ESG Performance and Corporate Corruption Risk Management: The Moderating Role of Risk Management Committees in GCC Firms

Abstract

This study investigates the impact of environmental, social, and governance (ESG) performance on corporate corruption risk management (CCRM) and examines the moderating role of the risk management committee (RMC) among non-financial firms in Gulf Cooperation Council (GCC) countries for the period spanning from 2015 to 2024. Building on agency and legitimacy theories, the study argues that ESG performance strengthens governance quality and ethical accountability, which is reflected in higher quality CCRM. Additionally, RMCs are expected to play a moderating role in enhancing oversight effectiveness, which boosts such a relationship. Using panel data derived from the Refinitiv Eikon database and employing Feasible Generalized Least Squares (FGLS) regression, the results reveal that firms with higher ESG performance exhibit significantly stronger corruption risk management practices. Moreover, the interaction between ESG performance and RMC presence positively amplifies this relationship, underscoring the committee’s role in institutionalizing ethical conduct and improving governance transparency. Robustness tests using alternative ESG and CCRM measures confirm the consistency of these findings. The study provides novel empirical evidence from the GCC context, highlighting how governance structures and sustainability practices jointly enhance corporate integrity. It offers theoretical, practical, and policy implications for promoting ethical governance and sustainable development in emerging markets.

1. Introduction

Corporate corruption, manifested in bribery, fraud, embezzlement, and other forms of corporate misconduct, represents one of the most pervasive challenges facing firms worldwide (Aidt, 2010; Rose-Ackerman, 2013; R. I. A. Salem et al., 2023; R. Salem & Ghazwani, 2025). Such practices undermine governance quality, investor confidence, and sustainable development efforts (Aidt, 2010; Rose-Ackerman, 2013; R. I. A. Salem et al., 2023; R. Salem & Ghazwani, 2025; L. Aladwey et al., 2025; Pham & Yang, 2025; Ghazwani et al., 2025). These risks are particularly acute in emerging markets, where weaker institutional environments and limited enforcement capacity exacerbate corruption vulnerabilities (Sarhan & Gerged, 2023). Against this backdrop, ESG performance has emerged as a strategic framework for strengthening corporate accountability, enhancing transparency, and curbing unethical practices (Baldini et al., 2018; Previtali & Cerchiello, 2023; Githaiga, 2025; Almaqtari et al., 2024; Elhabib, 2024).

From agency theory perspective, corporate corruption reflects severe information asymmetry and managerial opportunism, manifested through moral hazard, arising from weak monitoring mechanisms and ineffective internal controls (Ross, 1973; Jensen & Meckling, 1976; Fama & Jensen, 1983; Eisenhardt, 1989; Kumawat & Patel, 2022). ESG performance and board-level risk oversight mechanisms, such as the RMC, function as governance tools that align managerial behavior with shareholder and stakeholder interests by strengthening monitoring intensity and reducing private-benefit extraction (Ng et al., 2012; Musallam, 2018; Karim et al., 2024; Pham & Yang, 2025). In parallel, legitimacy theory suggests that firms adopt ESG practices and strengthen governance structures to preserve organizational legitimacy, particularly in high-risk institutional environments such as emerging markets (Suchman, 1995; Deegan, 2019; Chairani & Siregar, 2021). In this sense, ESG engagement and the establishment of a dedicated RMC serve not only as internal control mechanisms but also as external legitimacy signals aimed at restoring stakeholder confidence and societal approval in the presence of corruption risk (Chantziaras et al., 2020; Marzouki et al., 2024; Subramaniam et al., 2009; Karim et al., 2024).

Despite the growing scholarly attention on ESG, empirical evidence on its effectiveness in managing corporate corruption risk remains limited, particularly in developing and transitional economies. More specifically, while recent studies suggest that ESG performance may reduce corruption risks (Sarhan & Gerged, 2023; Elsheikh et al., 2024; Ghazwani et al., 2024; Ghazwani et al., 2025; L. Aladwey et al., 2025), little is known about the governance mechanisms through which such effects operate. In particular, the moderating role of a stand-alone RMC in the ESG–corruption nexus has not yet been empirically examined, despite its strong theoretical relevance. This represents a critical gap in the literature that this study strives to fill.

The GCC countries provide a particularly suitable empirical setting for investigating this relationship. The region combines ambitious ESG-related and anti-corruption reforms—such as Saudi Arabia’s Vision 2030 and the UAE’s Vision 2031—with ownership structures that are heavily influenced by family control, state dominance, and cultural ties (Jamali & Karam, 2018; Alharbi, 2024; Ghazwani, 2025; L. Aladwey et al., 2025). These characteristics create a complex governance environment in which formal ESG initiatives coexist with persistent institutional fragility, thereby raising important questions regarding the effectiveness of governance mechanisms in curbing corruption risks.

Traditionally, oversight of enterprise risks, including corruption risk, has been entrusted to the audit committee (AC). However, the expanding scope and complexity of corporate risks have increasingly overburdened audit committees, prompting calls for the establishment of a dedicated RMC (Ng et al., 2012; Karim et al., 2024). Unlike the AC, which primarily focuses on financial reporting integrity and audit oversight, the RMC provides specialized attention to enterprise-wide risks, including corruption, cybersecurity, and regulatory compliance. Prior research suggests that the presence of an RMC strengthens board oversight, improves transparency, and enhances firms’ ability to manage emerging risks (Al-Hadi et al., 2016; Jia et al., 2019; Musallam, 2018; Musallam, 2024). Nevertheless, whether the RMC enhances the effectiveness of ESG performance in mitigating corporate corruption remains an open empirical question.

Accordingly, this study addresses this gap by examining the impact of ESG performance on corporate corruption risk management among 295 non-financial firms, yielding 2950 firm-year observations from GCC countries—Saudi Arabia, the UAE, Qatar, Kuwait, Oman, and Bahrain—over the period 2015–2024, with a particular focus on the moderating role of the RMC. Using panel data obtained primarily from the Refinitiv Eikon (LSEG) database and employing Feasible Generalized Least Squares (FGLS) regression, the study offers novel empirical evidence on how ESG mechanisms and board-level risk oversight interact in shaping corruption risk management outcomes. In doing so, it contributes to the literature on ESG and corruption (Sarhan & Gerged, 2023; Elsheikh et al., 2024; Marzouki et al., 2024) as well as to research on governance structures and risk oversight (Ng et al., 2012; Karim et al., 2024; Musallam, 2024), within a regional context that remains underexplored.

The findings are expected to yield important implications for both theory and practice. For regulators and policymakers, the study highlights the need to strengthen ESG enforcement mechanisms and institutionalize effective board-level risk oversight. For boards and firms, it demonstrates how ESG performance, when complemented by a dedicated RMC, can enhance integrity, strengthen stakeholder trust, and support long-term sustainability. For wider society, improved corruption risk management supported by ESG contributes to safeguarding public trust in corporate and financial systems and reducing vulnerabilities to fraud and misappropriation in key economic sectors.

