regulation and risk-taking nexus. Moreover, prior studies adopt a simple model to investigate the direct relationship between capital adequacy regulation and bank risk-taking while ignoring the corporate board structure that can affect the effectiveness of the bank to successfully implement policies. In particular, prior studies did not consider the potential moderating role of board characteristics on the relationship between capital adequacy regulation and risk-taking relationships. This noticeable limitation in prior studies has motivated this paper. In this paper, we conjecture that board characteristics moderate the relationship between capital adequacy regulation and a bank’s risk-taking behavior. We further conjecture that a failure to account for board characteristics as a moderating mechanism might be responsible for the mixed findings between capital adequacy regulation and bank risk-taking in the prior empirical literature. There is a convincing theoretical and conceptual basis to argue that capital adequacy regulation and risk-taking nexus is influenced by board characteristics. Many studies (see Agyemang and Appiah, 2017) argue that board characteristics play an important role in the successful implementation of regulations and supervision policies, including capital adequacy regulations. Board of directors, as part of their responsibilities, is to ensure that the bank complies with all the regulatory requirements. This includes capital adequacy regulation. However, achieving such a regulatory requirement is dependent on the effectiveness of the board. The preposition of capital adequacy theory is that the main objective of capital regulation in the banking sector is to prevent managers and owners from taking excessive risks (Kim, 2015;Zhongming et al., 2019). Also, evidence exists to demonstrate that effective board characteristics are able to reduce managers’excessive risk-taking behavior. Considering the fact that board characteristics can influence compliance with regulatory requirements and risk-taking, board characteristics can be expected to moderate the relationship between capital adequacy regulation and bank risk-taking. Nonetheless, prior studies related to the influence of various board characteristics on the relationship between capital adequacy regulation and bank risk-taking behavior are rare. Accordingly, the paper aims to investigate the influence of various board characteristics on the relationship between capital adequacy regulation and bank risk-taking behavior in selected universal banks in Sub-Saharan Africa (SSA). We include Board size (BODSIZE), board independence (BIND) and board gender diversity (BGD) as keyboard characteristics because they are mostly used in prior board, bank risk-taking behaviour and capital regulation studies. Consequently, this paper contributes to the literature in several ways. First, the paper adds to the existing literature by demonstrating that capital adequacy regulation is a significant driver of risk-taking behavior reduction. Although extensive literature exists, the findings have been mixed, ambiguous and inconclusive. Hence, this paper provides further evidence. Second, the paper extends the dynamic relationship between capital adequacy regulations and risk-taking behavior. Unlike prior studies that examined the direct relationship between capital adequacy regulation and bank risk-taking, this paper further Board characteristics effect in universal banks 101 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 examines how board size, independence and gender diversity potentially influence the relationship between capital adequacy regulation and risk-taking behavior. This will provide further insight into how capital adequacy regulations impact on banks’risk-taking. Third, the study was conducted in selected developing economies. There are unique features of SSA that provide a compelling case to examine the moderating role of board characteristics in the relationship between capital adequacy regulation and bank risk-taking. SSAs are characterized by weak corporate governance and a fragile financial system. In recent years, many developing economies have implemented various forms of capital adequacy regulation policies to ensure that the banking sector is stable and sound in line with the requirements of the Basel Accord by the Basel Committee. Despite these massive reforms in the form of capital requirements, the banking sector in SSA seems to be still weak and experiencing a high level of failure due to high