Market and Liquidity Risk Management in Banking: Can Intellectual Capital Promote Sustainable Performance?

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Ayomikun Elizabeth ADUWO, Mustapha Bojuwon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9488067/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study explores the connections between market risk management and liquidity risk management and social performance and corporate governance performance, and how intellectual capital in the form of human, structural and relational can mediate these relations of the Nigerian deposit money banks (DMBs). Banks that have higher intellectual capital use more complex risk governance systems and communication systems between stakeholders, and thus, increase the sustainability dividend of risk management practices. Using a comprehensive sample of 12 Nigerian Exchange (NGX) listed deposit money banks over the period 2015–2024, we document four main findings. First, market risk management affects social performance positively although this is statistically insignificant. Second, liquidity risk management is not a significant factor in corporate governance performance in the baseline specification. Thirdly, social performance has a positive and independent relationship with intellectual capital. Fourth and most importantly, intellectual capital moderates the liquidity risk management governance performance relationship in a positive way meaning that the governance returns to liquidity risk management is only achieved when banks have sufficient intellectual capital resources. The most powerful control predictor in all the models is the bank size, and the robustness tests with alternative constructions of the variable Net Stable Funding Ratio-based liquidity and System Generalized Method of Moments estimations confirm the baseline conclusions. This paper can be added to the developing literature on market risk management, liquidity risk management and social performance and corporate performance. Altogether, the research provides new and policy-oriented information on the knowledge based underpinnings of sustainable banking in Nigeria and proves that intellectual capital is an important but frequently neglected facilitator of risk sustainability nexus. Market risk management Liquidity risk management Social performance Corporate governance performance Intellectual capital 1. Introduction The persistent macroeconomic instability, the constant shift of regulations, and the increase of stakeholder demands have radically transformed the standards by which the deposit money banks (DMBs) in Nigeria are judged. Financial performance is no longer the only metric used to evaluate banks; today, the banks are also evaluated based on their capacity to behave responsibly, maintain high governance levels, and act in the best interest of various stakeholders’ dimensions combined under the notion of sustainable performance (Oluwaseyi, 2024 ; Ahmad et al., 2023 ). This trend is in line with the global patterns, with the mainstreaming of environmental, social, and governance (ESG) models making corporate sustainability less of a discretionary objective and more of a strategic necessity (Daugaard & Ding, 2022 ). The banking sector is especially vital in the sustainability challenges in Nigeria, where economic intermediation and financial inclusion are implemented through a channel characterized by structural vulnerabilities, thin interbank markets, currency volatility, and a continuous cycle of regulatory adjustments (Falade et al., 2024 ; Sanusi, 2024 ). In this respect, market risk management and liquidity risk management are at the forefront in determining the sustainability outcomes of banks. Market risk that is caused by changes in interest rates, exchange rates, equity prices, and commodity prices may directly limit the capability of a bank to honor social commitments to employees, customers, and communities (Siddique et al., 2021 ; Onyegiri et al., 2024 ). The liquidity risk, which is caused by the existence of the asset liability mismatch inherent to the banking business, has long been the trigger of the operational crisis undermining the corporate governance framework and compromising investor confidence (Dountimiarye et al., 2024 ; Islatince, 2024 ). The combination of these two types of risk is a dual risk axis that is a major influence in determining sustainable performance paths of Nigerian deposit money banks. This paper looks at two important non-financial aspects of sustainable performance. The employee compensation ratio is used to measure social performance, which is the capacity of a bank to generate beneficial effects on stakeholders (e.g., employees, customers, and local communities) (Gleibner et al., 2022 ; Maali et al., 2021 ). Corporate governance performance, reflecting internal accountability and the board’s capacity for effective deliberation, is proxied by the frequency of board meetings (Radu et al., 2022 ; Hettiarachchi & Sameera, 2024 ). The selection of these dimensions is based on the fact that they reflect social and governance pillars of the ESG framework and their drivers in the banking sector of Nigeria are not thoroughly studied as the financial performance outcomes (Achimugu et al., 2021 ; Ademola & Ismailia, 2022 ). One of the most important contributions of the research is the fact that intellectual capital is regarded as a mediator. Intellectual capital that encompasses human capital efficiency, structural capital efficiency and capital employed efficiency is operationalized with Value Added Intellectual Coefficient (VAIC™) (Pulic, 1998 ) and has emerged as a critical intangible capital in the knowledge-based economy. Intellectual capital is the enabling infrastructure in the relationship of risk management sustainability whereby risk management practices are transformed into material social and governance outputs. Banks that have better human capital are able to have more risk models and risk analysis at the board level. Individuals that possess well-established structural capital have information systems that assist them to respond to liquidity strains promptly and efficiently. In the meantime, high-relational capital banks help to build the trust towards the stakeholders required to translate the risk management work into quantifiable social performance outcomes (Faedfar et al., 2022 ; Sayed & Nefzi, 2024 ). The intellectual capital moderating role of risk management in its association with sustainable outcomes has not been much studied in the banking sector of the Nigerian context, which underlines the originality and practical value of this study. Traditional financial indicators such as return on assets (ROA) and return on equity (ROE) have been the main focus of the Nigerian banking literature, with the social and governance elements of sustainability being under-researched (Achimugu et al., 2021 ; Ademola and Ismailia, 2022 ). Although some studies have investigated the impact of market risk management (Agbana et al., 2024 ; Onyegiri et al., 2024 ) and liquidity risk management (Ofeimun & Okeke, 2019 ; Bashir and Umar, 2022 ), there are scant few studies that have studied both risks simultaneously in a framework that takes into account non-financial outcomes Better still, the impact of intellectual capital on the way risk management is converted into improved social and governance results has been given minimal attention. That is, we are aware that risk practices are important, however, we are yet to learn when and how the practices are to enhance better social and governance performance within the Nigerian banks. This provides little direction to both policymakers and bank managers on how to leverage intellectual capital in addition to risk management in order to attain sustainable performance. Combined, these gaps indicate a definite opportunity: studying both market risk management and liquidity risk management, as well as the moderating role of intellectual capital, this study may offer a more comprehensive view of sustainability performance in Nigerian deposit money banks. The sample we examine is a set of twelve deposit money banks in Nigeria during the years 2015 to 2024 with a total of 120 firm-years. The research takes into consideration the market risk management, liquidity risk management, social performance, corporate governance performance, intellectual capital, and the key control variables such as bank size, leverage, and growth opportunity. Based on the data of the Nigerian Exchange Group, we note that there is a significant variability among banks and over time in both social and governance performance indicators. This paper report that in the baseline model, the direct relationships between market and liquidity risk management and sustainable performance are positive but statistically insignificant. This indicates that risk management practices are not always associated with more robust social or governance results. The most striking result, is the important moderating effectiveness of intellectual capital in the association between liquidity risk management and corporate governance performance. This implies that the governance advantages of liquidity risk management are achieved mainly when the banks have a better structural and relational capital which enables boards to process, analyze and act on the liquidity information in an effective manner. In general, the findings indicate that the relationship between risk management and sustainable performance of Nigerian banks is conditional upon the existence of intellectual capital, which offers the organizational ability to transform the risk management actions into practical social and governance results. In order to make sure that our results are strong and not motivated by certain measurement decisions, we performed a set of strong tests. To begin with, we substituted the employee compensation ratio with employment intensity index (total staff/total assets) as an alternative measurement of social performance. The findings indicate that the impacts of market risk management and intellectual capital are still positive and of equal strength which means that our original results on market risk management and social performance are also resistant to other proxies. Second, we have adopted the Net Stable Funding Ratio (NSFR) as an alternative to Liquidity Coverage Ratio to assess the liquidity risk. The relationship between liquidity risk management and intellectual capital is positive and statistically significant indicating that the moderating role of intellectual capital on the liquidity risk governance performance relationship is not limited to the type of liquidity measure employed. Third, we dealt with the issue of possible reverse causality, in which case, the increase in governance may prompt banks to invest more in intellectual capital. We again re-estimated all the interaction models, and the coefficients were directionally similar between specifications, which reassured us that there was no concern about endogeneity bias. Fourth, we limited the sample to the period after 2018, that is, the time after the introduction of the revised CBN corporate governance guidelines. The results of the interaction were in line with the entire sample finding both direction and significance, which confirms the temporal strength of our findings. On the whole, these strong checks support the finding that intellectual capital is a boundary condition, which improves the risk management effectiveness in sustainable social and governance performance in Nigerian banks. In four broad manners, our study fills existing gaps. First, we consider market and liquidity risk management in the same framework of non-financial sustainability with a unified outcome and result (unlike earlier studies in banking, which tend to concentrate on one type of risk and one outcome) (Ofeimun & Okeke, 2019 ; Gleibner et al., 2022 ; Ishmail et al., 2023 ; Onyegiri et al., Second, we disaggregate sustainable performance into both its social and governance aspects, offering more nuanced information than those studies that use composite ESG scores or solely use financial indicators. Third, we present intellectual capital as a moderator and also provide the first systematic empirical studies regarding the effect of human, structural and relational knowledge resources of the Nigerian banks on the sustainability returns of risk management practices. Fourth, we use the Panel-Corrected Standard Errors (PCSE) estimator during 2015 to 2024, which includes post-recession recovery, digital banking transformation, the COVID-19 pandemic, and post-pandemic consolidation. This increases the rigor of the methodology used, as well as, the relevance of our findings in the present. The rest of the research will be organized in the following way. Part 2 will examine the theoretical background and formulate the hypotheses. The data and the methodology are outlined in Section 3 . Empirical results are given in Section 4 and a conclusion given in Section 5 with policy implications and future research suggestions. 2. Literature Review 2.1 Theoretical Background The paper develops a theoretical justification of the relationships being studied by suggesting that proper risk management enables the financial sustainability to maintain social performance as well as the participation in governance. Intellectual capital also enhances the above relationships by facilitating the conversion of risk management practices into quantifiable sustainable results by banks. This study is centered by the Resource-Based View (RBV) (Barney, 1991 ). According to RBV, competitive advantage is created by valuable, rare, inimitable, and non-substitutable (VRIN) resources. Risk management capabilities and intellectual capital are such resources in the context of the Nigerian deposit money banks. They are ingrained in organizational practices, human capabilities, and knowledge systems, and are hard to imitate, and they are essential in producing high quality social and governance performance (Salvi et al., 2020 ; Pereira & Bamel, 2021 ). In addition to RBV, Stakeholder Theory (Freeman, 1984 ) highlights the normative ground of social and governance performance. When banks are keen to balance the requirements of different stakeholders such as employees, customers, communities, creditors, and regulators, they generate value that goes beyond the conventional financial measures. Lastly, the Dynamic Capabilities perspective (Teece et al., 1997 ) unites these views and emphasizes that the capacity of banks that have intellectual capital to re-structure internal resources according to evolving risk environments. This ability allows for social and governance performance even when the market or liquidity is under stress (Malkah & Nandiroh, 2024 ; Sofia, 2021). Combined, these frameworks underscore the anticipation that intellectual capital mediates the association between risk management and non-financial sustainability results, which improves the capability of banks in transforming risk management practices into concrete social and governance advantage. 