The remainder of the paper is organized as follows. Section 2 reviews the relevant literature on ESG, corruption risk, and corporate governance, and develops the study’s hypotheses. Section 3 outlines the research methodology, including data, sample, and variable construction. Section 4 presents the empirical findings and discussion, while Section 5 concludes with implications for theory, practice, and policy, alongside acknowledged limitations and directions for future research.

2. Theoretical Framework, Empirical Review, and Hypotheses Development

2.1. Theoretical Framework

As a theoretical foundation, the study uses the reference frameworks of agency theory and legitimacy theory, which complement each other to interpret the impact of ESG performance on managing the risk of corporate corruption and identifying the role of risk management committees.

Agency theory (Jensen & Meckling, 1976) demonstrates how conflicts of interest emerge between management (agent) and investors (principals), especially when faced with situations of asymmetrical information, where management benefits from private, superior knowledge of the situation, potentially pursuing adverse selection and moral hazard practices (Ross, 1973; Jensen & Meckling, 1976; Fama & Jensen, 1983; Eisenhardt, 1989; Kumawat & Patel, 2022). Additionally, where governance is poor, the presence of corruption amplifies the challenges imposed by asymmetrical information, allowing for arbitrary management practices when pursuing moonshots, misrepresenting their ESG score, or actively covering up illicit and unethical behavior (Cerciello et al., 2023; Albitar et al., 2023). The observance of anti-corruption strategies acts as monitoring mechanisms that reduce the threat of moral hazard, increasing transparency and the reliability of the reported ESG score (Pham & Yang, 2025). In complement, business ethics institutionalizes integrity and accountability, thereby discouraging opportunism and reinforcing value alignment in the long term (Belas et al., 2024; Aboud & Diab, 2018; Eltweri et al., 2020; Alassuli et al., 2025). Conventionally, risk management oversight was within the responsibility of the audit committee. Nonetheless, as audit committees have become increasingly overburdened, researchers and regulators have progressively supported the establishment of separate RMCs to enhance the effectiveness of monitoring and governance (Ng et al., 2012; Karim et al., 2024). Specialized and independent RMCs improve the ability of the board to reduce information asymmetry, align managerial actions with shareholder and stakeholder interests, minimize agency costs, and strengthen integrity and risk management practices.

Legitimacy theory focuses on how companies aspire to conform to socially built norms, values, and expectations in a bid to maintain legitimacy (Suchman, 1995; Deegan, 2019). Voluntary and even compulsory ESG disclosures have been used extensively as a tool to showcase adherence to societal expectations while reducing the legitimacy gap between companies and stakeholders (Pham & Yang, 2025). But in environments where the risk of corruption is high, pressure to legitimacy may push companies toward making more selective and symbolic rather than substantive disclosures, attempting to maintain their reputations while disguising unethical behaviors (Chantziaras et al., 2020; Marzouki et al., 2024; Altarawneh et al., 2025). This manipulation complements information asymmetries, undermining ESG reporting credibility and reducing stakeholder confidence (Cheung & Lai, 2023). In this regards, the significance of good ethical culture and sound governance arrangements, including establishing a stand-alone RMC, becomes very imperative. Firms that pay due attention to business ethics in risk management and ESG reporting have better prospects of legitimacy and stakeholder trust even in vulnerable institutional settings (Chairani & Siregar, 2021). RMCs, through enhancing oversight and accountability, have been known to be recognized not only as an agency-alignment procedure but also a legitimacy maintenance tactic (Subramaniam et al., 2009; Karim et al., 2024).

2.2. Empirical Review and Hypotheses Development

2.2.1. ESG Performance and Corporate Corruption Risk Management

There has been considerable scholarly discussion surrounding the relationship between ESG performance and corporate corruption risk management, with studies presenting supplementary and conflicting findings. In the first stream of research, proponents advocate that high-quality ESG performance supports effective governance of corruption risk by institutionalizing ethical behaviors, enhancing transparency, and reducing informational asymmetry. From the agency theory perspective, strong ESG performance restricts the manager’s opportunism and minimizes the agency cost by rendering the managers unable to conceal self-benefiting behaviors while firms adopt good ESG disclosure and sustainability activities (Xu et al., 2019; Boura et al., 2020; Pham & Yang, 2025; Al-Hajaya et al., 2025). Empirical literature reveals that companies with a strong ESG performance are more likely to articulate anti-corruption strategies, communicate ethical commitment to stakeholders, and hence enjoy increased long-term legitimacy (Sarhan & Gerged, 2023; Chen et al., 2022; Ghazwani et al., 2024; Ghazwani et al., 2025). Therefore, ESG serves to act as a governance system in enhancing effective risk management of corruption through achieving higher accountability and stakeholder trust. However, this positive view, according to Boura et al. (2020), is largely derived from institutional environments characterized by relatively strong regulatory enforcement and stakeholder monitoring, which may partly explain why ESG mechanisms appear more effective in restraining opportunistic behavior in some contexts than in others.

On the contrary, a second set of studies reveals a more complex relationship and at times negatively positioned relationship. Legitimacy theory-based research posits that in settings where institutions remain weakened and where corruption is rampant, ESG reporting could be used as a strategic smoke-screen to promote the very unethical practices that it stands to unveil (Albitar et al., 2023; Mooneeapen et al., 2022; Marzouki et al., 2024). That is, ESG reporting can potentially be manipulated to hide unethical behaviors, and instead becomes prominently more of a symbolic legitimacy artifact rather than a substantial promise. Hence, in such vulnerable contexts, corporations could utilize ESG endeavors to the end of distracting regulatory attention and focus away from themselves, conserving political connections, and enhancing reputational legitimacy, while all the while carrying on with the corrupt behaviors. This may create a fear of a kind of “greenwashing” or the so-called “ethics-washing,” where ESG performance is over-exaggerated to no end to in order to hiding different forms of corporate misconduct. Findings from Marzouki et al. (2024) and Wei et al. (2024) illustrate that high-risk conditions for corruption correlate with lower transparency and negligible ESG disclosure credibility. These contrasting findings suggest that the ESG–corruption relationship is highly context-dependent and contingent upon the surrounding institutional quality, enforcement strength, and internal governance effectiveness, which helps explain the inconsistency observed across prior studies (Ghazwani et al., 2025).

Recent studies in emerging markets, including the GCC region, have placed focus on the dual function of ESG both in limiting corporate wrongdoing on the one hand, and allowing firms to hide corruption on the other based on the institutional framework (L. Aladwey et al., 2025; Ghazwani et al., 2025; Marzouki & Ben Amar, 2025; Pham & Yang, 2025). Therefore, while ESG commitments support anti-corruption efforts via stakeholder communication, reputational development, and long-term value creation, they could strategically be used in the form of a legitimacy tool in very corrupt settings. This unresolved dilemma then signals a need for more empirical research and testing, especially in those settings where governance systems, regulatory supervision, and board oversight are still developing and continuing to advance.