risk. This puts doubt on the effectiveness of the capital adequacy requirement (CAR) in reducing bank risk-taking. Therefore, investigating the influence of board characteristics on the CAR policy and the bank’s risk-taking behavior is expected to have implications for bank executives and regulators on how to identify board characteristics that can positively influence the relationship between CAR and bank risk-taking. The paper finds that capital adequacy regulation is positively related to overall bank and liquidity risks. Nonetheless, capital adequacy regulation increases credit risk in the sampled banks. The paper further reports that board characteristics individually and significantly moderate the relationship between capital adequacy regulation and risk-taking. The paper proceeds as follows: Section 2 presents theoretical framing and empirical review. Section 3 captures the research design. Section 4 presents the results and discussion whereas Section 5 captures the conclusion and implications of the study. 2. Theoretical framing Capital adequacy theory argues that banks should hold capital buffers to safeguard banks’ vulnerability to liquidity risk against panic withdrawal (Zhongming et al., 2019). The main objective of capital regulation in the banking sector is to prevent managers and owners from taking excessive risks (Santomero, 1997). Nonetheless, critics of capital adequacy theory argue that CARs may increase a bank’s risk appetite (Calem and Rob, 1999;Milne, 2002). They posit this because it is costly for banks to hold higher capital ratios. Therefore, banks ought to incur more risk to compensate for costs associated with maintaining higher capital ratios. Following this theoretical preposition, empirical evidence on the relationship between capital adequacy and bank risk-taking appears to be contradictory and mixed. Bank shareholders have a high tendency to engage in higher risk behaviors because of moral hazard problems and convex payoff (Jensen and Meckling, 1976;John and Scholes, 1991). Due to the higher information asymmetry level in the banking industry, using debt contracts ex ante is not effective in curbing shareholders from taking more risks (Dewatripont and Tirole, 1994). Also, risk-adjusted capital increases the problem of moral hazard by encouraging shareholders to take more risky investments and failing to control banks’ incentives (Jensen and Meckling, 1976;Merton, 1977;John and Scholes, 1991). Moreover, according to agency theory, the principal–agent relationship should use information in the organization efficiently to minimize information asymmetry and riskbearing costs (Eisenhardt and Cathleen, 1989). Agency theory suggests two potential problems (moral hazard and adverse selection) that may arise within the manager–shareholder relationship for low-disclosure banks. Agency theory and corporate governance are used to recognize or regulate the role of agents in satisfying their part of the contractual relationship governing agency relationships. The basic view held by agency theorists of corporate governance is that the board of directors has a role to ensure that they comply with regulatory AJEB 8,1 102 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 requirements, including CARs and risk-taking. Hence, this study examines the moderating role of board characteristics in the relationship between CARs and risk-taking. 2.1 Empirical review of capital regulation and risk-taking of banks When managers’decisions and activities are highly regulated and supervised by authorities, too much risk-taking and its adverse effect on banks are reduced (Demsetz and Lehn,1985). In the public interest view, banking regulation and supervision policies are geared towards reducing bank risk-taking and ensuring bank sustainability (Petitjean, 2013;Pakhchanyan, 2016;Basel I, 1998;Basel II, 2011; and Basel III, 2015;Rachdi and Bouheni, 2016). Relating to banking regulation, supervisory policies and the level of risk-taking in banks, there are varying findings. Aggarwal and Jacques (2001) and Mateja s ak et al., 2009 attribute the variation in findings to the country, time period and variables studied. Heid and Krem (2003) discover a positive relationship between capital regulation and bank risk-taking in their study of the relevance of capital regulation and bank behavior. Bank regulations and supervision on banks’risk-taking are positive but insignificant associations between bank regulations and supervision. However, Shrieves and Dahl (1992) report a negative relationship