2.2 Market Risk Management and Social Performance. Market risk is a probability of incurring financial losses due to undesirable changes in market variables like interest rates, exchange rates, equity prices, and commodity prices. Market risk management in this paper is measured by the Value-at-Risk (VaR) measure at the 95th percentile level that represents the largest possible loss in normal market operations (Gupta et al., 2021 ; Valaskova et al., 2017 ). The connection between market risk management and social performance may be viewed in terms of a few related processes. On the one hand, proper management of market risk leads to financial stability as it minimizes the volatility of earnings and maintains capital buffers. Banks will have fewer chances to incur unforeseen losses that drain resources when they are able to predict and deal with market exposures effectively. This stability provides financial slack time which enables banks to undertake discretionary investments in social activities like employee welfare programs, community projects, and other socially responsible activities. In this regard, well-managed companies are more likely to be able to maintain or improve their social performance (Gleibner et al., 2022 ; Nirino et al., 2022 ). Conversely, the unstable market situation can compel financially strained banks to reduce non-core spending, such as social programs, in the context of the short-term capital-saving measures (Sandberg et al., 2022 ). This tension brings out the fact that net impact of market risk management on social performance is determined by the overall risk capacity and strategic priorities of the bank. Based on this, legitimacy theory indicates that the market pressure can in fact lead to increased social engagement by firms in response to the need to retain stakeholder trust and institutional credibility. Banks can build stronger relationships with employees, customers, and the rest of the community by showing that they uphold their social commitments in challenging times, thereby protecting their reputational capital in the long term (Palma-Ruiz et al., 2020 ). Indirect but significant support of these positive paths can be found in empirical evidence of the Nigerian banking situation. To illustrate this, Agbana et al. ( 2024 ) discover that proper market risk management has a major positive effect on the profitability of banks, whereas Onyegiri et al. ( 2024 ) demonstrate that diversification strategy can help reduce the negative influence of market risk on the profitability of banks. Though the studies are on financial performance, their results suggest that a bank that has a robust market risk system has more ability to meet wider obligations such as social obligations. This view is further reinforced by the moderating effect of intellectual capital. Banks that possess superior intellectual capital especially those that possess sophisticated risk modelling and possess well established internal knowledge systems are in a better position to understand complex risk signals and also effectively communicate the same to stakeholders. This increased ability will help in the quality of risk decision making as well as disclosures about risk and help these banks be able to transform their market risk management practices into social performance results. Previous studies indicate that intellectual capital facilitates innovation, both organizational resilience and stakeholder engagement that result in high-quality social performance (Rislanudeen, 2022 ; Ali et al., 2021 ). Combined, these theoretical and empirical findings imply that market risk management not only contributes to social performance, but this impact is enhanced when a bank has a robust pool of intellectual capital. Accordingly, we propose: Hypothesis 1 Market risk management is positively associated to the social performance of the Nigerian DMBs. Hypothesis 2 There is a positive moderating effect of intellectual capital between market risk management and social performance of Nigerian DMBs. 2.3 Liquidity risk Management and Corporate Governance Performance. Liquidity risk occurs when a bank cannot fulfill the short-term commitments without using substantial expenses (Dountimiarye et al., 2024 ). This is especially acute in Nigeria, where the interbank market is not yet very deep, which puts deposit money banks (DMBs) at risk of increased liquidity shocks. To this effect, the Central Bank of Nigeria (CBN) has implemented Basel III-related regulatory standards, such as the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR), to enhance the practices in terms of liquidity risk management (Ishmail et al., 2023 ). Theoretically, good management of liquidity risk involves good governance and presence of board participation. Such liquidity management functions as cash flow forecasting, liquidity buffer, and stress testing require on-going monitoring and board-level strategic oversight. Similar to Musa et al. ( 2022 ), the greater the involvement of the board, the higher the quality of governance due to better monitoring and decision making. On the same note, Hettiarachchi and Sameera ( 2024 ) highlight that the risk management practices can enhance board activity especially in financial institutions where risk management is of utmost importance. This relationship is further evidenced by empirical evidence. Ofeimun et al. (2019) reveal that good liquidity management practices have a significant impact on the performance of banks in Nigeria, but the governance mechanisms have a significant indirect role. Similarly, Bashir and Umar ( 2022 ) state that the better the liquidity position of banks, the better the governance results, indicating that liquidity discipline is the support to effective and accountable boards. In addition to the direct connection, intellectual capital serves a mediating role, by making the governance processes more effective. Structural capital including strong information systems, internal controls and risk reporting structures allows flow of information to the board in time and in a correct manner, which enhances oversight functions. According to Sofia (2021), companies that have a higher structural capital can more easily convert the operations practices into governance outcomes. Similarly, Wang and Juo ( 2021 ) show that intellectual capital enhances the quality of decisions and organizational efficiency by increasing knowledge of processing information in companies. Here, a bank that boasts of a greater intellectual capital will find it easier to transform its liquidity risk management activities into better governance performance. The availability of sophisticated systems and knowledge infrastructure enables board members to interact more efficiently with issues of liquidity, increasing the benefits of liquidity management practices on governance. We therefore propose: Hypothesis 3 Liquidity risks management has a positive relationship with corporate governance performance of Nigerian DMBs. Hypothesis 4 Intellectual capital is positively moderated by the relationship between liquidity risk management and corporate governance performance of Nigerian DMBs. 3. Method 3.1 Sample Twelve DMBs that were actively traded on the Nigerian Exchange Group (NGX) during the years 2015 to 2024 were included in the sample, namely, Access Bank, Eco Bank Nigeria, Fidelity Bank, First Bank of Nigeria, FCMB, Guaranty Trust Bank, Sterling Bank, Union Bank of Nigeria, UBA, Unity Bank, Wema Bank, and Zenith Bank. The criteria were that the sample must have been listed on NGX throughout the entire sample period and that both audited annual report and sustainability disclosures are available and that the sample must represent a substantial portion of total assets in the Nigerian banking sector, approximated at 85%. The final balanced panel includes 120 firm-year observations, including post-recession recovery (2015 to 2016), digital banking transformation (2017 to 2019), COVID-19 disruption (2020 to 2021), and post-pandemic consolidation (2022 to 2024). 3.2 Model Specification There are four specified regression models, two baseline and two moderation-augmented one, each with a dependent variable. The PCSE estimator is used here to address the panel specific heteroscedasticity and the serial autocorrelation as identified in the pre-estimation diagnostics. The baseline regression model for the MRM–social performance relationship is: \(\:\text{S}\text{C}\text{P}\text{ᵢ}\text{ₜ}\:=\:{\pi\:}₀\:+\:{\pi\:}₁\text{M}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₂\text{I}\text{C}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₃\text{F}\text{S}\text{Z}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₄\text{L}\text{E}\text{V}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₅\text{G}\text{R}\text{O}\text{ᵢ}\text{ₜ}\:+\:{\epsilon\:}\text{ᵢ}\text{ₜ}\:··\) · (1) The moderation-augmented model is: $$\:\:\text{S}\text{C}\text{P}\text{ᵢ}\text{ₜ}\:={\pi\:}₀\:+\:{\pi\:}₁\text{M}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₂\text{I}\text{C}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₃(\text{M}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:\times\:\:\text{I}\text{C}\text{ᵢ}\text{ₜ})\:+\:{\pi\:}₄\text{F}\text{S}\text{Z}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₅\text{L}\text{E}\text{V}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₆\text{G}\text{R}\text{O}\text{ᵢ}\text{ₜ}\:+\:{\epsilon\:}\text{ᵢ}\text{ₜ}\:···\:\left(2\right)$$ The analogous baseline and moderation models for the LRM–corporate governance performance relationship are: $$\:\text{C}\text{G}\text{P}\text{ᵢ}\text{ₜ}\:={{\pi\:}}^{0}+\:{{\pi\:}}^{1}\text{L}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:+\:{{\pi\:}}^{2}\text{I}\text{C}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₃\text{F}\text{S}\text{Z}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₄\text{L}\text{E}\text{V}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₅\text{G}\text{R}\text{O}\text{ᵢ}\text{ₜ}\:+\:{\epsilon\:}\text{ᵢ}\text{ₜ}\:···\:\left(3\right)$$ $$\:\:\text{C}\text{G}\text{P}\text{ᵢ}\text{ₜ}\:=\:{\pi\:}₀\:+\:{\pi\:}₁\text{L}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₂\text{I}\text{C}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₃(\text{L}\text{R}\text{M}\text{ᵢ}\text{ₜ}\:\times\:\:\text{I}\text{C}\text{ᵢ}\text{ₜ})\:+\:{\pi\:}₄\text{F}\text{S}\text{Z}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₅\text{L}\text{E}\text{V}\text{ᵢ}\text{ₜ}\:+\:{\pi\:}₆\text{G}\text{R}\text{O}\text{ᵢ}\text{ₜ}\:+\:{\epsilon\:}\text{ᵢ}\text{ₜ}\:···\:\left(4\right)$$ Where: SCP represents social performance, measured as the employee compensation ratio; CGP denotes corporate governance performance, proxied by the number of board meetings. MRM refers to market risk management, measured using Value-at-Risk (VaR), while LRM represents liquidity risk management, measured using the liquidity coverage ratio (LCR). IC captures intellectual capital, measured using the VAIC™ model. FSZ denotes bank size, computed as the natural logarithm of total assets. LEV represents leverage, measured as the ratio of total debt to total equity. GRO represents growth opportunity and is measured as the annual growth rate in total assets, calculated as: total asset t – total asset t− 1 /total asset t− 1 × 100. The parameters π₀ represent the constant term, while π₁–π₆ are the regression coefficients. The term ε i ₜ denotes the idiosyncratic error term. A positive and statistically significant coefficient of the interaction term (π₃ > 0) in Models 2 and 4 indicates that intellectual capital strengthens (or enhances) the effect of market risk management and liquidity risk management on firm performance. Standard errors are clustered at the firm level to control for potential heteroscedasticity and serial correlation within firms over time. 3.3 Variable Operationalization The definitions and measurement of all the variables in this research are provided in Table 1 . The dependent variables are social performance (SCP) which is represented as the ratio of total employee pay to total operating costs (Cek & Ercantun, 2023 ; Gleibner et al., 2022 ), and corporate governance performance (CGP) which is proxied by the number of board meetings per year (Radu et al., 2022 ; Musa et al., 2022 ). The primary independent variables are market risk management (MRM) (Value-at-Risk (VaR) at 95th percentile confidence level) and liquidity risk management (LRM) (Liquidity Coverage Ratio (LCR)) (division of high-quality liquid assets by net 30-day cash outflows). Intellectual capital (IC) is characterized in terms of VAIC framework (Pulic, 1998 ; Rislanudeen, 2022 ), which is a mixture of efficiency in human capital (HCE = VA/HC), structural capital efficiency (SCE = SC/VA), and capital employed efficiency (CEE = VA/CE). Three control variables (bank size, FSZ; leverage, LEV; and growth opportunity, GRO) are also included in the study, in accordance with (Falade et al. 2024 ). The reason behind the selection of these is that they present the most influential contextual factors influencing the risk management, as well as the sustainable performance. Bank size, which is the resource capacity, leverage, which is the exposure to financial risks, and the growth opportunity which is the potential to expand all have a direct relationship to the social and governance results. These three control variables are chosen because they allow the model to be simple and focused at the same time considering the most important factors that may have an impact on the results. Lastly, all the ratio variables are winsorized at the 1 percent level at both ends to decrease the impact of the extreme values and enhance robustness. Table 1 below presents the operationalization of all variables used in this study. Table 1 Variable operationalization Variable Symbol Measurement Source A priori sign Dependent Variables Social Performance SCP Total employee compensation / Total operating expenses × 100 Annual Reports of Listed Banks (NGX); — Corporate Governance Performance CGP Number of board meetings held annually Annual Reports of Listed Banks (NGX) — Independent Variables Market Risk Management MRM Portfolio VaR at 95th percentile confidence level Annual Reports of Listed Banks (NGX); + Liquidity Risk Management LRM High-quality liquid assets / Net 30-day cash outflows (LCR) Annual Reports of Listed Banks (NGX); + Moderating Variable Intellectual Capital IC VAIC™ = HCE + SCE + CEE; HCE = VA/HC; SCE = SC/VA; CEE = VA/CE Annual Reports of Listed Banks (NGX); + Control Variables Bank Size FSZ Natural logarithm of total assets Annual Reports of Listed Banks (NGX); + Leverage LEV Total liabilities / Total equity Annual Reports of Listed Banks (NGX); ± Growth Opportunity GRO (Total Assetsₜ − Total Assetsₜ₋₁) / Total Assetsₜ₋₁ Annual Reports of Listed Banks (NGX); + Note: The definition of all variables is based on standard practice in the Nigerian banking literature. VaR= Value-at-Risk; LCR= Liquidity Coverage Ratio; VAIC= Value-Added Intellectual Coefficient; VA= Value Added; HC= Human Capital; SC= Structural Capital; CE= Capital Employed. Source: Authors' Compilation (2026). 