Do Board Characteristics Affect Non-Performing Loans? GCC vs. Non-GCC Insights

Abstract

The Middle East and North Africa (MENA) region has faced challenges like political instability and economic fluctuations, which have impacted non-performing loans (NPL) levels. At the same time, over the years, reforms and regulations have encouraged stronger board structures to enhance corporate governance and improve risk management. The purpose of this paper is to investigate how board characteristics affect non-performing in the MENA region. Board characteristics shape governance quality, which influences risk management and reduces banks’ risk-taking behaviours. Hence, effective governance can reduce non-performing loans by improving oversight and credit decisions. To this end, we used a sample of 70 banks operating in 12 countries in the MENA region from 2010 to 2022. The System Generalized Method of Moments (SGMM) was employed as an empirical technique. To benefit from a comparative analysis, we divided the entire sample into two subsamples. The first subsample covers six Gulf Cooperation Council (GCC) countries with 42 banks. The second subsample is also relative to six non-Gulf Cooperation Council (non-GCC) countries with 28 banks. The empirical findings indicate that the presence of independent board members, a higher number of female board members, board remuneration, and the board index decrease NPLs across all regions, including MENA, GCC, and non-GCC. However, we found that board size, tenure, and duality increase NPLs. The results of this paper are beneficial for both policymakers and bankers, as they provide insights into how governance through board characteristics influences credit risk. These results support better decision-making in board appointments and governance practices to improve risk management and reduce non-performing loans.

1. Introduction

Board characteristics and NPLs play a significant role in bank and financial stability, particularly in the corporate governance framework. Strong governance and responsible lending ensure long-term resilience and reduce NPLs (Laeven & Levine, 2009; Correa & Goldberg, 2021). Furthermore, the banking sector plays a crucial role in economic development due to its financial functions (Beck & Levine, 2004; Tongurai & Vithessonthi, 2018). Therefore, its main priority is to ensure the sustained progression of economic and financial development (Sallemi et al., 2023). Since the financial crisis, this sector has faced serious problems, including a significant accumulation of NPLs. This poses serious risks to banks and can even lead to their failure (Jin et al., 2011; W. Lu & Whidbee, 2016). NPLs have become an alarming problem because they undermine the quality of bank assets and jeopardize the viability of the banking sector. Thus, banks must pay close attention to their growth to maintain stability (Polat, 2018; Park, 2012).

Researchers have given great attention to the determinants of NPLs, which can be classified into three main categories: The first category includes bank-specific variables like size, capital, liquidity risk, and performance (Chaibi & Ftiti, 2015; Hassan et al., 2018; Naili & Lahrichi, 2020). According to Saliba et al. (2023), bank-specific variables are significant predictors of NPLs in the BRICS countries. The second category contains macroeconomic variables that serve to check the environment in which a bank operates, such as GDP growth, inflation, unemployment, and institutional quality. Kumar et al. (2018); Jabbouri et al. (2022); and Karismaulia et al. (2023). Moreover, Adusei and Sarpong-Danquah (2021) highlighted that economic conditions are a significant determinant of the financial circumstances of bank customers, ultimately impacting their banking activities and performance. Lastly, the third category focuses on industry-specific indicators, offering a comprehensive perspective on the structure of the banking sector, such as competition and market concentration (Karadima & Louri, 2020; Alnabulsi et al., 2022).

Several studies have examined the structure of banking governance and its potential role in reducing NPLs. An effective governance structure improves operational efficiency and helps mitigate fraud and information asymmetry (Massi, 2016). In this context, there is a growing emphasis on bank governance structures, particularly regarding internal governance mechanisms that significantly impact banking risks (Golliard & Poder, 2007).

NPLs pose a significant risk to banks by reducing their profits and affecting their efficiency. In turn, they also impact financial stability and the real economy (Alnabulsi et al., 2023; Chun & Ardaaragchaa, 2024). According to Awad et al. (2024), banking governance is crucial for addressing problems by adhering to standards and regulations that ensure bank safety. The banks’ main objective is to establish a strong governance structure through boards of directors, which are responsible for overseeing organizational strategies to optimize the utilization of skills and achieve goals. This system also aims to define the standards for the board’s composition and structure. The board of directors is responsible for recruiting, evaluating, training, compensating, and dismissing executives, including the CEO (Jensen, 1993).

Banking governance differs from corporate governance due to its increasing opacity, which is linked to information asymmetry, high levels of indebtedness, and strict regulation. These factors amplify the role of the board of directors in reducing risks (Salas & Saurina, 2003). The board serves as a crucial control mechanism, supervising executives and guiding the bank’s strategy and implementation (Andres & Vallelado, 2008; J. G. Fernandes et al., 2017).

In the MENA region, NPLs remain a key concern for financial stability, highlighting the importance of governance, regulatory oversight, and economic resilience in addressing credit risk (Alnabulsi et al., 2022; Alnabulsi et al., 2023). This area presents a mixed picture, reflecting the region’s economic diversity and the varying degrees of financial sector development. Indeed, in more stable and wealthier Gulf countries, such as the UAE and Saudi Arabia, NPL ratios tend to be relatively low due to stronger banking regulations, better risk management, and government support (Alandejani & Asutay, 2017). In contrast, countries facing political instability, conflict, or economic difficulties often experience higher NPL levels (Hakimi et al., 2022).

Bank governance, particularly board characteristics, plays a critical role in managing NPLs in the MENA region. In the MENA region, where banks operate under varied economic, political, and regulatory conditions, strong board governance is especially important for ensuring sound credit decisions and accountability. However, weak governance structures often lead to poor risk assessment and higher NPL ratios, particularly in countries with political instability or limited regulatory enforcement (Al-Saeed, 2023).

As a consequence, studying the link between board characteristics and NPLs in this region is crucial. The motivation to explore the effect of board characteristics on non-performing loans in the MENA region arises from the crucial role that boards play in shaping bank governance and overseeing credit risk. In many MENA countries, banks face challenges such as weak regulatory enforcement, concentrated ownership, and economic volatility, making internal governance mechanisms, particularly board characteristics, even more critical. Board characteristics, such as board independence, size, diversity, and expertise, can significantly influence how effectively boards monitor lending practices and manage risk. Understanding this relationship helps highlight how specific board features can contribute to reducing NPLs and improving financial stability in a region. It also helps understand how internal governance through board characteristics influences banks’ ability to manage credit risk. It serves as a guide for both policymakers and financial institutions to strengthen governance frameworks that reduce loan defaults and support long-term financial stability.

The discussion regarding its effectiveness is based on these main characteristics. Jensen (1993) states that a board’s effectiveness is linked to its attributes, notably non-duality, board size, and the presence of potential shareholders. Furthermore, Sumner and Webb (2005) conclude that board structure influences credit policies. John et al. (2016) reported that the effectiveness of a board depends on its structure, namely its independence, size, committees, and the duality between the CEO and the Chairman, as well as its quality. Recently, the literature has emphasized the relationship between credit risk and NPLs. When reviewing the existing literature, we note the lack of comprehensive research that considers the combined impact of various board characteristics, such as gender diversity, board tenure, and administrators’ compensation, on NPLs. Additionally, the MENA region covers two different groups of countries, namely the GCC and GCC, which should be separately examined due to several economic, financial, and regulatory differences. This gap highlights the need for region-specific studies to better understand how governance structures influence credit risk in MENA banks’ operations.