between bank regulation and the risk-taking of banks, and Jacques and Nigro (1997) report a negative association between bank regulation and supervisory policies and bank risk-taking. Heid and Krem (2003) find that capital stringency marginally impacts bank risk. Their findings indicate that activity restrictions and deposit insurance (DI) increase bank risk. However, the findings are consistent with previous studies by Demerguç-Kunt and Detragiache (2002) and Barth et al. (2004). Contrary, some studies establish that capital requirements increase banks’risk-taking behavior (Blum, 1999;Calem and Rob, 1999). Alam (2012) finds that tighter restrictions reduce risk-taking. Klomp and De Haan (2012) establish that banking regulation and supervision impact the risk of banks. Rachdi and Bouheni (2016) report that improvement in the regulatory and supervisory policies will decrease the level of risk-taking in commercial banks in Europe. Consequently, the review of prior studies has concentrated in developed economies with strong supervisory capabilities. This SSA case may be different. Beck et al. (2015) argue that banks in SSA are characterized by weak supervisory capabilities and governance framework. In recognition of these weaknesses, Beck et al. (2015) observe that corresponding banks in developed countries required banks operating in SSA maintain high regulatory standards including CAR. Failure to maintain these regulatory standards may risk isolation from global trade. Considering the fact SSA countries are import-driven economies, banks in SSA may not risk isolation from the international trade. Accordingly, this paper conjectures that banks in SSA will comply with capital adequacy regulation and this will positively affect the risk-taking of universal banks. We, therefore, hypothesized that H1. Capital adequacy regulation policy positively affects the risk-taking of universal banks in SSA. 2.2 The moderating role of board characteristics on capital adequacy requirements and risktaking Poor corporate governance structures in banks do not ensure proper monitoring and management of risk, which leads to excessive risk-taking in banks (Jensen, 1993). According to Conyon et al.(2011), weak governance structures have contributed largely to unnecessary risktaking in banks during the financial crunch. Abou-El-Sood (2017) supports this by establishing that weak corporate governance structures in banks lead to inadequate risk monitoring by the board, which ultimately leads to unnecessary risk-taking. Kirkpatrick (2009) establishes that Board characteristics effect in universal banks 103 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 the board’s disclosures of foreseeable risk factors and systems for monitoring and managing risk were severely lacking in many failed banks. But Otero et al. (2019) argue that in order to maximize shareholders’worth, boards of directors and managers of banks take excessive risk. This assertion by Otero et al.(2019)brings about a conflict of interest between shareholders’ maximization theory and stakeholders’theory vying for the stability of banks. The size of the board of directors in banks matters in terms of risk-taking in the banks. Larger boards breed inefficiencies and hinder communication, coordination and decision capabilities to address excessive risk-taking (Jessen, 1983). Further to this, Jensen (1993) emphasizes that larger boards and more regulatory restrictions on outside directorship of banks outweigh the benefits of these governance mechanisms, which eventually undermine performance. As larger boards may exhibit inefficiencies, it is a feature that hinders board communication, coordination and decision-making abilities to mitigate excessive risks in the organization. Rachdi and Ben Ameur (2011) report that smaller boards lead to excessive risktaking by commercial banks in Tunisia, but BIND (nonexecutive directorship) has no effect on the banks’risk-taking when they examine 11 commercial banks in Tunisia from 1997 to 2006. Loh and Sok-Gee (2017) examine listed commercial banks in Malaysia between 2001 and 2012 and report that bigger boards lead to excessive risk-taking by commercial banks in Malaysia. Kusi et al. (2018) examined 215 banks from 29 African countries to establish a relationship between corporate governance and bank risk-taking in Africa, using board size as a measure that was negatively correlated with bank risk-taking in Africa. They conclude that larger boards lead to excessive risk-taking by banks in Africa. Meijer (2017) studying 