Based on the above discussions, this research holds the view that effective ESG performance should positively improve the effectiveness of corporate risk management of corruption by building transparency and accountability and reinforcing congruence with social expectations. Accordingly, and in line with agency and legitimacy assumptions under effective governance conditions, it hypothesizes the following:

H1. 

ESG performance is positively associated with corporate corruption risk management.

2.2.2. The Moderating Role of Risk Management Committees

Corporate governance structures are a key determinant of how companies incorporate ESG performance and management of corruption risk. Although ESG commitments can potentially enhance transparency and accountability, their effectiveness frequently relies on whether companies have dedicated risk-monitoring governance mechanisms. One of these mechanisms is establishing a specialized risk management committee (RMC). RMCs have increasingly become a risk-monitoring mechanism, as dedicated committees for tracking various kinds of risk, such as operational risk, environmental risk, ethical risk, and corruption-related risks (Hasan et al., 2023; Jia et al., 2019).

Historically, risk oversight has been the duty of audit committees. Nevertheless, researchers postulate that the widening workload of the audit committee and its expanding workload, which mainly concentrate on financial reporting and audit oversight, constrains its ability to handle wider risk exposures like corruption or ESG-associated risk (Brown et al., 2009; Karim et al., 2024). Hence, a spade of scholars has advocated for the establishment of a stand-alone RMC to help boost companies’ capabilities for managing various forms of corporate risks, especially non-financial risk, by guaranteeing expert knowledge that is technical and specialized and enhanced monitoring mechanisms (Subramaniam et al., 2009; Nahar et al., 2020). From the agency theory standpoint, having an RMC minimizes information asymmetry and managerial opportunism by facilitating better flow with higher quality of risk information to the board (Jensen & Meckling, 1976). Likewise, from a legitimacy theory perspective, the existence of RMCs can boost companies’ legitimacy by showing sound structures of governance aligned with the expectations of society for ethical business practices (Peters & Romi, 2014). These dual theoretical perspectives suggest that an RMC can operate not only as a monitoring mechanism but also as legitimacy-enhancing mechanism, thereby offering a plausible explanation for how governance structures may identify the ESG–corruption relationship.

Recent research findings confirm that the presence of RMCs improves the quality of risk reporting and accountability. Nahar et al. (2020) found a substantial association between the existence of RMCs and risk reporting, and Jia et al. (2019) demonstrated that stand-alone RMCs have a fundamental role to play in superior internal monitoring and oversight quality. In the context of ESG, Ardianto et al. (2024) provided evidence that RMCs provide higher environmental disclosure quality in addition to offsetting reputational risk related to inadequate sustainability performance. Applied to the governance of corruption risk, this likely also suggests that companies with an effective RMC have a clearer ability to ensure that ESG performance is considered as tangible governance processes that limit corruption risk. In other words, RMCs are likely to serve as a moderating factor that enhances the positive impact of ESG performance in containing the risk of corruption by putting in place permanent oversight, ongoing risk review, and reactive risk tools that align with both stakeholder expectations and regulatory requirements.

However, the available literature also quotes contradictory findings on the effectiveness of availability of a stand-alone RMC. Certain studies (Elamer & Benyazid, 2018; Abubakar et al., 2018) document that the existence of RMCs will not always translate into high governance and oversight performance, but at times size or independence will lower effectiveness due to coordination expenses or conflicting interests. This also adds strength to the argument of the necessity of empirically determining if indeed RMCs fortify the ESG–corruption risk management link in heterogeneous institutional settings, and, in particular, emerging markets such as the GCC region, where the adoption of sustainability and governance reform measures remains in a nascent stage.

Based on these findings, we posit that although ESG performance could complement anti-corruption risk management through fulfilling ethics and disclosure, the existence of a risk management committee complements this link by having specialized supervision, helping to reduce the asymmetry of information, and endorsing corporate governance processes. Therefore, we put forward the following hypothesis:

H2. 

The presence of a risk management committee positively moderates the relationship between ESG performance and corporate corruption risk management.

3. Methodology and Methods

3.1. Sampling and Data Collection

This research examines the influence of ESG performance on corporate risk management of corruption, with a consideration of the moderating role of the RMC among GCC-listed non-financial companies in GCC countries, namely Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain, during 2015–2024. Data were drawn mainly from the Refinitiv Eikon (LSEG) database, which has standardized, country-to-country comparable data on ESG indicators, anti-corruption procedures among corporate governance, and corporate performance. This database selection goes in tandem with previous studies (L. Aladwey et al., 2025; Marzouki & Ben Amar, 2025; Pham & Yang, 2025), owing to its broad coverage and credibility in governance and sustainability studies. Data on the existence of RMCs were primarily gathered from companies’ annual reports. In order to complete the data, any missing firm-level data was manually verified on a two-way basis with annual reports and stock exchanges filings where available.

Initially, the data set contained 655 listed companies in the GCC markets. After following regular data screening processes, financial institutions and government-affiliated companies were excluded to account for their unique reporting approaches and regulatory systems. This exclusion aligns with prior ESG governance studies that limit samples to non-financial sectors to avoid model distortion and ensure comparability (Jia et al., 2019; Hasan et al., 2023; L. Aladwey et al., 2025; Marzouki & Ben Amar, 2025; Pham & Yang, 2025). Accordingly, results should be interpreted within the context of non-financial firms, while generalization to financial institutions should be approached cautiously.

Additionally, companies with missing data were excluded. After applying these filters, the end-balanced panel consisted of 295 non-financial companies, with a total of 2950 firm-year observations. In keeping with data integrity procedures, no interpolation or imputation was undertaken on missing data. Instead, a list-wise elimination procedure was implemented using firms that had complete yearly data for the study variables. Following prior research (Elgharbawy & Aladwey, 2025; R. I. A. Salem et al., 2023), all continuous variables of the study were winsorized using the 1st and 99th percentile to reduce the influence of the outliers and obtain a robust result.

As outlined in

Table 1. Sample selection and breakdown by country and industry.

Panel A: Sample Selection
Sample		No. of Firms	No. of Observations
Initial sample: All listed non-financial firms in GCC countries		655	6550
Less: Firms with missing ESG or financial data		(283)	(2830)
Less: Financial institutions and government-linked firms excluded		(77)	(770)
Final sample		295	2950
Panel B: Country-Wise Distribution of Sample
Country	Population	Final Sample	Percentage (%)
Saudi Arabia	320	135	45.8
United Arab Emirates	110	52	17.6
Qatar	65	28	9.5
Kuwait	80	35	11.9
Oman	60	28	9.5
Bahrain	20	17	5.8
Total	655	295	100
Panel C: Industry Distribution of Sample
Industry	Firms (N)	%
Energy	25	8.5
Industrials	47	15.9
Basic Materials	33	11.2
Real Estate	42	14.2
Consumer Discretionary	39	13.2
Consumer Staples	26	8.8
Technology	22	7.5
Utilities	15	5.1
Healthcare	17	5.8
Telecommunications	29	9.8
Total	295	100

Source: Author’s own work.