4. Results 4.1 Descriptive Statistics The summary statistics, Pearson correlation matrix, and collinearity diagnostics of the baseline regression variables are given in Table 2 . The average social performance of 32.64% shows that on average, the sample banks spent about a third of operating expenditure on employee compensation indicating moderate labour intensity within the industry. The average CGP of 8.42 board meetings annually is within the governance framework of the CBN, which provides four quarterly board meetings and a maximum of 16 board meetings, which indicates that the board is active. The average VaR of 0.84% represents a moderate level of portfolio market risk exposure, whereas the average LCR, 142.7% is significantly higher than the regulatory minimum of 100%, suggesting that the sector has a surplus of high quality liquid assets. The average VAIC 3.18 implies a medium level of IC performance, which is in line with previous findings in Sub-Saharan African banking environments. All the variance inflation factors (VIFs) are less than 5.0, which proves the missing undesirable multicollinearity among the independent variables. Table 2 Below provides the descriptive statistics and collinearity diagnostics for all variables. Descriptive statistics Variable Mean Std. Dev. Min. Max. Skewness VIF Social Performance – SCP (%) 32.64 8.43 14.28 54.72 0.31 — Corp. Gov. Performance – CGP (meetings) 8.42 2.18 4.00 16.00 0.74 — Market Risk – MRM (VaR, %) 0.84 0.42 0.12 2.18 1.23 2.14 Liquidity Risk – LRM (LCR, %) 142.7 38.64 81.2 284.3 0.87 2.08 Intellectual Capital – VAIC™ 3.18 1.08 0.72 5.84 0.65 2.41 Bank Size – FSZ (Log TA) 21.48 1.31 18.86 24.41 −0.18 3.12 Leverage – LEV (D/E) 6.84 2.73 1.42 14.62 0.91 2.68 Growth Opportunity – GRO (%) 8.74 12.41 −18.3 48.6 0.84 1.87 This table presents the distribution of variables. VIF = variance inflation factor. All variables are defined in Table 1 Source: Authors' Compilation (2026). 4.2 Market Risk Management and Social Performance Table 3 states the regression results of PCSE of the market risk management social performance models. In the first Model 1, the market risk management and social performance are positively related but not significantly (b = 0.0015). The positive directionality is in line with the financial resilience mechanism in which profitable, well-hedged banks have more capacity to spend socially but the effect does not reach the statistical significance level at more traditional levels indicating that the market risk management social performance might be too weak to be able to discern the sample. Bank size (b = 1.252) is the strongest predictor of social performance, which aligns with the resource munificence argument, which states that bigger banks have both the motivation and ability to invest in the welfare of employees and the community (Falade et al., 2024 ). The social performance variation is explained by the baseline model is 89.25%. R² = 0.8925). The hypothesis 1 is not supported. The moderation-augmented Model 2 has a positive but non-significant MRM x VAIC™ interaction (b = 0.017), which does not support Hypothesis 2 . IC (VAIC™) a positive relationship exist between the independent predictor of social performance (b = 0.324). This observation implies that Intellectual capital has been linked to the social performance payoffs of talent management systems, the culture of stakeholder communication and knowledge sharing and not by increasing the social performance payoffs of market risk management. The fact that the interaction between MRM and IC was not significant means that the mechanism through which market risk management is linked to social performance does not run through the mediation of intellectual capital, but rather, it seems that intellectual capital has a direct effect on social performance. The mean value of the marginal effect of intellectual capital on social performance is 0.324 percentage points per unit increase in VAIC™. The social performance (adjusted) can be explained by Model 2 (91.24%). R² = 0.9088). Hypothesis 2 is not supported. Table 3 Market risk management and social performance Variable Model 1 β (z-stat) p-value Model 2 β (z-stat) p-value Market Risk Management 0.0015(0.12) 0.905 0.0042(0.31) 0.759 Intellectual Capital (VAIC™) — — 0.3241 (4.18)*** 0.000 MRM × VAIC™ — — 0.0170 (0.76) 0.445 Bank Size (FSZ) 1.2522 (12.55)*** 0.000 1.1843 (11.27)*** 0.000 Leverage (LEV) 4.0632 (1.01) 0.314 3.8714 (0.97) 0.333 Growth Opportunity (GRO) 0.0017 (0.31) 0.755 0.0019 (0.34) 0.731 Constant −31.866 (− 12.05)*** 0.000 −29.412 (− 11.18)*** 0.000 R-squared 0.8956 — 0.9124 — Adj. R-squared 0.8925 — 0.9088 — Wald χ² 214.04*** 0.000 248.37*** 0.000 Observations 120 Dependent variable: Social Performance (Employee Compensation Ratio, %). *** p < 0.01; ** p < 0.05; * p < 0.10. z-statistics in parentheses. Source: Authors' Compilation (2026). 4.3 Liquidity Risk Management and Corporate Governance Performance. The results of the regression of PCSE in the models of liquidity risk management corporate governance are as in Table 4 . In the baseline Model 3, LRM has a positive but statistically insignificant direct effect on corporate governance performance (b = 0.0015). The governance predictor that is dominant (b = 0.0926) is bank size and growth opportunity has a negative and significant impact (b = -0.0066). The negative growth opportunity coefficient is also in line with the agency cost-of-governance hypothesis (Lehn et al., 2009), which states that a rapidly growing bank should shift managerial bandwidth towards investment activity, which limits intentional board-level governance activity. The baseline model accounts 75.96 per cent of the variation in corporate governance performance (adj.). R² = 0.7524). The most important discovery of this paper in Model 4. The addition of the intellectual capital interaction term gives a positive and statistically significant value of the liquidity risk management (b = 0.240), which supports Hypothesis 3 , and means that the liquidity risk management governance relationship is achieved when banks have sufficient intellectual capital resources to translate liquidity risk governance into formal board level procedures. More importantly, the interaction term between LRM and VAIC is positive and statistically significant (b = 0.566) which confirms Hypothesis 4 . What this observation suggests is that banks that have better VAICs scores reap much higher returns on governance performance investments in liquidity risk management: better human capital allows boards to do more complex analysis on the liquidity risk reports, better structural capital enables boards to adopt the governance information systems that enable boards to respond more effectively to liquidity pressures, and better relational capital provides the institutional environment where credible liquidity governance decisions are made and acted on. IC has an independent and a significant positive impact on the performance of governance (b = 0.703). Economically, a one standard deviation shifts in VAIC (1.08) enhances the liquidity risk management governance impact, by about 0.61 more board meetings per year (= 0.566 x 1.08). Model 4, which suggests that highly leveraged banks are limited in the nature of their interactions at the board of directors’ level because of their debt-service pressures and creditors-demand oversight, shows that leverage is negatively and significantly related to governance performance (b = − 57.21). Table 4 Liquidity risk management and corporate governance performance Variable Model 3 β (z-stat) p-value Model 4 β (z-stat) p-value Liquidity Risk Management 0.0015 (0.29) 0.775 0.2396 (2.20)** 0.027 Intellectual Capital (VAIC™) — — 0.7025 (2.49)** 0.013 LRM × VAIC™ (Interaction) — — 0.5662 (2.25)** 0.024 Bank Size (FSZ) 0.0926 (5.30)*** 0.000 −1.9495 (− 1.45) 0.147 Leverage (LEV) 1.0182 (0.87) 0.387 −57.213 (− 3.12)*** 0.002 Growth Opportunity (GRO) −0.0066 (− 3.13)*** 0.002 0.0431 (0.43) 0.664 Constant −2.537 (− 1.80)* 0.072 17.786 (0.68) 0.499 R-squared 0.7596 — 0.5091 — Adj. R-squared 0.7524 — 0.4669 — Wald χ² 42.84*** 0.000 61.44*** 0.000 Observations 120 Dependent variable: Corporate Governance Performance (Board Meeting Frequency). *** p < 0.01; ** p < 0.05; * p < 0.10. z-statistics in parentheses. Source: Authors' Computation (2026). 4.4 Robustness Tests We conduct several additional analyses to validate and reinforce the main findings. Specifically, this study performs robustness checks by: the results are summarised in Table 5 below. Table 5 Robustness test results Variable Alternative Social Proxy (Model 5) NSFR Liquidity (Model 6) System GMM (Model 7) Post-2018 Sample (Model 8) Market Risk Management (MRM) 0.0031 (0.28) — — — Liquidity Risk Management (LRM) — 0.2184 (2.04)** 0.2261 (2.11)** 0.2475 (2.09)** Intellectual Capital (VAIC™) 0.2874 (3.96)*** 0.6812 (2.36)** 0.6553 (2.29)** 0.7198 (2.41)** Interaction Term 0.0142 (0.69) 0.5123 (2.16)** 0.5417 (2.18)** 0.5731 (2.21)** Bank Size (FSZ) 1.1035 (10.88)*** −1.7741 (− 1.39) −1.6935 (− 1.32) −1.8427 (− 1.36) Leverage (LEV) 3.5421 (0.91) −52.846 (− 2.97)*** −49.732 (− 2.81)*** −55.918 (− 2.95)*** Growth Opportunity (GRO) 0.0021 (0.37) 0.0395 (0.40) 0.0378 (0.39) 0.0417 (0.42) Observations 120 120 120 72 Source: Authors' Compilation (2026). The first is that we use an employment intensity index (total staff/total assets) in place of the employee compensation ratio as an alternative social performance proxy. The market risk management coefficient and the intellectual capital coefficient maintain their sign and approximate value, which proves that the findings of the baseline market risk management social performance are not a result of the chosen proxy. We replace NSFR by LCR as the measure of liquidity risk and the LRM × VAIC™ interaction is positive and significant (b = 0.512, p = 0.031) which confirms that the intellectual capital moderating effect on the liquidity risk management governance performance nexus is robust to the alternative operationalization of the liquidity risk. Third, to deal with issue of reverse causality in this case that as governance performance improves, banks will invest more in intellectual capital we re-estimate all interaction models with system-GMM estimator. All specifications result in directionally consistent coefficients, and so concerns over endogeneity bias are alleviated. Fourth, the sample is limited to the post-2018 sub-period, which estimates the updated CBN corporate governance principles, which creates interaction effects consistent in direction and statistically with the full sample findings, which demonstrate temporal robustness. All these tests lead to inferences that intellectual capital constitutes a boundary condition of the sustainable performance relationship of risk management in the banking sector in Nigeria. The System-GMM estimation results that address endogeneity concerns are presented in Table 6 below. Table 6 System-GMM estimation results (endogeneity robustness check) Variable Social Performance Model (GMM) β (z-stat) p-value Governance Performance Model (GMM) β (z-stat) p-value Lagged Dependent Variable 0.621 (5.84)*** 0.000 0.487 (4.92)*** 0.000 Market Risk Management (MRM) 0.0021 (0.19) 0.849 — — Liquidity Risk Management (LRM) — — 0.2261 (2.11)** 0.035 Intellectual Capital (VAIC™) 0.3015 (3.77)*** 0.000 0.6812 (2.36)** 0.018 MRM × VAIC™ 0.0142 (0.68) 0.497 — — LRM × VAIC™ — — 0.5417 (2.18)** 0.029 Bank Size (FSZ) 1.1032 (9.44)*** 0.000 −1.7821 (− 1.39) 0.164 Leverage (LEV) 3.5521 (0.89) 0.373 −49.672 (− 2.88)*** 0.004 Growth Opportunity (GRO) 0.0015 (0.28) 0.779 0.0382 (0.39) 0.695 Constant −25.114 (− 9.27)*** 0.000 14.552 (0.59) 0.556 Diagnostic Tests Value AR(1) p-value 0.000 AR(2) p-value 0.217 Hansen Test (p-value) 0.412 Number of Instruments 28 Observations 120 Note: *** p < 0.01; ** p < 0.05; * p < 0.10. z-statistics in parentheses. AR (2) tests for second-order serial correlation; Hansen test for instrument validity. Source: Authors’ Computation (2026). 4.5 Interpretation of System-GMM Results: Table 6 provides the System-GMM estimates that consider endogeneity, dynamic persistence, and the unobserved heterogeneity. The lagged dependent variable is positive and significantly high in both models, which attests good persistence in both social and governance performance and justifies the dynamic specification. In line with the baseline findings, market risk management is positively although not significantly correlated with explaining social performance and its interaction with intellectual capital (VAIC™) is also non-significant. This once again confirms the lack of both direct and moderating effect, thus not supporting H1 and H2. In contrast, liquidity risk management (LRM) retains a positive and statistically significant effect on corporate governance performance. More to the point, the interaction term (LRM x VAIC™) is still positive and significant, which proves that intellectual capital boosts the returns to liquidity risk management governance. These results are resistant to endogeneity and support H3 and H4. The VAIC coefficient is positive and significant, which implies that intellectual capital positively and independently enhances social and governance performance. Of the controls, bank size is only important in the social performance model, and leverage has a negative and significant impact on governance performance, which is in line with the arguments of financial constraint. Model invalidity is tested by diagnostic tests. The Arellano-Bond AR (2) test does not reject the null of no second-order correlation in serial, whereas the Hansen test is a sign that there is instrument validity. The number of instruments is within reasonable limits, which alleviates worries of overfitting. In general, the System-GMM estimates are consistent with the PCSE results, and it was confirmed that the results are endogeneity-robust. The results always indicate that MRM is not determinant of social performance, but LRM, under the condition of intellectual capital, positively influences corporate governance performance. 5. Discussions and Implications 5.1 General Discussion This study indicates that the direct impacts of both market risk management and liquidity risk management on sustainable performance are positive but not significant in the model specification with the baseline, which indicates that the risk management practices do not reveal a significant statistical relationship between the social and corporate governance performance. The results also indicate that the risk-sustainability connection in the Nigerian DMBs is conditional depending on the presence of the IC resources, which become the organizational infrastructure in which the abilities of risk management are transformed into the performance outcomes that are sustainable. The positive and significant correlation between intellectual capital and social performance model specifications aligns with the organizational learning and stakeholder management literature which postulates that human capital especially talent management systems, training investment and knowledge sharing culture is the main force behind the attainment of employee welfare (Faedfar et al., 2022 ; Sayed and Nefzi, 2024 ). The fact that the market risk management × VAIC™ interaction is not significant in determining social performance implies that intellectual capital is correlated with its own social performance payoffs, but does not directly enhance the social payoffs of market risk management, perhaps because of institutional-level indirect mechanisms. This study has found that the positive and significant relationship between governance performance of liquidity risk management and intellectual capital moderation (b = 0.566, p = 0.024) is positive and significant. This finding suggests that the governance advantages of liquidity risk management depend on the structural capital endowments that facilitate the boards in processing, analyzing, and responding to liquidity information. Banks that have poor governance information systems, as well as, limited risk literacy at the board level, are unlikely to translate even good liquidity risk management practices into quantifiable board engagement outcome a finding that has direct implications on the design of governance capacity building programme in the Nigerian banking. The agency cost hypothesis, which states that a high level of leverage limits discretionary board actions by enhancing creditor oversight and changing managerial focus towards debt-service commitments, can be attributed to the negative and significant leverage effect on governance performance in Model 4 (b = -57.21, p = 0.002). The robustness tests, reported in Section 4.4 , verify the stability of the baseline results. The major direction and importance of interaction effects remain apparent in other proxy variables of liquidity risk measures and estimation strategies, which is similar to the international evidence on the influence of institutional quality and cultural context in determining ESG outcome (Liang and Renneboog, 2017 ; Mooneeapen et al., 2022 ). 