127 commercial banks selected from developed countries between 2002 and 2016 using ordinary least square (OLS) reports that bigger boards and gender diversity lead to less risk-taking. The independence of boards of directors is negatively associated with bank risk-taking in developed countries. Palavia et al. (2015) study banks in America and report that banks with female board chairpersons take less risk and, however, have high solvency ratios. Zhu et al. (2018) argue that women, by their nature, are risk-averse and serving on banking boards will influence risk decisions positively. Notwithstanding the seemingly contradictory evidence on the relationship between board size, independence and gender diversity on risk-taking, there is a consensus on the impact of these board characteristics in improving board monitoring effectiveness in SSA (see Agyemang and Appiah, 2017;Agyemang and Assabil, 2021). Accordingly, this paper contends that effective configuration of the board in terms of board size, BIND and BGD will influence the relationship between capital adequacy regulation policy and bank risk-taking. Consequently, the paper hypothesized that H2a. Board size has a positive and significant moderating effect on the relationship between capital regulation policy and risk-taking of universal banks. H2b. Board independence has a positive and significant moderating effect on the relationship between capital regulation policy and risk-taking of universal banks. H2c. Board gender diversity has a positive and significant moderating effect on the relationship between capital regulation policy and risk-taking of universal banks. 3. Research design 3.1 Dataset and source A data set on banking capital adequacy regulation, financial ratios and board characteristics (board size, BIND and gender diversity) was manually extracted from annual reports of the banks for the study period (2009–2019). The use of panel data would avoid the problem of multicollinearity, aggregation bias and endogeneity problems (Solomon et al., 2000; AJEB 8,1 104 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 Bouheni, 2014). This study used unbalanced dynamic panel data for regulation and supervision, financial ratios and board size regression analysis to measure, establish and analyze the effect of regulation and supervision on bank performance and risk and also the moderating effect of board size on the relationship between regulation and supervision and performance on one hand and risk on the other hand of universal banks from Ghana, Nigeria and Kenya between 2009 and 2019. The focus of the study is all the universal/commercial banks in Ghana, Nigeria and Kenya. In all, 70 universal/commercial banks representing 82% of the banks in selected countries were considered for the study. This consists of 22 from Ghana, 16 from Nigeria and 32 from Kenya. Appendix 1 shows the sampled universal banks used for the study from each of the three countries. 3.2 Measurement of variables In this paper, our variable of interest is risk-taking of commercial/universal banks. Consistent with prior studies, the risk-taking of commercial and universal banks is proxied by Z-score, liquidity risk and credit risk. Z-score is the ratio of ROA plus EAR to the standard deviation of ROA, where ROA is the return on assets and EAR is the proportion of equity to assets (Higher Z-scores indicate lower chance of default and hence better performance with regard to risk management). Z-score used is the banking insolvency risk measure developed by Boyd et al., 1993. log ZSCORE ¼logROA þEAR δROA Liquidity Risk is measured using Loan-to-Deposit Ratio (LDR) which is the comparison of the total loans of the bank to the total deposit of the banks. Basically, the ratio is expressed as a percentage. Higher LDR ratio is not healthy for the banks because a slight increase in the demand for deposits by the depositors may lead to liquidity problems. LDR ¼gross loans total deposit Credit Risk is measured using loan loss provision to gross loans which is the comparison of loan loss provision or loan impairment charge to the gross loans granted at the end of the year. Higher ratio surfaces when the nonperforming loans are on the increase. Higher ratio depicts high credit risk and lower ratio indicates lower credit risk (Epure and Lafuente, 2015 and Muriithi, 2016). loan loss provision ratio ¼loan loss provision=gross loans and advance The independent variable of the study is the capital adequacy regulation. CAR is measured using regulatory capital requirement