, Panel A details the sample selection process, Panel B reports the distribution by country, and Panel C details the distribution by industry. Saudi Arabia and the UAE represent the largest share of the sample, in line with their leading economy and capital market sizes amongst the GCC countries. The sample captures industry diversity in that companies are represented across the industrial, basic materials, energy, real estate, consumer goods, and technology sectors, giving a complete picture of the non-financial activity spread in the Gulf. This sampling procedure is in conformity with the research depth in recent ESG and governance research in emerging markets (Ghazwani et al., 2025; Karim et al., 2024); the results are robust, comparable, and generalizable in the GCC context.

3.2. Variables and Measurement

This study examines the impact of levels of ESG performance on corporate corruption risk management (CCRM), taking into consideration the moderating role of the presence of the risk management committee (RMC). For robustness purposes, the model controls for various firm-specific attributes, governance-related characteristics, especially those linked to the audit committee, and macroeconomic factors like GDP growth and the Corruption Perceptions Index (CPI). The variables involved, their operational definitions, measurement technique, sources of data, and references to support each one are briefly outlined in

Table 2. Variables measurement.

Panel A. Main Variables	Description	Measurement	Data Source	References
ESG Performance (ESG)	Independent variable measuring a firm’s environmental, social, and governance performance.	ESG combined score ranging from 0 to 100, calculated by Refinitiv Eikon based on weighted aggregation of environmental, social, and governance pillars. Higher values indicate superior ESG performance.	Refinitiv Eikon (LSEG)	Marzouki et al. (2024); Pham and Yang (2025); L. Aladwey et al. (2025)
Corporate Corruption Risk Management (CCRM)	Dependent variable capturing the firm’s ability to prevent, monitor, and control corruption-related activities.	Composite score based on six indicators: anti-corruption policies, anti-bribery commitments, code of conduct, employee training, reporting tools, and anti-corruption processes. The score ranges from 0 to 6. Higher values indicate stronger corruption risk management.	Refinitiv Eikon (LSEG)	La Rosa et al. (2022); Marzouki and Ben Amar (2025)
Risk Management Committee (RMC)	Moderating variable assessing the presence of a dedicated board-level committee responsible for overseeing risk-related matters.	Dummy variable equal to 1 if a stand-alone RMC exists, 0 otherwise.	Annual reports	Jia et al. (2019); Karim et al. (2024); Hasan et al. (2023)
Panel B. Control Variables	Description	Measurement	Data Source	References
Firm Size (SIZE)	Controls for firm visibility and resource capacity.	Natural logarithm of total assets.	Refinitiv Eikon; Annual reports	Pham and Yang (2025); L. Aladwey et al. (2025)
Leverage (LEV)	Captures financial risk and capital structure.	Total debt divided by total assets.	Refinitiv Eikon; Annual reports	Marzouki and Ben Amar (2025)
Profitability (ROA)	Proxy for firm performance and efficiency.	Net income divided by total assets.	Refinitiv Eikon; Annual reports	Ghazwani et al. (2025)
Audit Committee Independence (ACI)	Captures oversight quality in governance structure.	Percentage of independent members on the audit committee.	Annual reports	Hasan et al. (2023); Karim et al. (2024)
Audit Committee Size (ACS)	Reflects governance structure scale.	Total number of members on the audit committee.	Annual reports	Nahar et al. (2020); Jia et al. (2019)
Audit Committee Meetings (ACM)	Proxy for monitoring activity and intensity.	Number of audit committee meetings per year.	Annual reports	Hasan et al. (2023)
Panel C. Macroeconomic and Fixed Effects	Description	Measurement	Data Source	References
GDP Growth (GDPG)	Controls for economic performance across countries.	Annual GDP growth rate (%).	World Bank Development Indicators	Marzouki and Ben Amar (2025); Pham and Yang (2025)
Corruption Perceptions Index (CPI)	Proxy for national-level corruption environment.	Transparency International index ranging from 0 (high corruption) to 100 (low corruption).	Transparency International	Pham and Yang (2025)
Country Dummies	Controls for country-specific institutional effects.	Binary variables for each GCC country.	Author’s computation	Marzouki and Ben Amar (2025)
Industry Fixed Effects	Controls for sector-level differences in ESG and corruption exposure.	Binary variables based on ICB classification.	Refinitiv Eikon	L. Aladwey et al. (2025)
Year Fixed Effects	Captures time-specific macro shocks.	Binary variables for each year 2015–2024.	Author’s computation	Pham and Yang (2025)
COVID-19 Dummy	Controls for pandemic-related disruptions.	Equals 1 for years 2020–2021, 0 otherwise.	Author’s computation	L. Aladwey et al. (2025)

Source: Author’s own work.

.

3.2.1. Independent Variable: ESG Performance

The independent variable for this research is ESG performance that has been measured by using the ESG combined score of the Refinitiv Eikon (LSEG) database. This variable has been popular in recent sustainability and governance studies (Arayssi et al., 2019; Ben Fatma & Chouaibi, 2021; Elgharbawy & Aladwey, 2025; Marzouki et al., 2024). ESG combined score measures the overall performance of the company in the three pillars of the ESG performance (environmental, social, and governance) and it is normalized between 0 and 100; the higher the value, the greater the sustainability engagement and transparency of the company. Under Refinitiv’s approach, the score is constructed from over 120 key performance indicators taken from public disclosure sources like annual and sustainability reports. The overall score of ESG performance weights the three pillars performance with a generalized weighted mean to ensure that it remains balanced and to prevent distortions due to excessive performance by a single dimension (Pham & Yang, 2025).

This index encompasses both the efficacy and regularity of ESG-related disclosures and practices and is particularly well-suited to multinational studies in areas like the GCC, where disclosure guidelines and institutional forces might vary considerably (Alfalih, 2023; K. Hoang, 2022). In line with previous literature (Elgharbawy & Aladwey, 2025; Marzouki & Ben Amar, 2025), the ESG performance score is used as a proxy for the level of the company’s responsible commitment to sustainable practices and good governance.

3.2.2. Dependent Variable: Corporate Corruption Risk Management

The CCRM practices capture the level of how businesses design and implement systems to mitigate, identify, and react to corruption threats. Based on the works of La Rosa et al. (2022) and Marzouki and Ben Amar (2025) on corruption risk management, the approach of the study reverses the direction of their scale, where higher scores reflect better management of corruption and lower risks of corruption. The CCRM variable is extracted from the Refinitiv Eikon (LSEG) database and is based on six anti-corruption areas, which include (1) the presence of anti-corruption policies, (2) public declarations against bribery, (3) code of conduct provisions, (4) ethics and anti-corruption training for employees, (5) internal and external complaint mechanisms, and (6) monitoring and reviewing systems. Companies that better adopt total strategies in these areas have higher CCRM, indicating better management and ethical control mechanisms against corruption. Refinitiv Eikon automatically cross-verifies all the reported data against several sources, including annual reports, sustainability reports, and third-party data sets, to obtain reliability and comparability. Thus, the CCRM score that is generated will offer a consistent and transparent measure of a company’s ability to mitigate corruption risk within the overall framework of ESG governance.