The objective of this paper is to investigate how board characteristics affect non-performing loans in the MENA region. It also seeks to explore how this effect differs across the two sub-regions of the MENA region. Given the critical role that boards play in overseeing credit policies and monitoring management decisions, this study investigates whether board characteristics are associated with lower levels of credit risk, as proxied by NPLs. This is particularly relevant in the MENA region, where governance practices and regulatory frameworks differ across countries, potentially affecting the stability and performance of banks.

To achieve these goals, we used a sample of 70 banks located in 12 countries in the MENA region from 2010 to 2020. To benefit from a comparative analysis, the entire sample of MENA countries was divided into six GCC and six non-GCC countries. The empirical model employed in this study is based on the SGMM.

Overall, the findings indicate that board size and tenure positively impact NPLs across all the regions analyzed. Regarding duality, it has a positive and significant impact only in the MENA and GCC regions. Conversely, board independence, gender diversity, board compensation, and the board index significantly and negatively affect NPLs across all regions, including MENA, GCC, and non-GCC.

This study makes several significant contributions to the literature on bank governance in developing countries. First, it addresses a notable research gap by examining the effects of board characteristics, such as gender diversity, board tenure, and administrators’ compensation, on NPLs. This issue has become a priority in the MENA region. Second, while previous research has typically analyzed two or three board characteristics separately (Boussaada et al., 2018; and C. Fernandes et al., 2021), this study takes a more holistic approach by constructing a board of directors’ index. Third, the entire sample of MENA countries was divided into two subsamples to benefit from a comparative analysis. Fourth, based on the findings of this paper, policymakers and bankers can adjust the structure and characteristics of the board of directors to reduce credit risk effectively.

The remainder of this paper is organized as follows: Section 2 provides a review of the related literature and outlines the development of the hypotheses. Section 3 describes the sample, the empirical approach, and the model specification. Section 4 presents the analysis and discusses the empirical findings. Finally, Section 5 concludes the paper and provides policy recommendations.

2. Literature Review and Hypotheses

2.1. Board Size and NPLs

Board size refers to the total number of bank board members (Muchemwa et al., 2016). Several studies have confirmed the inverse relationship between board size and NPLs (Switzer & Wang, 2013; Abid et al., 2021). According to this hypothesis, J. Lu and Boateng (2018) concluded that a larger board with well-qualified directors adopts prudent and less risky credit management strategies and a more regulated decision-making process. Rose (2017) concluded that adding two or more internal directors minimizes credit risk exposure and creates a stronger system of governance. Jensen (1993) concluded that an optimal size of 7 to 8 members seems necessary to coordinate its members’ viewpoints, promote speedy and appropriate decision-making, eliminate agency difficulties, and mitigate abusive behaviors. Other studies have not demonstrated any significant effect of board size on NPLs. (Boussaada et al., 2020; Djebali & Zaghdoudi, 2019).

Several studies have found a positive relationship between board size and the level of NPLs (Hakimi et al., 2023; Tarchouna et al., 2021; Boussaada et al., 2018). Similarly, Aljughaiman et al. (2024) state that size and board independence are used to measure board structure, which is one of the most important mechanisms for controlling agency problems within firms. According to Hakimi et al. (2022), board members face severe information asymmetries and intense pressure to handle loan applications, which leads to poor credit decisions. Doğan and Ekşi (2020) found that small board sizes negatively impact bank performance and credit portfolio quality, which causes financial instability. Consequently, a larger board is beneficial for minimizing NPLs and maintaining bank stability. In contrast, Pathan (2009) identifies smaller boards as crucial in promoting excessive risk-taking. However, it allows shareholders to apply more direct pressure on decisions, potentially encouraging directors to adopt riskier behaviors (Beltratti & Stulz, 2009; Pathan, 2009).

H1: 

Board size increases NPLs.

2.2. Board Independence and NPLs

The inclusion of independent directors on boards has attracted considerable attention from researchers. Weisbach (1988) demonstrates that more independent directors can mitigate conflicts of interest. Furthermore, these directors enhance the board’s independence, ensure more effective management oversight, improve overall board efficiency, and enable a more accurate assessment of the company’s performance (Ploix, 2003).

Empirically, Abid et al. (2021) and Djebali and Zaghdoudi (2019) found that independent directors positively impact NPLs. However, independent directors tend to have less comprehensive knowledge of management policies and decision-making processes than internal directors. However, Tarchouna et al. (2021), using the GMM approach on a sample of 184 American commercial banks from 2000 to 2013, found that a higher proportion of independent directors on the board ensures effective oversight. This, in turn, contributes to improved loan quality (Doğan & Ekşi, 2020). In addition, Boussaada et al. (2018) concluded that the presence of independent directors is essential for resolving agency conflicts. Switzer and Wang (2013) attributed this association to the board’s capacity to provide independent oversight, effectively assess management practices, and improve the bank’s performance. Similarly, Pathan (2009) showed a negative relationship between the presence of independent directors and risk-taking, emphasizing their role in balancing the interests of shareholders and other stakeholders.

H2: 

Board independence reduces NPLs.

2.3. Duality and NPLs

In the corporate governance literature, there is no agreement on the impact of duality on NPLs. Hakimi et al. (2022) and J. Lu and Boateng (2018) discovered that duality increases NPL levels. Combining the responsibilities of the CEO and board chair may have a detrimental impact on the approval of credit decisions since it promotes entrenchment and prioritizes personal interests. Separating decision-making from supervisory responsibilities, on the other hand, helps in the decentralization of authority and provides more effective executive monitoring. Furthermore, Abid et al. (2021) revealed that duality positively impacts credit risk in public banks, contributing to reducing NPLs in private banks.

However, Djebali and Zaghdoudi (2019) found that combining the positions of the chairman and CEO helps reduce credit risk. This duality improves decision-making control and protects both individual and shareholder interests. Similarly, Pathan (2009) pointed out that when CEOs accomplish various functions, they can convince directors to pursue less risky strategies, as they are often motivated by a preference for risk aversion. Simpson and Gleason (1999) found that this duality minimizes the likelihood of bankruptcy. Daily and Dalton (1994) found a negative relationship between leadership duality and the probability of corporate failure. Furthermore, Switzer and Wang (2013) find no significant relationship between duality and credit risk.

H3: 

Duality increases NPLs.

2.4. Gender Diversity and NPLs

This study considers gender diversity an essential characteristic that can significantly affect NPLs. Board Gender diversity, marked by the participation of female directors, is commonly considered essential in the board (Mohsni et al., 2021). They contribute significantly to improving a company’s reputation and value while also increasing its performance (Lückerath-Rovers, 2013; Olaoye & Adewumi, 2020). Furthermore, Post et al. (2011) noted that the participation of women enhances the integration of diverse knowledge, perspectives, and viewpoints into decision-making processes. Kramer et al. (2006) demonstrated that having a single woman on the board can improve its efficacy.