ratio which is CAR ¼Tier 1ðcore capitalÞ=RWA The moderating variables in this paper are corporate governance characteristics proxied by board size, BIND and BGD. Board size is measured as the number of individuals serving on the banking boards at the end of each financial year whereas BIND is measured as proportion of nonexecutive directors on board. BGD is measured as proportion of females on board. We also include in our model control variables. These include bank size for bank-specific variables and countries’macroeconomics indicators which include inflation rate, prime interest rate and GDP growth rate. Bank size is measured as the natural log of total assets of Board characteristics effect in universal banks 105 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 the banks. Size might be an important determinant of bank performance if there are increasing returns to scale in banking. The total assets for each bank for each year were reported in the local currency in the financial report of the sampled banks. Therefore, we used the average exchange rate at the end of each year to convert the value of total asset to USD. This approach is consistent with prior empirical literature (see Dietrich and Wanzenried (2011),Louzis et al. (2012) and Tan (2015)). The full details of the proxies used to measure the variables considered in this paper are captured in Appendix 2. 3.3 Model specification The basic model to be estimated takes the form of RISK TAKING ¼ffðCAR;BSIZE;INTRA;INFLA;GDPÞg (1) Incorporating error term and variable coefficients, the model for the dynamic generalized method of moments (GMM) short-term run measure of risk-taking becomes Rjit ¼β0þβ1Rjit−1þβ2CARjit þβ3Bsizejit þβ4INTRAjit þβ5IFLAjit þβ6GDPjit þyt þe (2) where R jit is the measure of banks’risk-taking (using banking insolvency’sZ-score, credit and liquidity risks) and R jit-1 is the lagged bank risk which emphasizes that the current year’s risk-taking depends on the previous year’srisklevel.CAR –capital adequacy requirement policy, Bsize –bank size, INTRA jit –interaction term (board size, independence and gender diversity), IFLA jit –inflation rate, GDP –growth rate, yt –year dummy and e–error term. Where j51–3, i 51–70 and t51–10. Considering variables and dataset for the study and empirical analysis to be done, other commonly used estimation techniques are inappropriate for this study. SYS-GMM is regarded as the finest estimation method with reference to econometric setting of the study (De Vita and Luo, 2018). The GMM is a statistical method that combines observed economic data with the information on population moment conditions to produce estimates of the unknown parameters of this economic model (Muriithii, 2016). Method two-step GMM-in-System estimator is used for this study and as suggested by Roodman (2009) and Bouheni (2014), this study considers a number of banks –70 –and a time period of 11 years –2009–2019. In studies like this, featuring such dataset, GMM estimator works well. This study considers time lag in view of that dynamic regression to test hypotheses. As suggested by Wooldridge (2010), the study adopts dynamic panel models because time lags are considered in the study and also there is likelihood of presence or absence of autocorrelation dynamics, in such situations, dynamic panel analysis is useful. The appropriate estimating technique for dynamic panel analysis as suggested by Verbeek (2004) is GMM estimator. As postulated by De Vita (2018) and Kyaw (2017), the two-step SYS-GMM estimator accounts for the fundamental dynamics of the data generation procedure while also dealing with country-specific effects, measurement error and endogeneity problems as compared to other estimating techniques such as fixed effect, random effect OLS and even one-step SYS-GMM. The GMM estimator also addresses the problem of reversal causality and simultaneity bias (Hansen, 1982;Liang et al., 2013;Tan, 2015;Hakimi et al., 2018). Two-step SYS-GMM estimation technique is widely used in corporate governance and finance studies since it deals with perceived endogeneity by using lagged variables (De Vita and Luo, 2018). Among such studies are Wintoki et al. (2012),De Mendonça et al. (2012), Adams and Mehran (2012),Liang et al. (2013),Kyaw (2017),Bouheni (2014),Haque (2017) and De Vita and Luo (2018). AJEB 8,1 106 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 4. Results and discussion 4.1 Descriptive statistics Table 1 shows the descriptive statistics of the study variables, that is, observations, mean, standard