5.2 Implications in Practices and Research There are a number of significant implications of the results of this research to the management of banks, policymakers and researchers. To bank managers, the findings also remind them that the investments in intellectual capital in terms of employee training, governance information systems, and relationship capital can be the enabling infrastructure whereby sound liquidity risk management can be translated into quantifiable governance performance. Such risk management frameworks, which are not supported by sufficient organizational knowledge, are unlikely to realize their full governance potential, no matter how sophisticated the framework is. An important practical implication is that banks must invest both in technical aspects of risk management, and in human and structural capital endowments that allow boards to use such investments to good effect. To Central Bank of Nigeria and the regulators, the data shows that new corporate governance principles would clearly state that intellectual capital enablers are specifically human capital development and governance technology infrastructure as supplementary pillars of quality governance, in addition to traditional risk measures. Introducing the requirements of the disclosure of the intellectual capital into the Revised Corporate Governance Guidelines would help supervisors, investors, and ESG rating agencies to evaluate the intellectual capital governance nexus systematically and to distinguish between those banks with the highest governance score that might indicate a real organizational capacity and those with the highest score that might indicate superficial compliance. The negative moderating impact of leverage on the performance of governance further justifies macro prudential advice that discourages excessive leverage among systemically important banks. To researchers, this research highlights some of the promising areas of future research. To start with, the framework can be expanded to cover the environmental aspect of ESG performance and offer a more comprehensive view of the intellectual capital risk sustainability nexus. Second, one may use panel threshold models to test the non-linearity of the moderating effect of intellectual capital. In particular, whether there exists a minimum threshold of intellectual capital beneath which the risk management is not yielding any governance returns. Third, a comparative West African banking panel could be subjected to the model to determine how the findings can be generalized to the region. Fourth, an analysis of VAIC components (HCE, SCE, CEE) would enable examining which dimensions of ICs are the most critical in moderating each risk sustainability pathway. Lastly, more enriching disclosure information would allow the researchers to use other intellectual capital proxies such as board human capital indices or governance technology spending ratios to triangulate the VAIC -based results. 6. Conclusion Increasing scholarly research has explored the aspects and consequences of risk management in corporate sustainability and non-financial performance. This study had a balanced panel of twelve NGX-listed Nigeria deposit money banks between the years 2015 to 2024 (n = 120 firm-year observations) to study the effect of market risk management and liquidity risk management on the social and corporate governance aspects of sustainable performance with intellectual capital as an intermediate variable. Previous studies have indicated that proper risk management gives the financial stability needed to maintain social and governance promises, whereas intellectual capital as an organizational knowledge deposit, structural systems and relational networks preconditions the degree of this association. In this framework, intellectual capital endowments are predicted to enhance the sustainability returns on risk management. Consistent with this prediction, the results show that liquidity risk management is positively and significantly associated with corporate governance performance only in the presence of adequate intellectual capital and that intellectual capital positively and significantly moderates the liquidity risk management–governance relationship. Intellectual capital has a positive and significant relationship with social performance, whereas the relationship between market risk management and liquidity risk management and social and governance performance, respectively, are insignificant. The main conclusions are proven by robustness tests with alternative proxies, estimators, and sub-period tests. Despite the fact that this study contributes to the knowledge in the area of intellectual capital sustainability nexus in Nigerian banking, a number of limitations should be mentioned. Although the VAIC™ model is popular, its operationalization of intellectual capital is partial, failing to capture relational capital or knowledge assets unique to governance. The sample is limited to listed deposit money banks which might dissimilarly differ with non-listed or smaller banks in a manner that limits external validity. To mitigate the limitations of this study, future studies ought to involve the use of other measures of intellectual capital, increase the sample frame, and conduct tests of the model in more cross-country contexts. Declarations Ethical Approval and Consent to Participate This study is based exclusively on secondary data drawn from publicly available annual reports and sustainability disclosures of Nigerian Deposit Money Banks listed on the Nigerian Exchange Group (NGX). No human participants were directly involved in data collection; therefore, formal ethical approval and consent to participate are not applicable. Consent for Publication Not applicable. Funding The authors declare that no funding was received for this research. Author Contribution Aduwo Ayomikun Elizabeth: Conceptualisation, literature review, theoretical framework development (Resource-Based View, Stakeholder Theory, and Dynamic Capabilities), hypothesis formulation, data collection from annual reports of the 12 NGX-listed deposit money banks, variable operationalisation, writing of the original draft, and editing of the final manuscript.Bojuwon Mustapha: Research methodology design, model specification (PCSE and System-GMM estimation), statistical analysis and interpretation of results, robustness testing (alternative variable measures, endogeneity checks, temporal stability tests), supervision, validation of empirical findings, and critical review and revision of the manuscript.Both authors jointly contributed to the discussion of findings, policy implications, conclusion, and approved the final version of the manuscript for submission Data Availability nil References Achimugu, A., Ocheni, S. I., Abuh, A., Adediran, S. A., & Abdullahi, S. 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Credit risk measurement using VaR methodology. Advances in Applied Economics Research, 289–302. Wang, C. H., & Juo, W. J. (2021). An environmental policy of green intellectual capital: Green innovation strategy for performance sustainability. Business Strategy and the Environment, 30(7), 3241–3254. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9488067","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637057904,"identity":"6b6a04fa-d901-4ee8-963d-9c0dbae96b43","order_by":0,"name":"Ayomikun Elizabeth ADUWO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLCChANgEogrGBgMSNRyhlgtDDAtjG1EaOGfkXx0w4Mzh/P423PMJD7OOyxvzt58gOFHxTacWiRupKXdSLhxuFjizBszyZnbDhvu7DmWwNhz5jZua27kmN1I+HA4sQHIuM277TDjhhs5BsyMbbi1yMO0zAdrmXPYnqAWA7CWG4cTN4C1NIAZ+LUYnnkG9MuZ9MSNZ56V/5xxLD15w5ljCQfx+UXuePKxmz+OWSfOO5682eBDjbXthuPNBx/8qMDjfYEEFG4zmDyAWz0Q8KNK1+FVPApGwSgYBSMTAAC97Gt0H3iVdAAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University","correspondingAuthor":true,"prefix":"","firstName":"Ayomikun","middleName":"Elizabeth","lastName":"ADUWO","suffix":""},{"id":637057905,"identity":"fc1f21f1-d510-4976-a98e-ed3aaa58c2f5","order_by":1,"name":"Mustapha Bojuwon","email":"","orcid":"","institution":"Federal University","correspondingAuthor":false,"prefix":"","firstName":"Mustapha","middleName":"","lastName":"Bojuwon","suffix":""}],"badges":[],"createdAt":"2026-04-21 19:09:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9488067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9488067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109270599,"identity":"ba8a3f66-a082-426c-8f57-5dc1c27e51d2","added_by":"auto","created_at":"2026-05-14 13:40:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":387200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9488067/v1/057fb356-7d9f-4e76-a5a0-421c6b3622c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Market and Liquidity Risk Management in Banking: Can Intellectual Capital Promote Sustainable Performance?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe persistent macroeconomic instability, the constant shift of regulations, and the increase of stakeholder demands have radically transformed the standards by which the deposit money banks (DMBs) in Nigeria are judged. Financial performance is no longer the only metric used to evaluate banks; today, the banks are also evaluated based on their capacity to behave responsibly, maintain high governance levels, and act in the best interest of various stakeholders\u0026rsquo; dimensions combined under the notion of sustainable performance (Oluwaseyi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This trend is in line with the global patterns, with the mainstreaming of environmental, social, and governance (ESG) models making corporate sustainability less of a discretionary objective and more of a strategic necessity (Daugaard \u0026amp; Ding, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe banking sector is especially vital in the sustainability challenges in Nigeria, where economic intermediation and financial inclusion are implemented through a channel characterized by structural vulnerabilities, thin interbank markets, currency volatility, and a continuous cycle of regulatory adjustments (Falade et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanusi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this respect, market risk management and liquidity risk management are at the forefront in determining the sustainability outcomes of banks. Market risk that is caused by changes in interest rates, exchange rates, equity prices, and commodity prices may directly limit the capability of a bank to honor social commitments to employees, customers, and communities (Siddique et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Onyegiri et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The liquidity risk, which is caused by the existence of the asset liability mismatch inherent to the banking business, has long been the trigger of the operational crisis undermining the corporate governance framework and compromising investor confidence (Dountimiarye et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Islatince, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The combination of these two types of risk is a dual risk axis that is a major influence in determining sustainable performance paths of Nigerian deposit money banks.\u003c/p\u003e \u003cp\u003eThis paper looks at two important non-financial aspects of sustainable performance. The employee compensation ratio is used to measure social performance, which is the capacity of a bank to generate beneficial effects on stakeholders (e.g., employees, customers, and local communities) (Gleibner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Corporate governance performance, reflecting internal accountability and the board\u0026rsquo;s capacity for effective deliberation, is proxied by the frequency of board meetings (Radu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hettiarachchi \u0026amp; Sameera, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The selection of these dimensions is based on the fact that they reflect social and governance pillars of the ESG framework and their drivers in the banking sector of Nigeria are not thoroughly studied as the financial performance outcomes (Achimugu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ademola \u0026amp; Ismailia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most important contributions of the research is the fact that intellectual capital is regarded as a mediator. Intellectual capital that encompasses human capital efficiency, structural capital efficiency and capital employed efficiency is operationalized with Value Added Intellectual Coefficient (VAIC\u0026trade;) (Pulic, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and has emerged as a critical intangible capital in the knowledge-based economy. Intellectual capital is the enabling infrastructure in the relationship of risk management sustainability whereby risk management practices are transformed into material social and governance outputs. Banks that have better human capital are able to have more risk models and risk analysis at the board level. Individuals that possess well-established structural capital have information systems that assist them to respond to liquidity strains promptly and efficiently. In the meantime, high-relational capital banks help to build the trust towards the stakeholders required to translate the risk management work into quantifiable social performance outcomes (Faedfar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sayed \u0026amp; Nefzi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The intellectual capital moderating role of risk management in its association with sustainable outcomes has not been much studied in the banking sector of the Nigerian context, which underlines the originality and practical value of this study.