Kinateder et al. (2021) proved that gender diversity is a key factor in reducing credit risk. Furthermore, they deployed critical mass theory to prove that boards with three or more women are more likely to manage and minimize credit risks than boards composed of men. Setiawan and Khoirotunnisa (2020) indicate that a higher proportion of women leads to a more effective control system. J. Lu and Boateng (2018) noted that female directors are recognized for their risk aversion in implementing and controlling risk management strategies and making financial decisions. According to Berger et al. (2014), board changes that increase the proportion of female board members result in more group diversity at the executive level, which has an impact on the bank’s risk strategy. Furthermore, new female board members have much less experience, which suggests that a lack of knowledge increases portfolio risk. Similarly, Adams and Funk (2012) find that female directors are more likely to take risks than men. Furthermore, Ahern and Dittmar (2012) find that Norway’s implementation of a gender quota in 2003 had a negative impact on firm values since the appointed female directors lacked expertise and were younger on average.

H4: 

Gender diversity reduces NPLs.

2.5. Board Tenure and NPLs

Another crucial characteristic of a board is tenure, which is a fundamental aspect of a management team, as it shapes engagement, experience, and decision-making processes (Golden & Zajac, 2001). However, Huang and Hilary (2018) contend that a director’s tenure represents a trade-off between the accumulation of knowledge and board independence. According to Vafeas (2003), extended board tenure enhances both management commitment and competence while expanding the organization’s knowledge base. Similarly, Golden and Zajac (2001) emphasize the importance of tenure in enhancing this institutional knowledge.

Furthermore, Beasley (1996) emphasizes that directors with longer tenures are less vulnerable to managerial pressure, hence increasing their effectiveness in reducing opportunistic behavior within the management team. In contrast, Byrd et al. (2010) present the “CEO loyalty hypothesis,” which contends that long-serving directors may build close relationships with executives, including the CEO, thereby jeopardizing their ability to prioritize shareholders’ interests. To study the relationship between board tenure and banking credit risk on an international scale, Kinateder et al. (2021) used data from 141 listed banks over the period 2006–2017. They concluded that banks with longer tenures face lower risks.

H5: 

Board tenure increases NPLs.

2.6. Board Compensation and NPLs

Administrator compensation significantly affects company investment decisions, especially when moral hazard and managerial discretion are present (John et al., 2016). Furthermore, Brewer et al. (2004) find a relationship between increased banking risk and a higher proportion of CEO compensation based on equity capital growth. Chen et al. (2006) found that remuneration structures based on stock options increased risk-taking in the banking sector. Similarly, Bebchuk and Spamann (2010) argue that incentive-based remuneration in banks encourages excessive risk-taking, which plays a significant part in the current financial crisis. Faccio et al. (2016) established a positive relationship between CEO compensation and risk-taking measures, suggesting that higher financial incentives motivate riskier decision-making among banking executives.

Some empirical studies have examined the relationship between compensation and NPLs. J. Lu and Boateng (2018) and Rose (2017) found that higher CEO compensation is linked to an increase in NPLs, suggesting that it incentivizes executives to engage in excessive risk-taking to achieve potential income. These findings align with the results of DeYoung and Torna (2013), who analyzed quarterly data from US banks during the 2008–2010 financial crisis. They concluded that high compensation for stakeholders, including CEOs, increases the likelihood of bank failures, as higher income motivates CEOs to make riskier choices. Based on this empirical discussion, we propose the following hypothesis:

H6: 

Board compensation increases NPLs.

From the above development, we note three main observations that motivate this paper. First, there is a lack of comprehensive research that considers the combined impact of various board characteristics, such as gender diversity, board tenure, and administrators’ compensation, on NPLs. Second, most previous studies have investigated the effect of board characteristics on NPLs using separate characteristics. In the current study, we respect this step, and we also built a synthetic index of board characteristics. Third, the MENA countries cover two different groups of countries, namely the GCC and GCC, which should be separately examined due to several economic, financial, and regulatory differences. This gap highlights the need for region-specific studies to better understand how governance structures influence credit risk in MENA banks. Based on previous studies that examined the relationship between board characteristics and NPLs, this study focuses on key attributes such as board size, independence, duality, gender diversity, tenure, and compensation.

3. Methodology

3.1. The Sample

This research uses a sample of 70 banks from 12 MENA countries observed over the period 2010–2022. Initially, we compiled a sample of 181 banks from the MENA region for the period 2000–2022. Due to the unavailability of certain data, we retained only banks with at least five board characteristics over ten years. Our final sample includes 70 banks, distributed as follows: 42 banks from the 6 GCC countries and 28 banks from non-GCC countries.

To begin this study, we collected data related to banks and their revenue characteristics from the Refinitiv Eikon database and the annual reports of each bank. We also obtained macroeconomic variables and industry-specific variables from the World Bank database (World Bank Indicators-WDI, Global Financial Development-GFD).

Table 1. Sample distribution and composition.

The Middle East and North Africa (MENA) Countries.
GCC Countries			Non-GCC Countries
Countries	N	%	Countries	N	%
Saudi Arabia Bahrain United Arab Emirates Kuwait Qatar Oman	10 2 11 7 7 5	14.28% 2.86% 15.71% 10% 10% 7.14%	7. Egypt 8. Jordan 9. Lebanon 10. Morocco 11. Tunisia 12. Turkey	3 4 1 2 10 8	4.28% 5.71% 1.43% 2.86 14.28% 11.43%
Total	42	60%	Total	28	40%

presents the distribution of our samples by country.

3.2. Empirical Approach, Model Specification and Variable Selection

As a panel data framework, the System Generalized Method of Moment (SGMM) is more effective as it resolves biases caused by missing data. The System-GMM estimator, which was developed by Blundell and Bond (1998), was chosen mainly because of the econometric issues that our dataset provides. Specifically, our study uses panel data with a moderate time dimension and a relatively larger cross-sectional dimension (i.e., a short T (13) and large N (70) structure) to examine the effects of board characteristics on non-performing loans (NPLs). Additionally, it addresses the endogeneity issue of explanatory variables, leading to more efficient and reliable results (Boussaada et al., 2025; Hakimi et al., 2022; and Danisman & Tarazi, 2020). Moreover, this method takes into account the dynamic nature of NPLs(−1) as a dependent variable arising from the persistence of bank risk-taking behavior over time, controls for unobserved heterogeneity specific to each bank and reduces measurement errors that affect risk indicators and board structure variables.

In this study, we measured the dependent variable using the NPLs ratio, which is widely recognized in the literature as an indicator for assessing bank credit risk (Tarchouna et al., 2021; Jabbouri et al., 2022). To assess the impact of board characteristics on NPLs, the key independent variables include board size, duality, board independence, gender diversity, compensation, and board tenure.