deviation, minimum and maximum values. From Table 1, the minimum assets stood at as low as $57,146 and the maximum at $9,085,662, with a mean of $670,682.20. From Table 1, majority of the banks have an LDR at the threshold of 70.9%, but ideally, 50–60% should be expected. Higher LDR might translate into growth of banks’average profits during the period under study since an increase in liquidity of banks reduces credits, hence profits (Sahyouni and Wang, 2019). The standard deviation of 59.1% lying below the mean is an indication that the dispersion is not all that much. But the maximum exceeding 100%, that is 1102.1%, is alarming. The loan loss provision on average stood at 3.6%, which indicates that credit risk is well managed, though it is dispersedly distributed among the banks, having a standard deviation of 19.5%, which is greater than the mean value. Again, some banks exhibit a credit risk of 488.9%, which is dangerous and can endanger the liquidity position of the banks. Notwithstanding, some banks making savings from loan loss provisions is an indication of proper management of credit risk during the study period. From Table 1, the banks are adequately capitalized considering the mean of 24% compared to the 8% threshold recommended by the Basel II accord and implemented by most regulators. The standard deviation of 27% indicates a high level of dispersion. Also, the number of members on the banking boards is too wide; thus, a difference of 15 between the minimum and maximum with an average of 10 members. The extreme is the presence of toobig-to-fail banks, which have other investment opportunities and for which more expertise is needed to manage various business ventures. From study observations, most banks are focused on traditional banking activities and, therefore, do not have larger boards. The minimum of five is consistent with the findings of Atuahene (2016) and Kyeneboah-Coleman and Bierpe (2006). Outside directorship is strongly advocated in the subregion, thus an average of seven independent directors to the average board size of ten. On the contrary, the proportion of women on the banking boards is very low, comparing an average of two to that of an average board size of ten. It can be observed that the BGD was sticky notwithstanding the fact that there were some variations with some banks from one year to another. This is consistent with the observations made by Ntim (2016) that board attributes such as BGD turn out to be sticky. This implies that do not easily change unless there is a change in policy Variables Observations Mean Standard deviation Minimum Maximum ZSCORE 650 1.225 0.358 0.308 3.783 LDR 665 0.709 0.591 0 11.022 CDR 661 0.037 0.195 0.045 4.889 CAR 598 0.216 0.270 1.98 2.618 BODSIZE 589 10.13 3.165 5 20 NED 589 6.996 2.287 4 13 FEMALES 589 1.740 1.257 0 6 ASSETS($) 770 670682.20 10,556,908 57,146 9,085,662 PRIME RATE 770 0.127 0.052 0.014 0.26 INFLATION 770 0.096 0.043 0.032 0.189 GDP 770 0.058 0.0308 0.016 0.174 Note(s): ROAs –Return on Assets; ROE –Return on Equity; NIM –Net Interest Margin; LDR –Liquidity Risk; CDR –Credit Risk; CAR –Capital Adequacy Requirement; BODSIZE –Board Size; BIND –Board Independence; BGD –Board Gender Diversity; GDP –Gross Domestic Product Growth Table 1. 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(2011), “Board characteristics, performance and risk-taking behaviour in Tunisian banks”,International Journal of Business and Management, Vol. 6 No. 6, pp. 88-97. Van, K. and Santemero, M. (1997), “Management responses to social activism in an era of corporate responsibility: a case study”,Journal of Business Ethics, Vol. 118 No. 2, pp. 497-513. Corresponding author Ebenezer Agyemang Badu can be contacted at: [email protected] AJEB 8,1 116 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 Appendix 1 Name of the bank ID Country Access Bank 1 Ghana Agricultural Development Bank 2 Ghana Bank of Africa 3 Ghana Barclays Bank 4 Ghana Sahel Sahara Bank 5 Ghana CalBank 6 Ghana Ecobank 7 Ghana First Atlantic Bank 8 Ghana Fidelity Bank 9 Ghana FBN Bank 10 Ghana First National Bank 11 Ghana GCB Bank 12 Ghana Guaranty Trust Bank 13 Ghana HFC (Republic) Bank 14 Ghana Universal Merchant Bank 15 Ghana National Investment Bank 16 Ghana Prudential Bank 17 Ghana Standard Chartered Bank 18 Ghana Societe Generale Bank 19 Ghana Stanbic Bank 20 Ghana United Bank for Africa 21 Ghana Zenith Bank 22 Ghana Access Bank 23 Nigeria Citi Bank 24 Nigeria Diamond Bank 25 Nigeria Ecobank 26 Nigeria Fidelity Bank 27 Nigeria First Bank 