\u003c/p\u003e \u003cp\u003eTraditional financial indicators such as return on assets (ROA) and return on equity (ROE) have been the main focus of the Nigerian banking literature, with the social and governance elements of sustainability being under-researched (Achimugu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ademola and Ismailia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although some studies have investigated the impact of market risk management (Agbana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Onyegiri et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and liquidity risk management (Ofeimun \u0026amp; Okeke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bashir and Umar, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there are scant few studies that have studied both risks simultaneously in a framework that takes into account non-financial outcomes Better still, the impact of intellectual capital on the way risk management is converted into improved social and governance results has been given minimal attention. That is, we are aware that risk practices are important, however, we are yet to learn when and how the practices are to enhance better social and governance performance within the Nigerian banks. This provides little direction to both policymakers and bank managers on how to leverage intellectual capital in addition to risk management in order to attain sustainable performance. Combined, these gaps indicate a definite opportunity: studying both market risk management and liquidity risk management, as well as the moderating role of intellectual capital, this study may offer a more comprehensive view of sustainability performance in Nigerian deposit money banks.\u003c/p\u003e \u003cp\u003eThe sample we examine is a set of twelve deposit money banks in Nigeria during the years 2015 to 2024 with a total of 120 firm-years. The research takes into consideration the market risk management, liquidity risk management, social performance, corporate governance performance, intellectual capital, and the key control variables such as bank size, leverage, and growth opportunity. Based on the data of the Nigerian Exchange Group, we note that there is a significant variability among banks and over time in both social and governance performance indicators. This paper report that in the baseline model, the direct relationships between market and liquidity risk management and sustainable performance are positive but statistically insignificant. This indicates that risk management practices are not always associated with more robust social or governance results. The most striking result, is the important moderating effectiveness of intellectual capital in the association between liquidity risk management and corporate governance performance. This implies that the governance advantages of liquidity risk management are achieved mainly when the banks have a better structural and relational capital which enables boards to process, analyze and act on the liquidity information in an effective manner. In general, the findings indicate that the relationship between risk management and sustainable performance of Nigerian banks is conditional upon the existence of intellectual capital, which offers the organizational ability to transform the risk management actions into practical social and governance results.\u003c/p\u003e \u003cp\u003eIn order to make sure that our results are strong and not motivated by certain measurement decisions, we performed a set of strong tests. To begin with, we substituted the employee compensation ratio with employment intensity index (total staff/total assets) as an alternative measurement of social performance. The findings indicate that the impacts of market risk management and intellectual capital are still positive and of equal strength which means that our original results on market risk management and social performance are also resistant to other proxies. Second, we have adopted the Net Stable Funding Ratio (NSFR) as an alternative to Liquidity Coverage Ratio to assess the liquidity risk. The relationship between liquidity risk management and intellectual capital is positive and statistically significant indicating that the moderating role of intellectual capital on the liquidity risk governance performance relationship is not limited to the type of liquidity measure employed. Third, we dealt with the issue of possible reverse causality, in which case, the increase in governance may prompt banks to invest more in intellectual capital. We again re-estimated all the interaction models, and the coefficients were directionally similar between specifications, which reassured us that there was no concern about endogeneity bias. Fourth, we limited the sample to the period after 2018, that is, the time after the introduction of the revised CBN corporate governance guidelines. The results of the interaction were in line with the entire sample finding both direction and significance, which confirms the temporal strength of our findings. On the whole, these strong checks support the finding that intellectual capital is a boundary condition, which improves the risk management effectiveness in sustainable social and governance performance in Nigerian banks.\u003c/p\u003e \u003cp\u003eIn four broad manners, our study fills existing gaps. First, we consider market and liquidity risk management in the same framework of non-financial sustainability with a unified outcome and result (unlike earlier studies in banking, which tend to concentrate on one type of risk and one outcome) (Ofeimun \u0026amp; Okeke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gleibner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ishmail et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Onyegiri et al., Second, we disaggregate sustainable performance into both its social and governance aspects, offering more nuanced information than those studies that use composite ESG scores or solely use financial indicators. Third, we present intellectual capital as a moderator and also provide the first systematic empirical studies regarding the effect of human, structural and relational knowledge resources of the Nigerian banks on the sustainability returns of risk management practices. Fourth, we use the Panel-Corrected Standard Errors (PCSE) estimator during 2015 to 2024, which includes post-recession recovery, digital banking transformation, the COVID-19 pandemic, and post-pandemic consolidation. This increases the rigor of the methodology used, as well as, the relevance of our findings in the present.\u003c/p\u003e \u003cp\u003eThe rest of the research will be organized in the following way. Part 2 will examine the theoretical background and formulate the hypotheses. The data and the methodology are outlined in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Empirical results are given in Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e and a conclusion given in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5\u003c/span\u003e with policy implications and future research suggestions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Background\u003c/h2\u003e \u003cp\u003eThe paper develops a theoretical justification of the relationships being studied by suggesting that proper risk management enables the financial sustainability to maintain social performance as well as the participation in governance. Intellectual capital also enhances the above relationships by facilitating the conversion of risk management practices into quantifiable sustainable results by banks.\u003c/p\u003e \u003cp\u003eThis study is centered by the Resource-Based View (RBV) (Barney, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). According to RBV, competitive advantage is created by valuable, rare, inimitable, and non-substitutable (VRIN) resources. Risk management capabilities and intellectual capital are such resources in the context of the Nigerian deposit money banks. They are ingrained in organizational practices, human capabilities, and knowledge systems, and are hard to imitate, and they are essential in producing high quality social and governance performance (Salvi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pereira \u0026amp; Bamel, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to RBV, Stakeholder Theory (Freeman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) highlights the normative ground of social and governance performance. When banks are keen to balance the requirements of different stakeholders such as employees, customers, communities, creditors, and regulators, they generate value that goes beyond the conventional financial measures. Lastly, the Dynamic Capabilities perspective (Teece et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) unites these views and emphasizes that the capacity of banks that have intellectual capital to re-structure internal resources according to evolving risk environments. This ability allows for social and governance performance even when the market or liquidity is under stress (Malkah \u0026amp; Nandiroh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofia, 2021). Combined, these frameworks underscore the anticipation that intellectual capital mediates the association between risk management and non-financial sustainability results, which improves the capability of banks in transforming risk management practices into concrete social and governance advantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Market Risk Management and Social Performance.\u003c/h2\u003e \u003cp\u003eMarket risk is a probability of incurring financial losses due to undesirable changes in market variables like interest rates, exchange rates, equity prices, and commodity prices. Market risk management in this paper is measured by the Value-at-Risk (VaR) measure at the 95th percentile level that represents the largest possible loss in normal market operations (Gupta et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Valaskova et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The connection between market risk management and social performance may be viewed in terms of a few related processes. On the one hand, proper management of market risk leads to financial stability as it minimizes the volatility of earnings and maintains capital buffers. Banks will have fewer chances to incur unforeseen losses that drain resources when they are able to predict and deal with market exposures effectively. This stability provides financial slack time which enables banks to undertake discretionary investments in social activities like employee welfare programs, community projects, and other socially responsible activities. In this regard, well-managed companies are more likely to be able to maintain or improve their social performance (Gleibner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nirino et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, the unstable market situation can compel financially strained banks to reduce non-core spending, such as social programs, in the context of the short-term capital-saving measures (Sandberg et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This tension brings out the fact that net impact of market risk management on social performance is determined by the overall risk capacity and strategic priorities of the bank. Based on this, legitimacy theory indicates that the market pressure can in fact lead to increased social engagement by firms in response to the need to retain stakeholder trust and institutional credibility. Banks can build stronger relationships with employees, customers, and the rest of the community by showing that they uphold their social commitments in challenging times, thereby protecting their reputational capital in the long term (Palma-Ruiz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndirect but significant support of these positive paths can be found in empirical evidence of the Nigerian banking situation. To illustrate this, Agbana et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) discover that proper market risk management has a major positive effect on the profitability of banks, whereas Onyegiri et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate that diversification strategy can help reduce the negative influence of market risk on the profitability of banks. Though the studies are on financial performance, their results suggest that a bank that has a robust market risk system has more ability to meet wider obligations such as social obligations. This view is further reinforced by the moderating effect of intellectual capital. Banks that possess superior intellectual capital especially those that possess sophisticated risk modelling and possess well established internal knowledge systems are in a better position to understand complex risk signals and also effectively communicate the same to stakeholders. This increased ability will help in the quality of risk decision making as well as disclosures about risk and help these banks be able to transform their market risk management practices into social performance results. Previous studies indicate that intellectual capital facilitates innovation, both organizational resilience and stakeholder engagement that result in high-quality social performance (Rislanudeen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Combined, these theoretical and empirical findings imply that market risk management not only contributes to social performance, but this impact is enhanced when a bank has a robust pool of intellectual capital. Accordingly, we propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eMarket risk management is positively associated to the social performance of the Nigerian DMBs.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eThere is a positive moderating effect of intellectual capital between market risk management and social performance of Nigerian DMBs.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Liquidity risk Management and Corporate Governance Performance.\u003c/h2\u003e \u003cp\u003eLiquidity risk occurs when a bank cannot fulfill the short-term commitments without using substantial expenses (Dountimiarye et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is especially acute in Nigeria, where the interbank market is not yet very deep, which puts deposit money banks (DMBs) at risk of increased liquidity shocks. To this effect, the Central Bank of Nigeria (CBN) has implemented Basel III-related regulatory standards, such as the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR), to enhance the practices in terms of liquidity risk management (Ishmail et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Theoretically, good management of liquidity risk involves good governance and presence of board participation. Such liquidity management functions as cash flow forecasting, liquidity buffer, and stress testing require on-going monitoring and board-level strategic oversight. Similar to Musa et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the greater the involvement of the board, the higher the quality of governance due to better monitoring and decision making. On the same note, Hettiarachchi and Sameera (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight that the risk management practices can enhance board activity especially in financial institutions where risk management is of utmost importance.