The empirical strategy is based on two models: In the first model, we estimate the impact of board characteristics on NPLs separately (Figure 1) using the following Equation (1):

$$\begin{array}{c}{\mathrm{NPLS}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{NPLS}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\beta }}_2\mathsf{\Sigma }{\mathrm{CA}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{CAP}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{ROA}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{LTD}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_7{\mathrm{NII}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{GDP}}_{\mathrm{i}}\\ +{\mathsf{\beta }}_9{\mathrm{INF}}_{\mathrm{i}}+{\mathsf{\beta }}_{10}{\mathrm{CONC}}_{\mathrm{i},\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}\end{array}$$

(1)

To investigate the effect of board characteristics on the level of NPLs in the MENA region, we included three groups of variables in the econometric model. The first group relates to board characteristics. We used board size (BS), which was measured by the number of directors on the Board of Directors. The independent directors’ variable (IND) was measured by the % of independent directors on the Board of Directors. Duality (Dual) is a dummy variable that takes the value of 1 if the CEO is the Chairman of the Board and 0 otherwise. Board gender diversity (BGD) indicates female representation on the board and is measured as the percentage of women in the total number of directors. Board tenure (MAND) indicates the term length of the board of directors. Compensation is included in the econometric model as board remuneration, measured by the total compensation of directors in US dollars relative to total assets. Finally, we built a board characteristic index (CA_index), which is a composite index of board characteristics with values ranging from 0 to 1.

According to Mehmood et al. (2025), Hunjra et al. (2024), Ngamvilaikorn et al. (2024), and Kinateder et al. (2021), corporate governance characteristics such as board size, board independence, gender diversity, board tenure, and duality are key factors in reducing credit risk. In contrast, Srairi (2024) and Hakimi et al. (2023) find that duality increases NPLs. Additionally; Awad et al. (2024) reported that board size has a negative impact on bank stock performance. Regarding compensation, Cheng et al. (2015) and Fahlenbrach and Stulz (2011) found that excessive CEO compensation may lead to increased risk-taking and diminished bank performance.

The second category of variables includes bank-specific variables. We used bank size (SIZE), measured by the natural logarithm of the total assets of each bank. Bank capital (CAP) is measured using the equity-to-total assets ratio. Liquidity risk (LTD) is proxied by the loans-to-deposit ratio. Return on assets (ROA) serves as a measure of bank profitability, and non-interest income (NII) serves as a proxy for bank diversification (Bashir et al., 2017; Jenkins et al., 2021; Alnabulsi et al., 2022; Hakimi et al., 2023).

The third category of variables includes the macroeconomic conditions. We used the annual growth rate of the gross domestic product (GDP) and the inflation rate (INF) (Hakimi et al., 2023; Alnabulsi et al., 2023).

In the second model, we propose constructing a board of directors index (CA_index). To do this, we use only the characteristics that significantly explain the level of NPLs in our sample. In the literature, there are two main methods for normalization: statistical and empirical normalization. Statistical normalization converts indicators to a common scale with a mean of zero and a standard deviation of one. The zero average avoids presenting accumulation alterations, stopping differences in the means of the indicators. In this study, we followed empirical standardization. Following Hakimi et al. (2022), the index is constructed through two main steps. The first step is normalization using empirical normalization, which adjusts the data values within a fixed range, typically between 0 and 1. This step reduces the data’s dimensionality while preserving the most relevant information. We chose normalization because it allows us to standardize highly variable data on a uniform scale. It also ensures that each component contributes proportionately to the final index, thereby reducing the influence of outliers and skewed variables. This leads to a more balanced and robust index that better reflects the underlying patterns across heterogeneous data sources. VN_k is the normalized value of each board characteristic (CA) during period t, and min (CA) and max (CA) represent the minimum and maximum values of each characteristic, respectively;

$${VN}_k=\left({CA}_{i,t}-min {CA}_i\right)/\left( max {CA}_i-min {CA}_i\right)$$

(2)

The second step is the ponderation of each normalized variable by equal coefficients (equal to 1/N) to ensure that the influence of each variable is uniform. This approach is simpler, more practical, and fairer, facilitating a consistent analysis of the importance of the variables without bias and avoiding weighting biases. Once the CA_index is calculated, the resulting values range from 0 to 1. N is the number of board characteristics used to construct the CA_index.

$$\mathrm{CA}\_{\mathrm{index}}_{\mathrm{i}}={{\displaystyle \sum }}_{\mathrm{k}=1}^{\mathrm{N}}\left({\displaystyle \frac{{\mathrm{VN}}_{\mathrm{k}}}{\mathrm{N}}}\right)$$

(3)

The second model (Figure 2) to be tested is presented in Equation (2).

$$\begin{array}{c}{\mathrm{NPLS}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{NPLS}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\beta }}_2\mathsf{\Sigma }\mathrm{CA}\_{\mathrm{index}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{CAP}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{ROA}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{LTD}}_{\mathrm{i}}+{\mathsf{\beta }}_7{\mathrm{NII}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{GDP}}_{\mathrm{i}}+\\ {\mathsf{\beta }}_9{\mathrm{INF}}_{\mathrm{i}}+{\mathsf{\beta }}_{10}{\mathrm{CONC}}_{\mathrm{i},\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}\end{array}$$

(4)

Table 2. Definition and measurement of the variables.

Variables	Definitions	Measures
Dependent variable
NPLs	Non-performing loans	Non-performing loans to total loans ratio (%).
Board characteristics
BS	Board size	Total number of directors within a Board of Directors
IND	Independent directors	Proportion of independent directors on a Board of Directors
DUAL	Duality	Binary variable takes 1 if the CEO is the Chairman of the Board, and 0 otherwise.
BGD	Gender diversity	Women on the board as a percentage of the total number of directors.
MAND	Board tenure	The term length of a board of directors.
COMP	Compensation	Total compensation of directors in US dollars relative to total assets (%).
CA_index	Board characteristics index	Composite index of board characteristics with values ranging from 0 to 1. For more details on the construction of this index, see the explanation on page 9.
Control variables
SIZE	Bank size	The natural logarithm of the total assets of each bank
CAP	Capital	Equity to total assets (%).
LTD	Liquidity risk	Loan-to-deposit ratio, (%).
ROA	Bank performance	Return on assets (ROA), (%)
NII	Bank diversification	Non-interest income as a percentage of total assets.
CONC	Concentration	The share of the five biggest banks’ assets to all banks’ assets (%).
GDP	Economic growth	Annual GDP growth rate, (%)
INF	Inflation rate	Annual growth of Consumer index price, (%)

provides the definition of all the variable used in the econometric model.

4. Analysis and Results

In this section, we first examine the descriptive statistics of our variables, followed by a Pearson correlation test between them. Next, we discuss and evaluate the findings on the relationship board characteristics and NPLs in the MENA, GCC, and non-GCC regions.

4.1. Summary Statistics and Correlation Matrix

Table 3. Descriptive statistics.

	Mean	Std. Dev.	Min	Max
NPLs	7.3	1.8	4.01	261
BS	10.52	1.95	5.02	19
IND	28.81	23.48	13.1	100
BGD	6.62	8.41	1.4	40
MAND	3.10	0.59	1	6
COMP	0.01	0.03	0	0.18
SIZE	23.74	1.22	20.94	26.51
CAP	16.7	4.6	3.5	42.9
ROA	1.4	0.8	−3.8	6.3
LTD	98.76	5.84	1.4	162
NII	38.44	17.26	9.55	96
CONC	82.20	13.36	56.04	100
GDP	3.12	4.07	−2.4	19.59
INF	4.83	10.96	−3.75	29.5

provides the descriptive statistics of the variables. The average NPLs ratio is 7.3%, with a maximum value of 261% recorded by a Turkish bank in 2012 and a minimum value of 4% observed by a Saudi bank in 2011.