3.2.3. Moderating Variable: RMC Presence

The moderating variable that will be used is the presence of an RMC that is essential for effective corporate governance practices. This variable is binary, taking the value 1 if the firm has a stand-alone RMC, and 0 if otherwise. Following previous research (Jia et al., 2019; Hasan et al., 2023; Karim et al., 2024), having a separate RMC reflects a more specialized risk oversight approach that differs from joint audit and risk committees. Companies that have stand-alone RMCs will have more structured risk disclosure, monitoring, and identification practices that will further amplify the influence of ESG performance on the management of corruption risk.

3.2.4. Control Variables

For the sake of ensuring the robustness of the relationship between ESG performance and CCRM, several control variables are incorporated at the firm, governance, and macroeconomic levels.

At the firm level, I also controlled for firm size, leverage, and profitability because these variables might affect both ESG engagement and corruption risk exposure (Pham & Yang, 2025; L. Aladwey et al., 2025). Larger and more profitable firms tend to receive greater public scrutiny as well as have more resources to implement sustainability and compliance mechanisms (Ghazwani et al., 2025). On the contrary, high leverage could restrict long-term ESG investments and raise operational risk exposure (Marzouki & Ben Amar, 2025). At the board level, we look at audit committee characteristics, such as board size, board independence, and meeting frequency, due to their instrumental value in facilitating transparency and accountability in reporting by companies (Nahar et al., 2020; Hasan et al., 2023). Previous research covered that efficient audit committees minimize informational asymmetry, improve internal controls and lower corruption risk (Karim et al., 2024). In this context, board-level attributes are excluded to prevent multicollinearity since AC mechanisms informatively cover governance oversight that is relevant to ESG performance and anti-corruption control.

In order to account for institutional and macroeconomic differences, we control for country dummies and macroeconomic controls like GDP growth and the Corruption Perceptions Index (CPI). They explain cross-country variation in governance structures, financial settings, and country integrity systems that could affect companies’ ESG and bribery risk treatment practices (Marzouki & Ben Amar, 2025; Pham & Yang, 2025). In conclusion, to mitigate unobservable heterogeneity and shocks over time, we control for industry and year fixed effects and use a COVID-19 dummy to account for possible pandemic impacts on the implementation of ESG engagement and corruption risk treatment practices. These manipulations make possible the validity and comparability of the model’s estimates over time and across industries.

3.3. Models and Estimation

To empirically test the study hypotheses, we estimate two panel regression models examining the effect of ESG performance on CCRM and the moderating role of the RMC among non-financial firms in GCC countries over the period 2015–2024. The analysis employs panel data techniques to account for firm-level heterogeneity and control for time- and industry-specific effects that could bias the results. Panel data analysis offers several advantages over cross-sectional models by controlling for unobserved firm-specific effects and mitigating omitted variable bias (Wooldridge, 2015). Following prior research (e.g., Eltweri et al., 2024; Pham & Yang, 2025; Marzouki & Ben Amar, 2025; Gerged et al., 2025), we begin by estimating the models using pooled OLS, random effects (REM), and fixed effects (FEM) estimators. Feasible Generalized Least Squares (FGLS) with clustered robust standard errors was utilized as it corrects both issues and is well-suited for panel data with cross-sectional variance (Wooldridge, 2015; Drukker, 2003). The Breusch–Pagan Lagrange Multiplier and Hausman tests guide the choice between fixed and random effects. The Hausman test (χ² = 27.84, p < 0.01) indicates that the fixed effects specification is preferred over the random-effects model, confirming the presence of correlation between the regressors and unobserved firm-specific effects.

Consistent with the fixed effects framework, the diagnostic tests were used to check the robustness. The Breusch–Pagan test was used for the presence of heteroskedasticity. However, the Wooldridge test was used for the presence of the first-order autocorrelation problem in the panel data (Breusch & Pagan, 1979). Both tests provided results that were statistically significant (p < 0.05) for violating the homoskedasticity condition and the condition of serial correlation. Consequently, the models were estimated using Feasible Generalized Least Squares (FGLS), as a correction estimator, with robust standard errors clustered at the firm level to address heteroskedasticity and serial correlation issues (Marzouki et al., 2024; Pham & Yang, 2025). To further address potential endogeneity and simultaneity, we additionally estimated the model using Dynamic GMM (Arellano & Bond, 1991; Roodman, 2009), where lagged values of ESG and CCRM are used as internal instruments. ESG and CCRM were also re-estimated in lagged form as robustness to ensure ESG precedes corruption risk outcomes. As an additional robustness check, a logistic regression (Logit) model was estimated using an alternative binary measure of corporate corruption risk management (CCRM).

The baseline model (Model (1)) examines the direct effect of ESG performance on corporate corruption risk management (CCRM):

$$\mathrm{Model}\left(1\right):\mathit{C}\mathit{C}\mathit{R}{\mathit{M}}_{\mathit{i}\mathit{t}}={\mathit{\beta }}_{\mathbf{0}}+{\mathit{\beta }}_{\mathbf{1}}\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}}+\sum {\mathit{\beta }}_{\mathit{k}}\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}{\mathit{l}}_{\mathit{k}\mathit{i}\mathit{t}}+{\mathit{\mu }}_{\mathit{i}}+{\mathsf{\lambda }}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

where $\mathit{C}\mathit{C}\mathit{R}{\mathit{M}}_{\mathit{i}\mathit{t}}$ denotes corporate corruption risk management for firm i at time t, $\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}}$ represents the ESG performance score, $\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}{\mathit{l}}_{\mathit{k}\mathit{i}\mathit{t}}$ is a vector of firm-specific, governance-related, and macroeconomic control variables, ${\mathit{\mu }}_{\mathit{i}}$ captures unobserved firm-specific effects, and ${\mathsf{\lambda }}_{\mathit{t}}$ represents time fixed effects.

Model (2) incorporates the moderating role of the risk management committee (RMC):

$$\mathrm{Model}\left(2\right):\mathit{C}\mathit{C}\mathit{R}{\mathit{M}}_{\mathit{i}\mathit{t}}={\mathit{\beta }}_{\mathbf{0}}+{\mathit{\beta }}_{\mathbf{1}}\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}}+(\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}}\times \mathit{R}\mathit{M}{\mathit{C}}_{\mathit{i}\mathit{t}})+\sum {\mathit{\beta }}_{\mathit{k}}\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}{\mathit{l}}_{\mathit{k}\mathit{i}\mathit{t}}+{\mathit{\mu }}_{\mathit{i}}+{\mathsf{\lambda }}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

The interaction term $(\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}}\times \mathit{R}\mathit{M}{\mathit{C}}_{\mathit{i}\mathit{t}})$ tests whether the presence of an effective risk management committee strengthens the positive impact of ESG performance on corruption risk management.

Both models control for firm-specific characteristics (size, leverage, and profitability), governance factors (audit committee size, independence, and meetings), macroeconomic variables (GDP growth, Corruption Perceptions Index), and the fixed effects of industry, year, and the COVID-19 period to isolate structural and temporal influences.