\u003c/p\u003e \u003cp\u003eThis relationship is further evidenced by empirical evidence. Ofeimun et al. (2019) reveal that good liquidity management practices have a significant impact on the performance of banks in Nigeria, but the governance mechanisms have a significant indirect role. Similarly, Bashir and Umar (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) state that the better the liquidity position of banks, the better the governance results, indicating that liquidity discipline is the support to effective and accountable boards. In addition to the direct connection, intellectual capital serves a mediating role, by making the governance processes more effective. Structural capital including strong information systems, internal controls and risk reporting structures allows flow of information to the board in time and in a correct manner, which enhances oversight functions. According to Sofia (2021), companies that have a higher structural capital can more easily convert the operations practices into governance outcomes. Similarly, Wang and Juo (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that intellectual capital enhances the quality of decisions and organizational efficiency by increasing knowledge of processing information in companies.\u003c/p\u003e \u003cp\u003eHere, a bank that boasts of a greater intellectual capital will find it easier to transform its liquidity risk management activities into better governance performance. The availability of sophisticated systems and knowledge infrastructure enables board members to interact more efficiently with issues of liquidity, increasing the benefits of liquidity management practices on governance. We therefore propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eLiquidity risks management has a positive relationship with corporate governance performance of Nigerian DMBs.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003eIntellectual capital is positively moderated by the relationship between liquidity risk management and corporate governance performance of Nigerian DMBs.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample\u003c/h2\u003e \u003cp\u003eTwelve DMBs that were actively traded on the Nigerian Exchange Group (NGX) during the years 2015 to 2024 were included in the sample, namely, Access Bank, Eco Bank Nigeria, Fidelity Bank, First Bank of Nigeria, FCMB, Guaranty Trust Bank, Sterling Bank, Union Bank of Nigeria, UBA, Unity Bank, Wema Bank, and Zenith Bank. The criteria were that the sample must have been listed on NGX throughout the entire sample period and that both audited annual report and sustainability disclosures are available and that the sample must represent a substantial portion of total assets in the Nigerian banking sector, approximated at 85%. The final balanced panel includes 120 firm-year observations, including post-recession recovery (2015 to 2016), digital banking transformation (2017 to 2019), COVID-19 disruption (2020 to 2021), and post-pandemic consolidation (2022 to 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model Specification\u003c/h2\u003e \u003cp\u003eThere are four specified regression models, two baseline and two moderation-augmented one, each with a dependent variable. The PCSE estimator is used here to address the panel specific heteroscedasticity and the serial autocorrelation as identified in the pre-estimation diagnostics.\u003c/p\u003e \u003cp\u003eThe baseline regression model for the MRM\u0026ndash;social performance relationship is:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{C}\\text{P}\\text{ᵢ}\\text{ₜ}\\:=\\:{\\pi\\:}₀\\:+\\:{\\pi\\:}₁\\text{M}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₂\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₃\\text{F}\\text{S}\\text{Z}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₄\\text{L}\\text{E}\\text{V}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₅\\text{G}\\text{R}\\text{O}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\epsilon\\:}\\text{ᵢ}\\text{ₜ}\\:\u0026middot;\u0026middot;\\)\u003c/span\u003e \u003c/span\u003e\u0026middot; (1)\u003c/p\u003e \u003cp\u003eThe moderation-augmented model is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\text{S}\\text{C}\\text{P}\\text{ᵢ}\\text{ₜ}\\:={\\pi\\:}₀\\:+\\:{\\pi\\:}₁\\text{M}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₂\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₃(\\text{M}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:\\times\\:\\:\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ})\\:+\\:{\\pi\\:}₄\\text{F}\\text{S}\\text{Z}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₅\\text{L}\\text{E}\\text{V}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₆\\text{G}\\text{R}\\text{O}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\epsilon\\:}\\text{ᵢ}\\text{ₜ}\\:\u0026middot;\u0026middot;\u0026middot;\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe analogous baseline and moderation models for the LRM\u0026ndash;corporate governance performance relationship are:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{G}\\text{P}\\text{ᵢ}\\text{ₜ}\\:={{\\pi\\:}}^{0}+\\:{{\\pi\\:}}^{1}\\text{L}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:+\\:{{\\pi\\:}}^{2}\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₃\\text{F}\\text{S}\\text{Z}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₄\\text{L}\\text{E}\\text{V}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₅\\text{G}\\text{R}\\text{O}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\epsilon\\:}\\text{ᵢ}\\text{ₜ}\\:\u0026middot;\u0026middot;\u0026middot;\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\:\\text{C}\\text{G}\\text{P}\\text{ᵢ}\\text{ₜ}\\:=\\:{\\pi\\:}₀\\:+\\:{\\pi\\:}₁\\text{L}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₂\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₃(\\text{L}\\text{R}\\text{M}\\text{ᵢ}\\text{ₜ}\\:\\times\\:\\:\\text{I}\\text{C}\\text{ᵢ}\\text{ₜ})\\:+\\:{\\pi\\:}₄\\text{F}\\text{S}\\text{Z}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₅\\text{L}\\text{E}\\text{V}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\pi\\:}₆\\text{G}\\text{R}\\text{O}\\text{ᵢ}\\text{ₜ}\\:+\\:{\\epsilon\\:}\\text{ᵢ}\\text{ₜ}\\:\u0026middot;\u0026middot;\u0026middot;\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: SCP represents social performance, measured as the employee compensation ratio; CGP denotes corporate governance performance, proxied by the number of board meetings. MRM refers to market risk management, measured using Value-at-Risk (VaR), while LRM represents liquidity risk management, measured using the liquidity coverage ratio (LCR). IC captures intellectual capital, measured using the VAIC\u0026trade; model. FSZ denotes bank size, computed as the natural logarithm of total assets. LEV represents leverage, measured as the ratio of total debt to total equity. GRO represents growth opportunity and is measured as the annual growth rate in total assets, calculated as: total asset \u003csub\u003et\u003c/sub\u003e \u0026ndash; total asset \u003csub\u003et\u0026minus;\u0026thinsp;1\u003c/sub\u003e/total asset \u003csub\u003et\u0026minus;\u0026thinsp;1\u003c/sub\u003e \u0026times; 100. The parameters π₀ represent the constant term, while π₁\u0026ndash;π₆ are the regression coefficients. The term ε\u003csub\u003ei\u003c/sub\u003eₜ denotes the idiosyncratic error term. A positive and statistically significant coefficient of the interaction term (π₃ \u0026gt; 0) in Models 2 and 4 indicates that intellectual capital strengthens (or enhances) the effect of market risk management and liquidity risk management on firm performance. Standard errors are clustered at the firm level to control for potential heteroscedasticity and serial correlation within firms over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Variable Operationalization\u003c/h2\u003e \u003cp\u003eThe definitions and measurement of all the variables in this research are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dependent variables are social performance (SCP) which is represented as the ratio of total employee pay to total operating costs (Cek \u0026amp; Ercantun, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gleibner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and corporate governance performance (CGP) which is proxied by the number of board meetings per year (Radu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Musa et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The primary independent variables are market risk management (MRM) (Value-at-Risk (VaR) at 95th percentile confidence level) and liquidity risk management (LRM) (Liquidity Coverage Ratio (LCR)) (division of high-quality liquid assets by net 30-day cash outflows). Intellectual capital (IC) is characterized in terms of VAIC framework (Pulic, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rislanudeen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which is a mixture of efficiency in human capital (HCE\u0026thinsp;=\u0026thinsp;VA/HC), structural capital efficiency (SCE\u0026thinsp;=\u0026thinsp;SC/VA), and capital employed efficiency (CEE\u0026thinsp;=\u0026thinsp;VA/CE). Three control variables (bank size, FSZ; leverage, LEV; and growth opportunity, GRO) are also included in the study, in accordance with (Falade et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The reason behind the selection of these is that they present the most influential contextual factors influencing the risk management, as well as the sustainable performance. Bank size, which is the resource capacity, leverage, which is the exposure to financial risks, and the growth opportunity which is the potential to expand all have a direct relationship to the social and governance results. These three control variables are chosen because they allow the model to be simple and focused at the same time considering the most important factors that may have an impact on the results. Lastly, all the ratio variables are winsorized at the 1 percent level at both ends to decrease the impact of the extreme values and enhance robustness.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below presents the operationalization of all variables used in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eVariable operationalization\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA priori sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal employee compensation / Total operating expenses \u0026times; 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorporate Governance Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of board meetings held annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Risk Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePortfolio VaR at 95th percentile confidence level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity Risk Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-quality liquid assets / Net 30-day cash outflows (LCR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerating Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVAIC\u0026trade; = HCE\u0026thinsp;+\u0026thinsp;SCE\u0026thinsp;+\u0026thinsp;CEE; HCE\u0026thinsp;=\u0026thinsp;VA/HC; SCE\u0026thinsp;=\u0026thinsp;SC/VA; CEE\u0026thinsp;=\u0026thinsp;VA/CE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural logarithm of total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal liabilities / Total equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Total Assetsₜ \u0026minus; Total Assetsₜ₋₁) / Total Assetsₜ₋₁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Reports of Listed Banks (NGX);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: The definition of all variables is based on standard practice in the Nigerian banking literature. VaR= Value-at-Risk; LCR= Liquidity Coverage Ratio; VAIC= Value-Added Intellectual Coefficient; VA= Value Added; HC= Human Capital; SC= Structural Capital; CE= Capital Employed. Source: Authors' Compilation (2026).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe summary statistics, Pearson correlation matrix, and collinearity diagnostics of the baseline regression variables are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average social performance of 32.64% shows that on average, the sample banks spent about a third of operating expenditure on employee compensation indicating moderate labour intensity within the industry. The average CGP of 8.42 board meetings annually is within the governance framework of the CBN, which provides four quarterly board meetings and a maximum of 16 board meetings, which indicates that the board is active. The average VaR of 0.84% represents a moderate level of portfolio market risk exposure, whereas the average LCR, 142.7% is significantly higher than the regulatory minimum of 100%, suggesting that the sector has a surplus of high quality liquid assets. The average VAIC 3.18 implies a medium level of IC performance, which is in line with previous findings in Sub-Saharan African banking environments. All the variance inflation factors (VIFs) are less than 5.0, which proves the missing undesirable multicollinearity among the independent variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBelow provides the descriptive statistics and collinearity diagnostics for all variables. \u003cb\u003eDescriptive statistics\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Performance \u0026ndash; SCP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorp. Gov. Performance \u0026ndash; CGP (meetings)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Risk \u0026ndash; MRM (VaR, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity Risk \u0026ndash; LRM (LCR, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e284.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital \u0026ndash; VAIC\u0026trade;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size \u0026ndash; FSZ (Log TA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage \u0026ndash; LEV (D/E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity \u0026ndash; GRO (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table presents the distribution of variables. VIF\u0026thinsp;=\u0026thinsp;variance inflation factor. All variables are defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eSource: Authors' Compilation (2026).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Market Risk Management and Social Performance\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e states the regression results of PCSE of the market risk management social performance models. In the first Model 1, the market risk management and social performance are positively related but not significantly (b\u0026thinsp;=\u0026thinsp;0.0015). The positive directionality is in line with the financial resilience mechanism in which profitable, well-hedged banks have more capacity to spend socially but the effect does not reach the statistical significance level at more traditional levels indicating that the market risk management social performance might be too weak to be able to discern the sample. Bank size (b\u0026thinsp;=\u0026thinsp;1.252) is the strongest predictor of social performance, which aligns with the resource munificence argument, which states that bigger banks have both the motivation and ability to invest in the welfare of employees and the community (Falade et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The social performance variation is explained by the baseline model is 89.25%. R\u0026sup2; = 0.8925). The hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is not supported.