Statistics indicate that the average board size in the MENA region is 10.52 members. The largest recorded board consisted of 19 members at a Turkish bank in 2010, while a Tunisian bank had a much smaller board with only five members in the same year. In terms of board independence, 28.81% of board members are independent, with the highest proportion being 100%. The average percentage of women on boards is 6.62%, with a maximum of 40% female board members. The minimum proportion of independent members and women on the board is 0%. Board tenure varies from 1 to 6 years, with an average of 3.1 years. The mean ratio of board member compensation to total assets is 0.01%, with the highest value recorded at 0.18% and the lowest at zero.

The average bank size is 23.737, with a maximum value of 26.512 and a minimum value of 20.942. The mean value of bank capital is 16.7%, ranging from a maximum of 42.9% to a minimum of 3.5%. The highest value for bank performance (ROA) was 6.3%, and the lowest was −3.8%. The mean LTD ratio was 98.76%, with a maximum of 162% and a minimum of 1.4%. The average value for bank diversification (NII) is 38.44%, with a maximum of 96% and a minimum of 9.55%. The mean concentration was 82.197%, with a maximum of 100% and a minimum of 56.035%. As for the macroeconomic factors, the average values for GDP and inflation (INF) are 3.120% and 4.833%, respectively. The maximum values are 19.592% for GDP and 29.5% for inflation, while the minimum values are −2.4% and −3.749%.

Table 4. Descriptive statistics for dichotomous variables.

DUAL	Frequency	Percentage
0	599	87.79%
1	83	12.21%
Total	682	100%

presents the descriptive statistics for the dichotomous variable “Duality.” The majority of banks in the MENA region (87.79%) opt to separate the roles of the Chairman of the Board and CEO. In contrast, 12.21% of banks follow a policy of combining these roles, which contradicts the recommendations of the best governance practices that advocate for the separation of these functions.

Table 5. Correlation matrix.

	NPLs	BS	IND	DUAL	BGD	MAND	COMP	SIZE	CAP	ROA	LTD	NII	CONC	GDP	INF
NPLs	1.0000
BS	0.2801 *	1.0000
	(0.0000)
IND	−0.1264 *	−0.0729	1.0000
	(0.0023)	(0.0790)
DUAL	0.0562	0.1680 *	−0.0999 *	1.0000
	(0.1765)	(0.0000)	(0.0147)
BGD	0.2524 *	0.2375 *	−0.1397 *	−0.0833 *	1.0000
	(0.0000)	(0.0000)	(0.0007)	(0.0428)
MAND	−0.0235	0.0537	−0.1279 *	0.3443 *	−0.0853 *	1.0000
	(0.5765)	(0.2014)	(0.0020)	(0.0000)	(0.0407)
COMP	0.1480 *	−0.0474	−0.2020 *	0.1559 *	0.3829 *	−0.0748	1.0000
	(0.0111)	(0.4169)	(0.0005)	(0.0072)	(0.0000)	(0.2066)
SIZE	−0.1867 *	−0.0606	0.0904 *	0.1377 *	−0.3643 *	0.0912 *	−0.5102	1.0000
	(0.0000)	(0.1662)	(0.0376)	(0.0015)	(0.0000)	(0.0380)	(0.0000)
CAP	−0.1057 *	−0.0414	0.1254*	0.0712	−0.1474 *	0.2518 *	−0.3640 *	0.1943 *	1.0000
	(0.0023)	(0.3213)	(0.0024)	(0.0843)	(0.0004)	(0.0000)	(0.0000)	(0.0000
ROA	−0.1725 *	−0.0358	−0.0224	−0.0530	0.0695	−0.0307	0.1635 *	0.1648 *)	0.0823 *	1.0000
	(0.0000)	(0.3876)	(0.5854)	(0.1956)	(0.0910)	(0.4591)	(0.0048)	(0.0000	(0.0149)
LTD	−0.0520	0.0380	−0.0515	−0.0430	0.1046 *	−0.0191	−0.0184	−0.0411	0.0197	0.0950 *	1.0000
	(0.1298)	(0.3617)	(0.2136)	(0.2980)	(0.0116)	(0.6478)	(0.7524)	(0.2804)	(0.5680)	(0.0050)
NII	−0.0016	0.0723	−0.0744	0.0164	−0.1013 *	0.3225 *	−0.1536 *	0.3106 *	0.1425 *	−0.0791 *	0.0853 *	1.0000
	(0.9643)	(0.0814)	(0.0699)	(0.6902)	(0.0138)	(0.0000)	(0.0083)	(0.0000)	(0.0001)	(0.0256)	(0.0176)
CONC	−0.2255 *	−0.3393 *	0.2763 *	−0.1467 *	−0.4319 *	0.0805	−0.5140 *	0.2194 *	0.2336 *	−0.0099	−0.1181 *	−0.0298	1.0000
	(0.0000)	(0.0000)	(0.0000)	(0.0004)	(0.0000)	(0.0538)	(0.0000)	(0.0000)	(0.0000)	(0.7689)	(0.0006)	(0.4088)
GDP	−0.0666	0.1410 *	−0.0188	0.0578	0.0442	0.0737	- 0.0813	0.0757 *	0.0386	0.3077 *	0.0578	0.1127 *	−0.0360	1.0000
	(0.0521)	(0.0006)	(0.6469)	(0.1586)	(0.2834)	(0.0758)	(0.1632)	(0.0462)	(0.2547)	(0.0000)	(0.0885)	(0.0014)	(0.2879)
INF	0.0638	0.1093 *	0.0283	0.0279	0.1209 *	−0.2337 *	0.3666 *	0.0022	−0.0548	0.1216	0.0732 *	−0.0661	−0.2669 *	0.0281	1.0000
	(0.0625)	(0.0082)	(0.4898)	(0.4954)	(0.0032)	(0.0000)	(0.0000)	(0.9533)	(0.1054)	(0.0002)	(0.0309)	(0.0622)	(0.0000)	(0.3981)

* indicates significance at the 5% level.

displays the results of the correlation matrix for our variables. The Pearson correlation test shows that the coefficients are below 0.7 in absolute value (Kervin, 1992). Therefore, our model is free from multicollinearity.

4.2. Discussion of the Empirical Findings

The results displayed in

Table 6. The impact of board characteristics on NPLs in the MENA region.