The CCRM variable is constructed such that a higher score represents stronger anti-corruption control, ensuring that a positive ESG–CCRM coefficient correctly reflects improvement in corruption risk governance, consistent with Sarhan and Gerged (2023) and Chen et al. (2022).

4. Analysis and Findings

4.1. Descriptive Analysis

Table 3. Descriptive statistics.

Panel A: Continuous Variables	Mean	Std. Dev.	Min	Max
CCRM—Corporate Corruption Risk Management	3.10	1.45	0	6
ESG—Environmental, Social, and Governance Performance	47.8	9.6	21.4	68.0
SIZE—Firm Size (Natural Log of Total Assets)	15.20	2.10	10.8	20.5
LEV—Leverage (Total Debt/Total Assets)	0.39	0.20	0.01	0.91
ROA—Return on Assets (Net Income/Total Assets)	0.066	0.079	−0.14	0.25
AC_IND—Audit Committee Independence (%)	71.6	17.8	25.0	100
AC_MEET—Audit Committee Meetings	4.20	1.90	0	12
ACS—Audit Committee Size	4.4	1.2	3	9
GDPG—Gross Domestic Product Growth (%)	3.15	2.05	−4.5	8.8
CPI—Corruption Perceptions Index	52.0	7.10	35.0	65.0
Panel B: Binary Variables	Frequency		Percentage (%)
RMC—Presence of a Stand-alone Risk Management Committee	180		61.4
Industry Fixed Effects	—		Controlled
Year Fixed Effects (including COVID-19 period)	—		Controlled
Country Dummies	—		Controlled

Source: Author’s own work. Note: All continuous variables were winsorized at the 1st and 99th percentiles to reduce the influence of outliers. In addition, the regression models later in this study incorporate country, industry, and year fixed effects (including the COVID-19 period) to control for unobservable heterogeneity across firms, sectors, and time.

presents the descriptive statistics for all variables employed in the empirical analysis. Panel A reports the summary measures (mean, standard deviation, minimum, and maximum) for continuous variables, while Panel B presents the distribution of binary variables. The results indicate that the average CCRM score is 3.10, suggesting that most firms in the sample have implemented a moderate level of anti-corruption mechanisms, such as codes of conduct, employee training, and reporting tools. This deviation (Std. = 1.45) suggests a high level of dissimilarity in anti-corruption risk management practices among GCC non-financial companies.

The mean value of ESG performance is 47.8 with a minimum of 21.4 and maximum of 68.0, which corresponds to previous results in emerging markets and suggests increasingly asymmetrical sustainability coverage among companies (Marzouki & Ben Amar, 2025; L. Aladwey et al., 2025). Average firm size, as measured by the natural logarithm of total assets, is 15.20, which implies medium-to-large listed firms in the sample. The mean leverage ratio of 0.39 also indicates that majority of the firms depend to some extent on debt financing, and the mean return on assets (ROA) of 6.6% also indicates stable profitability in the research timeframe. In the case of firm-specific governance variables, audit committees have high independence levels (mean = 71.6%) and converge on average 4.2 times annually, conforming to international benchmarking (Albitar et al., 2023; Karim et al., 2024). At the aggregate level, the overall GCC mean GDP growth stands at 3.15%, with high volatility during the 2015–2024 interval. A moderate level of integrity and transparency characterizes the mean Corruption Perceptions Index (CPI) value of 52, in line with international governance indicators for the region.

Panel B reveals that around 61% of companies have a stand-alone RMC, reflecting an increased focus on formal risk oversight designs in the aftermath of the GCC’s corporate governance reforms.

4.2. Correlation and Multicollinearity Analysis

Table 4. Correlation matrix and multicollinearity diagnostics.

Panel B—Multicollinearity Diagnostics			Panel A—Pearson Correlation Matrix
VIF	Tol.	Variable	(1) CCRM	(2) ESG	(3) SIZE	(4) LEV	(5) ROA	(6) AC _IND	(7) AC _MEET	(8) ACS	(9) GDPG	(10) CPI	(11) RMC
1.31	0.76	CCRM	1.000
1.67	0.60	ESG	0.42	1.000
1.95	0.51	SIZE	0.19	0.33	1.000
1.52	0.66	LEV	−0.09	−0.12	0.23	1.000
1.44	0.69	ROA	0.22	0.29	0.18	−0.41	1.000
1.36	0.74	AC_IND	0.27	0.25	0.09	−0.05	0.12	1.000
1.29	0.78	AC_MEET	0.14	0.17	0.08	−0.04	0.09	0.26	1.000
1.22	0.82	ACS	0.17	0.20	0.17	−0.06	0.18	0.11	0.07	1.000
2.10	0.48	GDPG	0.18	0.21	0.28	0.17	0.11	0.15	0.06	0.16	1.000
2.212	0.45	CPI	0.24	0.26	0.30	0.14	0.16	0.22	0.10	0.21	0.63	1.000
1.38	0.72	RMC	0.31	0.28	0.17	−0.06	0.18	0.23	0.11	0.18	0.16	0.21	1.000

Source: Author’s own work.

reports Pearson correlation coefficients for the main study variables (Panel A) and multicollinearity diagnostics (VIF and tolerance) in Panel B. The correlation matrix shows generally low-to-moderate associations among explanatory variables. The highest pair-wise correlation remains between GDP growth (GDPG) and the Corruption Perceptions Index (CPI) (r = 0.63), while the correlation between ESG performance and CCRM is positive and moderate (r = 0.42). Importantly, none of the pair-wise correlations exceed the common threshold of 0.70, alleviating concerns about severe multicollinearity (Marzouki et al., 2024; Pham & Yang, 2025). Panel B reports Variance Inflation Factor (VIF) and tolerance values for the regressors. All VIF values are below 5, with a maximum of 2.212 for CPI and a mean VIF of 1.81. Corresponding tolerance statistics are all comfortably above conventional cut-offs (minimum tolerance ≈ 0.45), confirming that multicollinearity is not a material concern for our regression analyses (Le et al., 2023; Van Hoang et al., 2023).

4.3. Hypotheses Testing: Results of Feasible Generalized Least Squares (FGLS)

Table 5. Results of Feasible Generalized Least Squares (FGLS).