\u003c/p\u003e \u003cp\u003eThe moderation-augmented Model 2 has a positive but non-significant MRM x VAIC\u0026trade; interaction (b\u0026thinsp;=\u0026thinsp;0.017), which does not support Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. IC (VAIC\u0026trade;) a positive relationship exist between the independent predictor of social performance (b\u0026thinsp;=\u0026thinsp;0.324). This observation implies that Intellectual capital has been linked to the social performance payoffs of talent management systems, the culture of stakeholder communication and knowledge sharing and not by increasing the social performance payoffs of market risk management. The fact that the interaction between MRM and IC was not significant means that the mechanism through which market risk management is linked to social performance does not run through the mediation of intellectual capital, but rather, it seems that intellectual capital has a direct effect on social performance. The mean value of the marginal effect of intellectual capital on social performance is 0.324 percentage points per unit increase in VAIC\u0026trade;. The social performance (adjusted) can be explained by Model 2 (91.24%). R\u0026sup2; = 0.9088). Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is not supported.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMarket risk management and social performance\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Risk Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0015(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0042(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital (VAIC\u0026trade;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3241 (4.18)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM \u0026times; VAIC\u0026trade;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0170 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size (FSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2522 (12.55)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1843 (11.27)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage (LEV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0632 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8714 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity (GRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0017 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0019 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;31.866 (\u0026minus;\u0026thinsp;12.05)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;29.412 (\u0026minus;\u0026thinsp;11.18)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.04***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDependent variable: Social Performance (Employee Compensation Ratio, %). *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. z-statistics in parentheses. Source: Authors' Compilation (2026).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Liquidity Risk Management and Corporate Governance Performance.\u003c/h2\u003e \u003cp\u003eThe results of the regression of PCSE in the models of liquidity risk management corporate governance are as in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the baseline Model 3, LRM has a positive but statistically insignificant direct effect on corporate governance performance (b\u0026thinsp;=\u0026thinsp;0.0015). The governance predictor that is dominant (b\u0026thinsp;=\u0026thinsp;0.0926) is bank size and growth opportunity has a negative and significant impact (b = -0.0066). The negative growth opportunity coefficient is also in line with the agency cost-of-governance hypothesis (Lehn et al., 2009), which states that a rapidly growing bank should shift managerial bandwidth towards investment activity, which limits intentional board-level governance activity. The baseline model accounts 75.96 per cent of the variation in corporate governance performance (adj.). R\u0026sup2; = 0.7524).\u003c/p\u003e \u003cp\u003eThe most important discovery of this paper in Model 4. The addition of the intellectual capital interaction term gives a positive and statistically significant value of the liquidity risk management (b\u0026thinsp;=\u0026thinsp;0.240), which supports Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and means that the liquidity risk management governance relationship is achieved when banks have sufficient intellectual capital resources to translate liquidity risk governance into formal board level procedures. More importantly, the interaction term between LRM and VAIC is positive and statistically significant (b\u0026thinsp;=\u0026thinsp;0.566) which confirms Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. What this observation suggests is that banks that have better VAICs scores reap much higher returns on governance performance investments in liquidity risk management: better human capital allows boards to do more complex analysis on the liquidity risk reports, better structural capital enables boards to adopt the governance information systems that enable boards to respond more effectively to liquidity pressures, and better relational capital provides the institutional environment where credible liquidity governance decisions are made and acted on. IC has an independent and a significant positive impact on the performance of governance (b\u0026thinsp;=\u0026thinsp;0.703). Economically, a one standard deviation shifts in VAIC (1.08) enhances the liquidity risk management governance impact, by about 0.61 more board meetings per year (=\u0026thinsp;0.566 x 1.08). Model 4, which suggests that highly leveraged banks are limited in the nature of their interactions at the board of directors\u0026rsquo; level because of their debt-service pressures and creditors-demand oversight, shows that leverage is negatively and significantly related to governance performance (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;57.21).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLiquidity risk management and corporate governance performance\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3 β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 4 β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity Risk Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0015 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2396 (2.20)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital (VAIC\u0026trade;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7025 (2.49)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRM \u0026times; VAIC\u0026trade; (Interaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5662 (2.25)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size (FSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0926 (5.30)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.9495 (\u0026minus;\u0026thinsp;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage (LEV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0182 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;57.213 (\u0026minus;\u0026thinsp;3.12)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity (GRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0066 (\u0026minus;\u0026thinsp;3.13)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0431 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.537 (\u0026minus;\u0026thinsp;1.80)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.786 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.84***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDependent variable: Corporate Governance Performance (Board Meeting Frequency). *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. z-statistics in parentheses. Source: Authors' Computation (2026).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness Tests\u003c/h2\u003e \u003cp\u003eWe conduct several additional analyses to validate and reinforce the main findings. Specifically, this study performs robustness checks by: the results are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRobustness test results\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative Social Proxy (Model 5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSFR Liquidity (Model 6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystem GMM (Model 7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-2018 Sample (Model 8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Risk Management (MRM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0031 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity Risk Management (LRM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2184 (2.04)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2261 (2.11)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2475 (2.09)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital (VAIC\u0026trade;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2874 (3.96)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6812 (2.36)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6553 (2.29)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7198 (2.41)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0142 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5123 (2.16)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5417 (2.18)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5731 (2.21)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size (FSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1035 (10.88)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.7741 (\u0026minus;\u0026thinsp;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.6935 (\u0026minus;\u0026thinsp;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.8427 (\u0026minus;\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage (LEV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5421 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;52.846 (\u0026minus;\u0026thinsp;2.97)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;49.732 (\u0026minus;\u0026thinsp;2.81)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;55.918 (\u0026minus;\u0026thinsp;2.95)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity (GRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0021 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0395 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0378 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0417 (0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors' Compilation (2026).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe first is that we use an employment intensity index (total staff/total assets) in place of the employee compensation ratio as an alternative social performance proxy. The market risk management coefficient and the intellectual capital coefficient maintain their sign and approximate value, which proves that the findings of the baseline market risk management social performance are not a result of the chosen proxy.\u003c/p\u003e \u003cp\u003eWe replace NSFR by LCR as the measure of liquidity risk and the LRM \u0026times; VAIC\u0026trade; interaction is positive and significant (b\u0026thinsp;=\u0026thinsp;0.512, p\u0026thinsp;=\u0026thinsp;0.031) which confirms that the intellectual capital moderating effect on the liquidity risk management governance performance nexus is robust to the alternative operationalization of the liquidity risk.\u003c/p\u003e \u003cp\u003eThird, to deal with issue of reverse causality in this case that as governance performance improves, banks will invest more in intellectual capital we re-estimate all interaction models with system-GMM estimator. All specifications result in directionally consistent coefficients, and so concerns over endogeneity bias are alleviated.\u003c/p\u003e \u003cp\u003eFourth, the sample is limited to the post-2018 sub-period, which estimates the updated CBN corporate governance principles, which creates interaction effects consistent in direction and statistically with the full sample findings, which demonstrate temporal robustness. All these tests lead to inferences that intellectual capital constitutes a boundary condition of the sustainable performance relationship of risk management in the banking sector in Nigeria. The System-GMM estimation results that address endogeneity concerns are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSystem-GMM estimation results (endogeneity robustness check)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Performance Model (GMM) β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovernance Performance Model (GMM) β (z-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagged Dependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.621 (5.84)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487 (4.92)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Risk Management (MRM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0021 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity Risk Management (LRM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2261 (2.11)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Capital (VAIC\u0026trade;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3015 (3.77)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6812 (2.36)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM \u0026times; VAIC\u0026trade;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0142 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRM \u0026times; VAIC\u0026trade;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5417 (2.18)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size (FSZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1032 (9.44)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.7821 (\u0026minus;\u0026thinsp;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage (LEV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5521 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;49.672 (\u0026minus;\u0026thinsp;2.88)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Opportunity (GRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0015 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0382 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;25.114 (\u0026minus;\u0026thinsp;9.27)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.552 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostic Tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1) p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2) p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen Test (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. z-statistics in parentheses. AR (2) tests for second-order serial correlation; Hansen test for instrument validity. Source: Authors\u0026rsquo; Computation (2026).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Interpretation of System-GMM Results:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides the System-GMM estimates that consider endogeneity, dynamic persistence, and the unobserved heterogeneity. The lagged dependent variable is positive and significantly high in both models, which attests good persistence in both social and governance performance and justifies the dynamic specification.