	(M1) BS and NPLs		(M2) IND and NPLs		(M3) DUAL and NPLs		(M4) BGD and NPLs		(M5) MAND and NPLs		(M6) COMP and NPLs
	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z
NPLs (−1)	0.134	365.73 ***	0.157	299.01 ***	0.159	412.39 ***	0.154	452.94 ***	0.157	455.77 ***	0.151	5.38 ***
SIZE	−0.055	−19.72 ***	−0.047	−25.26 ***	−0.049	−24.43 ***	−0.052	−45.92 ***	−0.049	−42.24 ***	−0.029	−14.31 ***
CAP	−0.014	−3.43 ***	−0.016	−4.33 ***	−0.014	−4.16 ***	−0.015	−2.90 ***	−0.016	−5.60 ***	−0.106	−10.04 ***
ROA	−0.412	−5.77 ***	−0.612	−10.12 ***	−0.569	12.55 ***	−0.647	−10.22 ***	−0.849	−13.17 ***	−0.319	−7.17 ***
LTD	−0.0002	−0.30	0.001	0.39	−0.000	−0.35	−0.000	−0.37	−0.001	−0.62	−0.003	−2.48 **
NII	−0.003	−60.85 ***	−0.002	−42.31 ***	−0.002	−34.85 ***	−0.002	−53.72 ***	−0.002	−74.34 ***	−0.001	−6.15 ***
CONC	0.003	30.01 ***	0.001	34.61 ***	0.002	33.14 ***	0.002	49.53 ***	0.001	26.01 ***	0.002	16.63 ***
GDP	−0.002	−29.95 ***	−0.001	−28.82 ***	−0.001	−26.11 ***	−0.001	−31.49 ***	−0.002	−39.41 ***	−0.001	−20.39 ***
INF	−0.0004	−9.68 ***	−0.0003	−8.16 ***	−0.000	−9.96 ***	−0.000	−9.59 ***	−0.000	−3.66 ***	0.003	16.36 ***
BS	0.019	78.11 ***	-		-		-		-		-
IND	-		−0.0003	−13.38 ***	-		-		-		-
DUAL	-		-		0.006	1.87 *	-		-		-
BGD	-		-		-		−0.002	−35.54 ***	-		-
MAND	-		-		-		-		0.082	255.71 ***	-
COMP	-		-		-		-		-		−4.819	−0.72
_cons	1.035	14.59 ***	1.140	24.30 ***	1.190	22.79 ***	1.221	42.77 ***	0.971	29.56 ***	0.617	14.32 ***
AR(1)	−1.21		−1.1286		−1.124		−1.1217		−1.1271		−2.033
Prob	0.2263		0.2591		0.2610		0.2620		0.2597		0.0421
AR(2)	−1.4448		−1.3284		−1.2352		−1.6401		−1.1391		−0.09712
Prob	0.1485		0.1840		0.2168		0.1010		0.2546		0.9226
Sargan test	62.498		59.559		59.780		61.835		66.302		42.885
Prob	0.8671		0.9176		0.9143		0.8798		0.7788		0.9992
N	682		682		682		682		682		682

***, ** and * indicate level of significance at 1%, 5% and 10%.

are relative to the effect of board characteristics on NPLs in the MENA region. However,

Table 7. The impact of board characteristics on NPLs in the GCC countries.

	(M1) BS and NPLs		(M2) IND and NPLs		(M3) DUAL and NPLs		(M4) BGD and NPLs		(M5) MAND and NPLs		(M6) COMP and NPLs
	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z
NPLs (−1)	0.130	7.95 ***	0.146	340.69 ***	0.150	797.51 ***	0.149	682.04 ***	0.158	18.97 ***	.248	9.91 ***
SIZE	−0.013	−13.66 ***	−0.008	−4.70 ***	−0.010	−10.09 ***	−0.010	−6.68 ***	−0.008	−6.19 ***	−0.005	−2.66 ***
CAP	−0.114	−3.21 ***	−0.158	−5.17 ***	−0.124	−5.39 ***	−0.157	−6.81 ***	−0.142	−8.71 ***	0.049	2.24 **
ROA	0.138	0.61	0.231	2.03 **	0.019	0.25	0.095	1.03	−0.311	−4.45 ***	0.160	3.13 ***
LTD	−0.00007	(0.07)	−0.0004	0.31	−0.001	−0.43	−0.001	−0.79	−0.001	−0.82	2.37	0.03
NII	−0.002	−43.14 ***	−0.002	−29.65 ***	−0.002	−71.64 ***	−0.002	−33.32 ***	−0.002	−65.17 ***	−0.00006	−1.58
CONC	0.005	23.99 ***	0.003	20.92 ***	0.003	22.86 ***	0.003	32.54 ***	0.003	34.78 ***	−0.0003	−2.89 ***
GDP	−0.002	−31.02 ***	−0.002	−29.40 ***	−0.002	−32.48 ***	−0.002	−26.91 ***	−0.002	−54.82 ***	−0.0005	−6.52 ***
INF	−0.001	−6.10 ***	−0.001	−9.60 ***	−0.001	−17.17 ***	−0.001	−12.21 ***	−0.0004	−12.16 ***	−0.0003	−16.04 ***
BS	0.020	69.00 ***	--		-		-		-		-
IND	-		−0.0003	−11.86 ***	-		-		-		-
DUAL	-		-		0.016	2.19 **	-		-		-
BGD	-		--		-		−0.0001	−2.99 ***	-		-
MAND	-		-		-		--		0.087	173.37 ***	-
COMP	-		-		-		-		--		−16.867	−2.50 **
_cons	−0.128	−4.01 ***	0.093	1.94 *	0.125	4.22 ***	0.111	2.99 ***	−0.171	−4.72	0.191	3.34 ***
AR(1)	−1.1294		−1.0873		1.0893		−1.0897		−1.1601		−2.18
Prob	0.2587		0.2769		0.2760		0.2759		0.2460		0.0293
AR(2)	−1.2813		−1.4329		−1.2058		−1.281		−1.106		−1.2647
Prob	0.2001		0.1519		0.2279		0.2002		0.2687		0.2060
Sargan test	46.192		44.385		46.099		49.552		49.875		31.191
Prob	0.9973		0.9986		0.9974		0.9919		0.9911		1.0000
N	436		436		436		436		436		436

***, ** and * indicate level of significance at 1%, 5% and 10%.

and

Table 8. The impact of board characteristics on NPLs in non-GCC countries.

	(M1) BS and NPLs		(M2) IND and NPLs		(M3) DUAL and NPLs		(M4) BGD and NPLs		(M5) MAND and NPLs		(M6) COMP and NPLs
	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z	Coef.	Z
NPLs (−1)	0.130	1.90 *	0.171	5.28 ***	0.186	4.34 ***	0.120	2.56 **	0.160	3.69 ***	0.110	50.47 ***
SIZE	−0.029	−1.68 *	−0.024	−6.10 ***	−0.018	−1.97 **	−0.035	−3.92 ***	−0.014	−2.99 ***	−0.022	−1.93 *
CAP	−0.005	−0.87	−0.013	−1.40	−0.006	−0.59	−0.020	−1.65 *	−0.004	−3.26 ***	−0.002	−0.36
ROA	0.004	0.01	−0.801	−0.97	−1.004	−1.67 *	−0.255	−0.59	−1.649	−2.32 **	0.474	0.54
LTD	0.001	0.26	−0.001	0.32	0.0008	0.00	−0.0004	−0.25	−0.001	−0.33	0.00006	0.26
NII	−0.005	−16.15 ***	−0.003	−8.69 ***	−0.002	−5.36 ***	−0.003	−5.02 ***	−0.004	−7.87 ***	−0.003	−5.36 ***
CONC	0.004	6.01 ***	0.003	3.74 ***	0.002	4.95 ***	0.003	4.92 ***	0.002	5.31 ***	0.004	5.65 ***
GDP	−0.003	−5.63 ***	−0.003	−7.93 ***	−0.003	−8.30 ***	−0.003	−11.96 ***	−0.003	−7.96 ***	−0.003	−9.92 ***
INF	−0.001	−1.85 *	−0.0003	−0.98	0. 00009	0.28	−0.001	−1.82 *	0.0002	0.93	−0.001	−2.64 ***
BS	0.026	12.80 ***	-		-		-		-		-
IND	-		−0.0004	−2.27 **	-		-		-		-
DUAL	-		-		0.005	0.69	-		-		-
BGD	-		-		-		−0.003	−16.93 ***	-		-
MAND	-		-		-		-		0.064	44.72 ***	-
COMP	-		-		-		-		-		−17.897	−1.71 *
_cons	0.364	0.87	0.593	5.08 ***	0.434	2.00 **	0.817	3.74 ***	0.215	1.83	0.473	1.64
AR(1)	−1.2841		−1.1358		−1.2184		−1.0025		−1.5868		−1.0848
Prob	0.1991		0.2561		0.2231		0.3161		0.1126		0.2780
AR(2)	−0.94766		−0.94337		−1.3721		−1.2485		−0.98756		−0.79825
Prob	0.3433		0.3455		0.1700		0.2118		0.3234		0.4247
Sargan test	18.290		21.813		13.879		21.457		16.603		16.929
Prob	1.0000		1.0000		1.0000		1.0000		1.0000		1.0000
N	292		292		292		292		292		292