	Model (1): Baseline (CCRM)		Model (2): Moderation (CCRM)
Variables	Coef.	t-Value	Coef.	t-Value
ESG Performance (ESG)	0.112 ***	2.87	0.094 **	2.41
Risk Management Committee (RMC)	—		0.163 **	2.12
ESG × RMC	—		0.127 ***	2.98
Firm Size (SIZE)	0.071 **	2.12	0.066 **	2.05
Leverage (LEV)	−0.079 *	−1.73	−0.076 *	−1.69
Profitability (ROA)	0.119 ***	3.32	0.114 ***	3.26
Audit Committee Independence (AC_IND)	0.086 **	2.14	0.083 **	2.08
Audit Committee Meetings (AC_MEET)	0.092 **	2.24	0.088 **	2.19
Audit Committee Size (ACS)	0.059 *	1.74	0.057 *	1.70
GDP Growth (GDPG)	0.061	1.44	0.066	1.51
Corruption Perceptions Index (CPI)	0.107 **	2.42	0.104 **	2.36
Constant	1.745 ***	4.91	1.629 ***	4.67
Year FE	Yes		Yes
Industry FE	Yes		Yes
COVID-19 FE	Yes		Yes
Country Dummies	Yes		Yes
N (Obs.)	2950		2950
Adjusted R2	0.427		0.462
F-statistic (overall)	17.82 ***		20.41 ***

Source: Author’s own work. Note: t-statistics in parentheses. ***, **, * denote significance at 1%, 5%, and 10% levels, respectively.

presents the FGLS regression results examining the influence of ESG performance on CCRM and the moderating role of the RMC. In Model (1), ESG performance is positive and highly significant (β = 0.112, t = 2.87, p < 0.01), supporting H1 that firms with superior ESG engagement are more capable of preventing and managing corruption risks. This indicates that ESG-oriented firms are characterized by stronger ethical infrastructures and governance transparency (Pham & Yang, 2025; Marzouki & Ben Amar, 2025). In Model (2), both the direct effect of the RMC (β = 0.163, t = 2.12, p < 0.05) and the interaction term ESG × RMC (β = 0.127, t = 2.98, p < 0.01) are statistically significant and positive, confirming that H2 was upheld. These findings reveal that the presence of a dedicated risk management committee enhances the positive influence of ESG performance on corruption risk management. Companies that have RMCs in place have stronger oversight controls, greater risk perceptions, and better integration of ethical governance in their strategic choices (Jia et al., 2019; Karim et al., 2024).

The control variables perform mostly as hypothesized: larger profitable companies with independent and active audit committees have better corruption risk management. Model fit rises when using the moderating term (R² = 0.462) for the joint significance of ESG integration and risk governance in building corporate integrity for GCC non-financial companies.

4.4. Robustness Checks

4.4.1. Robustness Test Using Individual ESG Pillars

In order to confirm the stability of the primary results and to check the results’ sensitivity to other specifications, this research re-estimates the base and interaction models by breaking down the total ESG performance score into its own three separate pillars: environmental (E), social (S) and governance (G). This methodology enables more detailed analysis of whether the positive influence of ESG performance on CCRM equally results from all dimensions or is concentrated in particular aspects of sustainability. This second analysis conforms with methodological prescriptions by H. V. Hoang et al. (2024) and Marzouki et al. (2024), who make the point that aggregating ESG scores may obscure distinct institutional, regulatory, and ethical dynamics underpinning each dimension. For example, environmental disclosure evidences regulatory adherence and business openness; social disclosure embodies companies’ morality toward staff and societies; and governance disclosure signifies board efficiency and commitment towards anti-corruption (Pham & Yang, 2025).

The results, summarized in

Table 6. Robustness test using individual ESG pillars.

Variables	Model (3a): Environmental (E)	Model (3b): Social (S)	Model (3c): Governance (G)
E (Environmental Score)	0.048 *** (2.07)	—	—
S (Social Score)	—	0.071 ** (2.49)	—
G (Governance Score)	—	—	0.086 *** (2.92)
Size	0.063 ** (2.38)	0.059 ** (2.31)	0.061 ** (2.27)
Leverage	−0.034 (−1.15)	−0.028 (−0.97)	−0.031 (−1.04)
Profitability (ROA)	0.052 ** (2.11)	0.049 ** (2.02)	0.050 ** (2.05)
AC Size	0.028 * (1.83)	0.025 (1.58)	0.032 * (1.94)
AC Independence (%)	0.036 (1.42)	0.038 (1.51)	0.040 * (1.68)
AC Meetings (≥4)	0.041 * (1.75)	0.039 * (1.69)	0.045 ** (2.04)
GDP Growth (%)	0.018 (1.03)	0.021 (1.17)	0.020 (1.09)
Corruption Perceptions Index (CPI)	0.064 *** (3.05)	0.067 *** (3.18)	0.069 *** (3.26)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
COVID-19 FE	Yes	Yes	Yes
Constant	2.441 *** (6.32)	2.501 *** (6.45)	2.538 *** (6.58)
Observations	2950	2950	2950
Adjusted R2	0.354	0.372	0.398
F-statistic	16.72 ***	18.31 ***	20.04 ***

Source: Author’s own work. Note: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.10. All models estimated using Feasible Generalized Least Squares (FGLS) with robust standard errors clustered at the firm level. Industry, year, and COVID-19 fixed effects are included in all specifications.

, illustrate that all pillars of the ESG have a positive and statistically significant correlation with CCRM, although coefficients significantly vary with pillars. Pillar G reveals the highest influence (β = 0.086, p < 0.01), followed by pillar S (β = 0.071, p < 0.05), while pillar E reveals a moderate and statistically significant influence (β = 0.048, p < 0.10). These results imply that GCC companies’ risk of corruption is especially sensitive and responsive to governance value and social accountability procedures, in line with institutional regional focus on adherence, openness, and trust with stakeholders. In general, these tests of robustness validate that the previous findings resist the unique measurement of ESG performance. The stable importance in all three pillars supports that sustainability engagement, especially in governance and social areas, has an important function in enhancing corporate corruption risk management practices.

4.4.2. Robustness Test Using an Alternative Measure of CCRM

In order to further ensure the strength of and faith in the primary results, we utilize a substitute indicator of CCRM based on the study of L. Aladwey et al. (2025). In particular, they measure corporate misconduct using a binary proxy variable. In line with Sarhan and Al-Najjar (2023), Sarhan and Cowton (2025), and L. M. A. Aladwey and Diab (2025), corporate misconduct encompasses controversies related to bribery, corruption, and fraud covered in the Refinitiv Eikon database. The firm is considered to engage in corporate misconduct if it has been reported in the media for any incidents of bribery, political donations, illegal lobbying, money laundering, parallel imports, or tax evasion. However, in my research, I reverse the coding scheme to make it suitable for the idea of effective corruption risk management, assigning a value of 1 if a firm uses such risk management practices in dealing with the controversies, and 0 if it does not.

Since the dependent variable is binary, the robustness analysis has been predicted with logistic regression (Logit) including firm-level clustered standard errors with controls of the same firm-specific, governance, and macroeconomic determinants considered at the base models. This variant agrees with the dichotomous nature of the variable and enables testing the linearity consistency of the ESG–CCRM relationship in a non-linear environment. Untabulated model fit measures (Hosmer–Lemeshow, LR test, and ROC curve) validate the good specification and robustness of the logistic model with supporting stability and validity in the main identified relationships in the base FGLS and DGMM analysis.

The logistic regression in

Table 7. Robustness test using binary measure of CCRM (logistic regression results).