28 Nigeria First City Bank 29 Nigeria Guaranty Trust Bank 30 Nigeria Skype/Polaris Bank 31 Nigeria Stanbic IBTC Bank 32 Nigeria Sterling Bank 33 Nigeria United Bank for Africa 34 Nigeria Union Bank of Nigeria 35 Nigeria Unity Bank Plc 36 Nigeria Wema Bank 37 Nigeria Zenith Bank 38 Nigeria KCB Bank Kenya Ltd 39 Kenya Equity Bank Kenya Ltd 40 Kenya The Co-operative Bank 41 Kenya Barclays Bank of Kenya 42 Kenya Standard Chartered Bank Kenya Ltd 43 Kenya Diamond Trust Bank 44 Kenya Stanbic Bank Kenya Ltd 45 Kenya Commercial Bank of Africa 46 Kenya I&M Bank Ltd 47 Kenya NIC Bank Plc 48 Kenya Bank of Baroda 49 Kenya Prime Bank Ltd 50 Kenya National Bank of Kenya Ltd 51 Kenya Citibank N.A. Kenya 52 Kenya (continued) Table A1. Universal banks used for the study Board characteristics effect in universal banks 117 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 Name of the bank ID Country Bank of India 53 Kenya Family Bank Ltd 54 Kenya Ecobank Kenya Ltd 55 Kenya Bank of Africa (K) Ltd 56 Kenya Victoria Commercial Bank 57 Kenya Gulf African Bank Ltd 58 Kenya Guaranty Trust Bank Ltd 59 Kenya African Banking Corporation Ltd 60 Kenya Sidian Bank Ltd 61 Kenya Credit Bank Ltd 62 Kenya Guardian Bank Limited 63 Kenya First Community Bank Ltd 64 Kenya UBA Kenya Bank Ltd 65 Kenya M-Oriental Commercial Bank Ltd 66 Kenya Transnational Bank Limited 67 Kenya Consolidated Bank Limited 68 Kenya Paramount Bank Ltd 69 Kenya Spire Bank Limited 70 Kenya Table A1. AJEB 8,1 118 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 Appendix 2 Research variables Proxies Expected signs Measurement Data Source Dependent variables ROA/ ROE/ NIM Liquidity/credit Risk ZSCORE log ZSCORE ¼logROA þEAR δROABank Scope/Audited Annual Report 2009–2019 Liquidity risk LQR 5gross loans/deposit Credit risk CDR 5loan loss provision/gross loans Independent variables Regulation Capital Adequacy Requirement (CAR) or Stringency þ–CAR 5TIER 1/RWA Bank Scope/ Audited Annual Report 2009–2019 Bank-specific variables Banks Size (Log of total assets) þþ Log of banks’total assets Corporate governance characteristics Board Size (BSIZE), Board independence (BIND) and Board Gender Diversity (BGD) þ–BSIZE 5number of individuals on board at the end of the financial year; BIND 5proportion of nonexecutive directors on board; BGD 5proportion of females on board Macroeconomic variables – countrywide data Inflation, GDP, prime interest rate þ/þ/Central Banks, World Bank, IMF, IFSM and WDI Source(s): Researcher’s field survey Table A2. Measurement of variables, expected signs and data source Board characteristics effect in universal banks 119 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025 Appendix 3 Variables ROA ROE NIM ZSCORE LDR CDR CAR BODSIZE BIND BGD CAR*BODS CAR*BIND CAR*BGD Assets ITRA Inflation GDP ROA 1.000 ROE 0.774 1.000 NIM 0.058 0.022 1.000 ZSCORE 0.360 0.264 0.008 1.000 LDR 0.129 0.119 0.011 0.036 1.000 CDR 0.241 0.177 0.002 0.159 0.036 1.000 CAR 0.175 0.044 0.012 0.105 0.062 0.010 1.000 BODSIZE 0.012 0.020 0.020 0.080 0.026 0.053 0.212 1.000 BIND 0.003 0.050 0.085 0.110 0.015 0.001 0.189 0.733 1.000 BGD 0.072 0.138 0.022 0.008 0.010 0.021 0.256 0.185 0.043 1.000 CAR*BODS 0.296 0.151 0.017 0.003 0.139 0.037 0.779 0.167 0.205 0.176 1.000 CAR*BIND 0.267 0.124 0.029 0.077 0.116 0.065 0.629 0.173 0.303 0.088 0.906 1.000 CAR*BGD 0.099 0.069 0.023 0.0249 0.022 0.242 0.172 0.271 0.150 0.132 0.104 0.141 1.000 ASSETS 0.305 0.272 0.032 0.285 0.003 0.077 0.321 0.634 0.501 0.372 0.343 0.332 0.028 1.000 PRIME RATE 0.061 0.082 0.016 0.212 0.153 0.226 0.160 0.175 0.180 0.007 0.069 0.130 0.332 0.182 1.000 INFLATION 0.035 0.070 0.038 0.167 0.003 0.245 0.058 0.175 0.023 0.007 0.016 0.080 0.376 0.021 0.402 1.000 GDP 0.029 0.043 0.038 0.034 0.137 0.084 0.023 0.206 0.099 0.126 0.090 0.119 0.085 0.238 0.198 0.380 1.000 Note(s): ROA –Return on Assets; ROE –Return on Equity; NIM –Net Interest Margin; LDR –Liquidity Risk; CDR –Credit Risk; CAR –Capital Adequacy Regulation; BODSIZE –Board Size; BIND –Board Independence; BGD –Board Gender Diversity; CAR*BODS –Interaction of Capital Adequacy Regulation and Board Size; CAR*BIND –Interaction of Capital Adequacy Regulation and Board Independence; CAR*BGD –Interaction of Capital Adequacy Regulation and Board Gender Diversity; GDP –Gross Domestic Product Growth; ITRA –Interest rate Table A3. Person correlation matrix of the study variables AJEB 8,1 120 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/100/9525649/ajeb-08-2022-0108.pdf by ZBW German National Library of Economics user on 16 December 2025