***, ** and * Level of significance at 1%, 5% and 10%.

compare the results of this relationship in the GCC and non-GCC. Finding showed that both the Sargan test and the autocorrelation test did not reject the null hypothesis, confirming the validity of the over-identification restrictions and the absence of correlation. The p-values for both the Sargan test and AR (2) exceeded 5%. The lagged dependent variable (NPLs (−1)) has a positive and significant effect at the 1% level on credit risk in the MENA region, as well as in the GCC and non-GCC countries. This suggests that the volume of NPLs from the previous year has a significant impact on those of the current year.

Board size has a positive and significant effect at the 1% level for the MENA region, GCC countries, and non-GCC countries. These findings show that a 1% increase in board members increases the NPL rate by 1.9% in the MENA region, 2% in GCC banks, and 2.6% in non-GCC banks. Theoretically, agency theory suggests that larger boards may suffer from coordination problems and weaker monitoring due to diluted accountability. Inefficient communication and slower decision-making can impair timely responses to credit risks. These issues may lead to lax oversight of lending practices, increasing the likelihood of non-performing loans. Furthermore, a large number of directors may dilute individual responsibility, reducing the effectiveness of supervision and risk management. The diversity of opinions and interests on an extended board may also hinder consensus-building, leading to divergences that make it difficult to develop coherent strategies for managing credit risks. Our results are consistent with those of Tarchouna et al. (2021) and Doğan and Ekşi (2020). The positive sign observed for both regions confirms hypothesis H1.

Board independence has a negative coefficient with a statistically significant effect of 1% in the MENA region and GCC countries, while it is significant at 5% for non-GCC banks. Thus, a 1% increase in the number of independent directors reduces the level of NPLs by 0.03% in these two regions and by 0.04% in non-GCC countries. The agency theory and resource dependence theory underpin these findings. Independent directors enhance board objectivity and oversight, reducing agency conflicts and improving the monitoring of lending decisions. Their external perspective and expertise strengthen risk assessment, leading to lower non-performing loans through more prudent credit management. In addition, the presence of independent directors promotes transparency in decision-making processes since they are less likely to be influenced by internal conflicts of interest. More specifically, the recruitment of independent directors provides new financial skills and knowledge, allowing for a more rigorous and critical evaluation of credit requests. Their independence from internal and external pressures helps in the quick identification of risky loan portfolios and the recommendation of necessary adjustments to prevent potential losses. These results corroborate those of Switzer and Wang (2013) and Pathan (2009). Therefore, we accept hypothesis H2.

CEO duality has a positive and significant impact on NPLs. Specifically, a 1% increase in CEO duality leads to a 0.6% increase in NPLs in the MENA region. Likewise, a 1% increase in the DUAL variable results in a 1.6% increase in NPLs for banks in the GCC. However, no significant relationship is found between CEO duality and NPLs in non-GCC banks.

These findings align with agency theory, which argues that CEO duality weakens the board’s ability to monitor management effectively due to the concentration of power. This lack of oversight can lead to self-serving decisions, reduced transparency, and increased credit risk. The absence of checks and balances undermines risk control, increasing the likelihood of non-performing loans. Additionally, combining the CEO and chair positions may limit the diversity of viewpoints, undermine internal control, and diminish independent monitoring. As a result, the decision-making process becomes less susceptible to critique and review, potentially concealing issues and heightening credit risk. These findings align with those of Abid et al. (2021) and J. Lu and Boateng (2018). Hence, we accept the hypothesis H3.

The results regarding gender diversity reveal a negative and significant impact at the 1% level for banks in the MENA, GCC, and non-GCC regions. A 1% increase in the number of women on a board leads to a notable reduction in NPLs by 0.2% for the MENA region, 0.01% for GCC countries, and 0.3% for non-GCC banks. These findings are supported by the resource dependence theory, which suggests that gender-diverse boards benefit from broader perspectives, improved decision-making, and enhanced monitoring. Women often exhibit risk-averse behavior and collaborative skills that contribute to more prudent credit assessments. This reduces agency problems and leads to lower levels of non-performing loans. Furthermore, the presence of women on boards promotes more balanced decision-making as women are often more detail-oriented and adopt a more cautious financial approach. Additionally, they bring valuable skills, such as empathy, innovation, improved communication, and enhanced relationships with other board members. Female directors also contribute to the diversity of perspectives within credit teams, allowing for a better understanding and a more balanced evaluation of credit applications, thereby reducing the levels of NPLs. Our findings support those of Kinateder et al. (2021) and J. Lu and Boateng (2018). Based on these results, we accept H4.

The length of board tenure shows a positive and statistically significant association at the 1% level. As such, a 1% increase in board tenure results in an 8.2% increase in the NPLs ratio for banks in the MENA region, 8.7% for GCC banks, and 6.4% for non-GCC banks. Extending board tenure allows board members to build closer and more lasting relationships with a bank’s stakeholders, which may influence their decisions regarding loan management and control. As a result, administrators may become less rigorous in applying risk management procedures, thereby weakening loan control and increasing the likelihood of default. Moreover, a longer mandate can lead to more subjective and personal decisions, resulting in less stringent management of credit processes. These findings confirm the results of Kinateder et al. (2021) and support H5, which suggests that longer tenure increases NPL levels.

Regarding director compensation, the results show a statistically significant negative relationship between the compensation variable (COMP) and NPLs at the 5% level for GCC countries and at the 10% level for non-GCC banks. However, compensation has no significant impact on NPLs for banks in the MENA region. A higher performance-based compensation can incentivize board members to engage more actively in prudent management of credit risk. Indeed, substantial compensation encourages board members to implement rigorous oversight policies and establish robust risk management processes. Moreover, higher compensation improves the financial position of directors and may also reduce conflicts of interest. As a result, directors are less likely to take excessive risks or adopt strategies that might generate quick profits but jeopardize long-term financial stability. Our findings contradict those of J. Lu and Boateng (2018) and Rose (2017). Therefore, we reject hypothesis H6.