\u003c/p\u003e \u003cp\u003eIn line with the baseline findings, market risk management is positively although not significantly correlated with explaining social performance and its interaction with intellectual capital (VAIC\u0026trade;) is also non-significant. This once again confirms the lack of both direct and moderating effect, thus not supporting H1 and H2. In contrast, liquidity risk management (LRM) retains a positive and statistically significant effect on corporate governance performance. More to the point, the interaction term (LRM x VAIC\u0026trade;) is still positive and significant, which proves that intellectual capital boosts the returns to liquidity risk management governance.\u003c/p\u003e \u003cp\u003eThese results are resistant to endogeneity and support H3 and H4. The VAIC coefficient is positive and significant, which implies that intellectual capital positively and independently enhances social and governance performance. Of the controls, bank size is only important in the social performance model, and leverage has a negative and significant impact on governance performance, which is in line with the arguments of financial constraint. Model invalidity is tested by diagnostic tests. The Arellano-Bond AR (2) test does not reject the null of no second-order correlation in serial, whereas the Hansen test is a sign that there is instrument validity. The number of instruments is within reasonable limits, which alleviates worries of overfitting. In general, the System-GMM estimates are consistent with the PCSE results, and it was confirmed that the results are endogeneity-robust. The results always indicate that MRM is not determinant of social performance, but LRM, under the condition of intellectual capital, positively influences corporate governance performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussions and Implications","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 General Discussion\u003c/h2\u003e \u003cp\u003eThis study indicates that the direct impacts of both market risk management and liquidity risk management on sustainable performance are positive but not significant in the model specification with the baseline, which indicates that the risk management practices do not reveal a significant statistical relationship between the social and corporate governance performance. The results also indicate that the risk-sustainability connection in the Nigerian DMBs is conditional depending on the presence of the IC resources, which become the organizational infrastructure in which the abilities of risk management are transformed into the performance outcomes that are sustainable.\u003c/p\u003e \u003cp\u003eThe positive and significant correlation between intellectual capital and social performance model specifications aligns with the organizational learning and stakeholder management literature which postulates that human capital especially talent management systems, training investment and knowledge sharing culture is the main force behind the attainment of employee welfare (Faedfar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sayed and Nefzi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The fact that the market risk management \u0026times; VAIC\u0026trade; interaction is not significant in determining social performance implies that intellectual capital is correlated with its own social performance payoffs, but does not directly enhance the social payoffs of market risk management, perhaps because of institutional-level indirect mechanisms.\u003c/p\u003e \u003cp\u003eThis study has found that the positive and significant relationship between governance performance of liquidity risk management and intellectual capital moderation (b\u0026thinsp;=\u0026thinsp;0.566, p\u0026thinsp;=\u0026thinsp;0.024) is positive and significant. This finding suggests that the governance advantages of liquidity risk management depend on the structural capital endowments that facilitate the boards in processing, analyzing, and responding to liquidity information. Banks that have poor governance information systems, as well as, limited risk literacy at the board level, are unlikely to translate even good liquidity risk management practices into quantifiable board engagement outcome a finding that has direct implications on the design of governance capacity building programme in the Nigerian banking. The agency cost hypothesis, which states that a high level of leverage limits discretionary board actions by enhancing creditor oversight and changing managerial focus towards debt-service commitments, can be attributed to the negative and significant leverage effect on governance performance in Model 4 (b = -57.21, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eThe robustness tests, reported in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e, verify the stability of the baseline results. The major direction and importance of interaction effects remain apparent in other proxy variables of liquidity risk measures and estimation strategies, which is similar to the international evidence on the influence of institutional quality and cultural context in determining ESG outcome (Liang and Renneboog, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mooneeapen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Implications in Practices and Research\u003c/h2\u003e \u003cp\u003eThere are a number of significant implications of the results of this research to the management of banks, policymakers and researchers. To bank managers, the findings also remind them that the investments in intellectual capital in terms of employee training, governance information systems, and relationship capital can be the enabling infrastructure whereby sound liquidity risk management can be translated into quantifiable governance performance. Such risk management frameworks, which are not supported by sufficient organizational knowledge, are unlikely to realize their full governance potential, no matter how sophisticated the framework is. An important practical implication is that banks must invest both in technical aspects of risk management, and in human and structural capital endowments that allow boards to use such investments to good effect.\u003c/p\u003e \u003cp\u003eTo Central Bank of Nigeria and the regulators, the data shows that new corporate governance principles would clearly state that intellectual capital enablers are specifically human capital development and governance technology infrastructure as supplementary pillars of quality governance, in addition to traditional risk measures. Introducing the requirements of the disclosure of the intellectual capital into the Revised Corporate Governance Guidelines would help supervisors, investors, and ESG rating agencies to evaluate the intellectual capital governance nexus systematically and to distinguish between those banks with the highest governance score that might indicate a real organizational capacity and those with the highest score that might indicate superficial compliance. The negative moderating impact of leverage on the performance of governance further justifies macro prudential advice that discourages excessive leverage among systemically important banks.\u003c/p\u003e \u003cp\u003eTo researchers, this research highlights some of the promising areas of future research. To start with, the framework can be expanded to cover the environmental aspect of ESG performance and offer a more comprehensive view of the intellectual capital risk sustainability nexus. Second, one may use panel threshold models to test the non-linearity of the moderating effect of intellectual capital. In particular, whether there exists a minimum threshold of intellectual capital beneath which the risk management is not yielding any governance returns. Third, a comparative West African banking panel could be subjected to the model to determine how the findings can be generalized to the region. Fourth, an analysis of VAIC components (HCE, SCE, CEE) would enable examining which dimensions of ICs are the most critical in moderating each risk sustainability pathway. Lastly, more enriching disclosure information would allow the researchers to use other intellectual capital proxies such as board human capital indices or governance technology spending ratios to triangulate the VAIC -based results.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIncreasing scholarly research has explored the aspects and consequences of risk management in corporate sustainability and non-financial performance. This study had a balanced panel of twelve NGX-listed Nigeria deposit money banks between the years 2015 to 2024 (n\u0026thinsp;=\u0026thinsp;120 firm-year observations) to study the effect of market risk management and liquidity risk management on the social and corporate governance aspects of sustainable performance with intellectual capital as an intermediate variable. Previous studies have indicated that proper risk management gives the financial stability needed to maintain social and governance promises, whereas intellectual capital as an organizational knowledge deposit, structural systems and relational networks preconditions the degree of this association. In this framework, intellectual capital endowments are predicted to enhance the sustainability returns on risk management.\u003c/p\u003e \u003cp\u003eConsistent with this prediction, the results show that liquidity risk management is positively and significantly associated with corporate governance performance only in the presence of adequate intellectual capital and that intellectual capital positively and significantly moderates the liquidity risk management\u0026ndash;governance relationship. Intellectual capital has a positive and significant relationship with social performance, whereas the relationship between market risk management and liquidity risk management and social and governance performance, respectively, are insignificant. The main conclusions are proven by robustness tests with alternative proxies, estimators, and sub-period tests.\u003c/p\u003e \u003cp\u003eDespite the fact that this study contributes to the knowledge in the area of intellectual capital sustainability nexus in Nigerian banking, a number of limitations should be mentioned. Although the VAIC\u0026trade; model is popular, its operationalization of intellectual capital is partial, failing to capture relational capital or knowledge assets unique to governance. The sample is limited to listed deposit money banks which might dissimilarly differ with non-listed or smaller banks in a manner that limits external validity. To mitigate the limitations of this study, future studies ought to involve the use of other measures of intellectual capital, increase the sample frame, and conduct tests of the model in more cross-country contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eThis study is based exclusively on secondary data drawn from publicly available annual reports and sustainability disclosures of Nigerian Deposit Money Banks listed on the Nigerian Exchange Group (NGX). No human participants were directly involved in data collection; therefore, formal ethical approval and consent to participate are not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funding was received for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAduwo Ayomikun Elizabeth: Conceptualisation, literature review, theoretical framework development (Resource-Based View, Stakeholder Theory, and Dynamic Capabilities), hypothesis formulation, data collection from annual reports of the 12 NGX-listed deposit money banks, variable operationalisation, writing of the original draft, and editing of the final manuscript.Bojuwon Mustapha: Research methodology design, model specification (PCSE and System-GMM estimation), statistical analysis and interpretation of results, robustness testing (alternative variable measures, endogeneity checks, temporal stability tests), supervision, validation of empirical findings, and critical review and revision of the manuscript.Both authors jointly contributed to the discussion of findings, policy implications, conclusion, and approved the final version of the manuscript for submission\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003enil\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAchimugu, A., Ocheni, S. 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Business Strategy and the Environment, 30(7), 3241\u0026ndash;3254.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Market risk management, Liquidity risk management, Social performance, Corporate governance performance, Intellectual capital","lastPublishedDoi":"10.21203/rs.3.rs-9488067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9488067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study explores the connections between market risk management and liquidity risk management and social performance and corporate governance performance, and how intellectual capital in the form of human, structural and relational can mediate these relations of the Nigerian deposit money banks (DMBs). Banks that have higher intellectual capital use more complex risk governance systems and communication systems between stakeholders, and thus, increase the sustainability dividend of risk management practices. Using a comprehensive sample of 12 Nigerian Exchange (NGX) listed deposit money banks over the period 2015\u0026ndash;2024, we document four main findings. First, market risk management affects social performance positively although this is statistically insignificant. Second, liquidity risk management is not a significant factor in corporate governance performance in the baseline specification. Thirdly, social performance has a positive and independent relationship with intellectual capital. Fourth and most importantly, intellectual capital moderates the liquidity risk management governance performance relationship in a positive way meaning that the governance returns to liquidity risk management is only achieved when banks have sufficient intellectual capital resources. The most powerful control predictor in all the models is the bank size, and the robustness tests with alternative constructions of the variable Net Stable Funding Ratio-based liquidity and System Generalized Method of Moments estimations confirm the baseline conclusions. This paper can be added to the developing literature on market risk management, liquidity risk management and social performance and corporate performance. Altogether, the research provides new and policy-oriented information on the knowledge based underpinnings of sustainable banking in Nigeria and proves that intellectual capital is an important but frequently neglected facilitator of risk sustainability nexus.\u003c/p\u003e","manuscriptTitle":"Market and Liquidity Risk Management in Banking: Can Intellectual Capital Promote Sustainable Performance?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 13:39:55","doi":"10.21203/rs.3.rs-9488067/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c135c5c-674b-41f6-b595-ce24c8fd6342","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"309218660631901726682603673840974427063","date":"2026-05-08T17:36:03+00:00","index":10,"fulltext":""},{"type":"reviewersInvited","content":"2","date":"2026-05-06T01:48:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T13:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 13:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9488067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9488067","identity":"rs-9488067","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}