The size of the bank significantly reduces NPLs at the 1% level in the MENA region, GCC countries, and non-GCC countries. Larger banks are generally more diversified and equipped with advanced technologies, skilled human resources, and expertise in risk management, which enables them to better control credit risk. These results are consistent with those reported by Jenkins et al. (2021).

Moreover, capital has a negative and significant effect on NPLs in the MENA region, GCC countries, and non-GCC countries. This finding aligns with moral hazard theory and the capital buffer theory, which suggest that well-capitalized banks are more cautious in their lending, as they have more to lose in the event of default. Higher capital levels enhance financial stability and absorb potential losses, thereby encouraging stricter credit screening. The results show that stronger capital positions are associated with lower non-performing loans. This result supports the findings of Naili and Lahrichi (2020), who observed that banks with excess capital tend to avoid NPLs in order to protect their capital reserves, which are usually mobilized in the event of significant risks.

Bank performance shows a negative and significant relationship with NPLs in the MENA region, GCC countries (M5), and non-GCC countries (M3 and M5). This indicates that high-performing banks are less likely to grant risky loans. This finding is consistent with the efficiency-risk hypothesis, which posits that well-performing banks have better risk management systems and more efficient operations, reducing the likelihood of poor loan decisions. Higher profitability allows banks to invest in better credit assessment tools and staff. Consequently, strong performance is linked to lower levels of non-performing loans. This result confirms the findings from the previous chapter and aligns with the studies of Jenkins et al. (2021) and Bashir et al. (2017).

Regarding the effect of banking diversification, the results reveal a negative and significant relationship at the 1% level across all three groups: MENA, GCC, and non-GCC countries. This suggests that diversified banks focus more on maximizing non-interest income than on taking excessive risks. This finding is consistent with Ghosh (2017).

The variable CONC shows a positive and significant impact at the 1% level on NPLs for banks in the MENA region, GCC, and non-GCC countries. Banks with higher market concentrations receive more credit applications, making them more flexible in managing these requests. In contrast, concentration has a negative and significant effect at the 1% level (M6) on NPLs for GCC banks. This result is consistent with the findings of Hakimi and Khemiri (2024), who observed that a more concentrated banking sector is associated with lower levels of NPLs.

Regarding macroeconomic conditions, GDP growth and inflation rates have statistically significant negative effects at the 1% level on NPLs in the MENA region, GCC countries, and non-GCC countries. A favorable macroeconomic environment, characterized by strong growth and low inflation, enhances borrowers’ ability to repay their loans by improving their financial situation and increasing their solvency. These results align with the conclusions of Jabbouri et al. (2022), who found that improved economic conditions are associated with decreased credit risk.

The results show that all board characteristics significantly influence NPLs. As a result, we will construct a board index (CA_index) that incorporates key characteristics such as board size, duality, board independence, gender diversity, director compensation, and board tenure. This index allows us to better understand the combined impact of these characteristics on credit risk. The results are presented in the

Table 9. The impact of board characteristics index on NPLs.

	MENA		GCC		Non-GCC
	Coef.	Z	Coef.	Z	Coef.	Z
NPLs (−1)	0.156	313.48 ***	0.151	11.28 ***	0.104	2.28 **
SIZE	−0.064	−47.50 ***	−0.028	−15.77 ***	−0.029	−3.03 ***
CAP	−0.017	−8.75 ***	−0.198	−12.12 ***	−0.017	−1.76 *
ROA	−0.787	−12.53 ***	−0.074	−1.75 *	−0.781	−2.26 **
LTD	−0.002	1.82	−0.001	−0.68	−0.00001	−0.01
NII	−0.002	−67.39 ***	−0.001	−27.27 ***	−0.003	−5.84 ***
CONC	0.002	43.96 **	0.003	32.00 ***	0.002	4.37 ***
GDP	−0.001	−39.08 ***	−0.001	−16.56 ***	−0.003	−4.49 ***
INF	−0.000	−5.09 ***	−0.001	−16.82 ***	−0.000	−2.07 **
CA_index	−0.136	−19.67 ***	−0.094	−8.20 ***	−0.130	−3.82 ***
_cons	1.476	40.63 ***	0.495	10.21 ***	0.709	3.21 ***
AR(1)	−1.1312		−1.1043		−1.9127
Prob	0.2580		0.2694		0.0558
AR(2)	−1.6633		−1.3959		−1.3593
Prob	0.0962		0.1627		0.1741
Sargan test	63.481		47.206		17.022
Prob	0.8467		0.9961		1.0000
N	682		436		292

***, ** and * indicate the 1%, 5% and 10% significance lelvels.

below. Moreover, the board of directors index (CA_index) has a negative and statistically significant impact on NPLs. In the MENA region, GCC countries, and non-GCC countries, a 1% increase in the index coefficient reduces NPL levels by 13.6%, 9.4%, and 13%, respectively. These results suggest that banks with effective board characteristics are more likely to reduce their NPL levels. Additionally, a robust board structure enables board members to enhance their skills and perspectives, thereby enriching decision-making and improving the oversight of management processes. Board members of higher quality are better equipped to develop risk management strategies that are responsive to economic changes. Furthermore, such a board promotes transparency and responsibility within the organization, facilitating timely detection and resolution of potential credit issues. Our findings contradict those of Srairi et al. (2021), who argued that Islamic banks with strong governance are associated with higher risk-taking.

5. Conclusions

This research significantly contributes to the debate on bank credit risk, particularly the relationship between board characteristics and credit risk. We used data from 70 banks across 12 countries in the MENA region from 2010 to 2022 and applied the SGMM. Furthermore, we performed a comparative analysis between the sub-regions of MENA: GCC and non-GCC countries.

The empirical results reveal that in the MENA region and GCC countries, a larger board size, CEO duality, and longer tenure significantly increase the volume of NPLs. Conversely, an increase in the number of independent directors, proportion of female directors, and board compensation significantly reduces NPLs. Regarding non-GCC countries, board size and tenure are positively associated with higher NPLs. In contrast, board independence, gender diversity, and compensation significantly reduce the NPLs ratio. Additionally, the findings show that as board quality improves, the NPL ratio decreases across all three regions: MENA, GCC and non-GCC. These results allow us to conclude that an effective board of directors is a key factor in mitigating credit risks.

This study presents several policy implications, highlighting the necessity of prioritizing the effectiveness of boards and their essential role in the control and supervision of banking activities. Policymakers, regulators, and banks should focus on fostering board independence and diversity, as these factors can significantly mitigate credit risks. Indeed, adopting sound governance practices helps reduce insolvency risks and address information asymmetry. Consequently, effective governance strengthens the banking sector’s stability, enhancing financial resilience. Finally, policymakers and bankers are invited to implement clear accountability mechanisms and performance evaluations for boards to ensure that they play an active role in monitoring loan portfolios and enforcing sound lending practices, ultimately reducing NPL levels across banks in the MENA region.

While the results of this study are interesting, it has some limitations. First, we were unable to examine this relationship across all countries in the MENA region due to a lack of available data. Second, including additional board characteristics, such as board culture and specific features of the audit committee, could have enriched our study. Future research could be conducted using a comparative analysis based on the level of corruption or board size to provide valuable insights and open new perspectives. Furthermore, it is crucial to examine whether a threshold effect exists in the relationship between board characteristics and NPLs in the MENA region.