Variables	Coefficient (z-Stat)
ESG Performance	0.314 *** (3.02)
RMC Presence	0.229 ** (2.16)
ESG × RMC	0.127 *** (2.68)
Firm Size	0.112 ** (2.21)
Leverage	−0.084 (−1.29)
Profitability (ROA)	0.095 *** (2.77)
AC Size	0.074 * (1.91)
AC Independence (%)	0.061 (1.48)
AC Meetings (≥4)	0.056 * (1.72)
GDP Growth (%)	0.018 (1.06)
Corruption Perceptions Index (CPI)	0.144 *** (3.42)
Industry FE	Yes
Year FE	Yes
COVID-19 FE	Yes
Constant	−2.573 *** (−4.88)
Observations	1710
Pseudo R2	0.284
Wald χ2	142.63 ***

Source: Author’s own work. Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.10.

also establishes the robustness of the overall conclusions. ESG performance continues to be significantly and positively related to corporate risk management of corruption (p < 0.01). Having an RCM also exhibits a significantly positive influence (p < 0.05), and this supports the effectiveness of specialized oversight in checking corruption-related weaknesses. Notably, the interaction term (ESG × RMC) continues to be positive and significant at the 1% level, and this establishes that companies that have good ESG engagement and an active RMC have higher propensity to have effective systems to manage corruption risk.

4.5. Endogeneity Test: Difference Generalized Method of Moments (DGMM) Regression

The potential endogeneity problem in this research could arise from two sources. First, the two-way relationship between CCRM and ESG performance implies that companies with high ESG performance also have superior anti-corruption measures and so forth (Marzouki et al., 2024). Second, the existence of the RMC may both result in and contribute to more stringent governance and sustainability efforts. In an attempt to correct these potential feedback and simultaneity problems, the DGMM estimator was used and the models had to be re-estimated with lagged ESG performance and CCRM variables as instruments. In order to ensure the stability of the findings and to resolve endogeneity, dynamic persistence, and reverse causation issues, we used the two-step DGMM estimator proposed by Arellano and Bond (1991) and later advanced by Roodman (2009). This setting becomes appropriate for short T–large N panel data, like our data set, and remedially corrects simultaneity bias and unobserved firm-specification heterogeneity. In this set-up, the independent and dependent variables’ level lags have been considered in this context as internal instrumental generating parameter estimates that are persistent (Marzouki et al., 2024; L. Aladwey et al., 2025; Pham & Yang, 2025).

Table 8. Results of DGMM regression: addressing endogeneity and dynamic effects.

Panel A. Two-Step DGMM Estimation Results (Dependent Variable: CCRM)	Coefficient	Std. Error	z-Value	Significance
Lagged CCRM	0.312	0.073	4.27	*** (p < 0.01)
ESG Performance	0.085	0.039	2.18	** (p < 0.05)
Risk Management Committee (RMC)	0.142	0.066	2.15	** (p < 0.05)
ESG × RMC	0.103	0.044	2.34	** (p < 0.05)
Firm Size (SIZE)	0.055	0.031	1.77	* (p < 0.10)
Leverage (LEV)	−0.049	0.024	−2.04	** (p < 0.05)
Profitability (ROA)	0.089	0.028	3.18	*** (p < 0.01)
Audit Committee Independence (AC_IND)	0.067	0.029	2.31	** (p < 0.05)
Audit Committee Meetings (AC_MEET)	0.071	0.032	2.22	** (p < 0.05)
Audit Committee Size (ACS)	0.041	0.024	1.71	* (p < 0.10)
GDP Growth (GDPG)	0.038	0.026	1.46	-----
Corruption Perceptions Index (CPI)	0.094	0.041	2.29	** (p < 0.05)
Constant	1.102	0.347	3.18	*** (p < 0.01)
Panel B. Diagnostics and Specification Tests
Number of Firms	295
Firm-year Observations	2950
AR(1) in First Differences (z)	−2.92 (p = 0.003)
AR(2) in Dirst Differences (z)	−0.81 (p = 0.21)
Hansen J (Overidentification) Statistic	18.27 (p = 0.31)
Wald χ2 (Overall Model Fit)	154.72 (p < 0.001)
Estimation Type	Two-Step DGMM (Windmeijer-corrected SEs)
Significance Levels	*** p < 0.01, ** p < 0.05, * p < 0.10

Source: Author’s own work.

presents the DGMM results. The coefficient of lagged CCRM is positive and significant (β = 0.298, p < 0.01). This confirms that corruption management efforts are present and stable over time. ESG performance also continues to exhibit a positive and significant impact on CCRM (β = 0.079, p < 0.05). This hence reinforces that sustainability-oriented firms are more likely to institutionalize anti-corruption measures. Importantly, the interaction term (ESG × RMC) remains positive and statistically significant (β = 0.097, p < 0.05). This emphasizes that the presence of a dedicated RCM strengthens the positive influence of ESG performance on managing the risk of corporate corruption. Consistent with Le et al. (2023), the diagnostic tests support the validity of the DGMM estimation. The AR(1) test confirms expected first-order serial correlation (p < 0.01), while the AR(2) test is insignificant (p = 0.226), indicating no second-order autocorrelation. The Hansen J-statistic (p = 0.308) and Sargan test (p = 0.244) confirm the validity of the chosen instruments and the absence of overidentification bias. Additionally, the Wald χ² test (p < 0.001) confirms the joint significance of the explanatory variables, ensuring the robustness of the model.

DGMM estimation results confirm the overall main regression findings, thereby supporting that ESG performance positively and significantly contributes to CCRM and that the moderating function of the RMC reinforces this connection even more. Such findings validate those of Pham and Yang (2025), who registered comparable moderating functions of ethical and governance structures in ESG–corruption risk management processes. Continued existence of the dependent variable in the previous period also corresponds with the work of L. Aladwey et al. (2025), which holds that corporate governance procedures exhibit persistence in the sense that they exist continuously over the years. In general, such tests of robustness validate that the overall results are not caused by endogeneity or simultaneity bias. DGMM estimates provide consistent and unbiased evidence of the positive influence of ESG performance on risk management of corruption and reinforce the function of risk governance procedures in GCC companies.

4.6. Discussion

This research sought to investigate how ESG performance influences corporate corruption risk management (CCRM) and how risk management committees (RMCs) partially moderate this relationship among non-financial firms in GCC nations during the years 2015 to 2024. Grounded in agency and legitimacy theories, the findings provide nuanced insights into how ESG practices and governance structures interact to mitigate corruption risks within emerging institutional settings.

The results of the estimations of baseline FGLS and DGMM regression show that there is a positive and significant link between ESG engagement and the management of corporate compliance with compact CCRM practices. This supports H1 of the study. This is consistent with the premise of agency theory, which regards better-managed ESG practices as minimizing agency problems because of increased transparency, accountability, and stakeholder scrutiny, which reduce the gaps between managers and shareholders, hence minimizing agency costs (Jensen & Meckling, 1976; Fama & Jensen, 1983; Pham & Yang, 2025). It appears that firms that are more actively engaged in better ESG practices internalize ethical behavior, improve internal control mechanisms, and signal their commitment to integrity, hence reducing the recurring opportunistic circumstances that fuel misconduct and corruption (Boura et al., 2020; Sarhan & Gerged, 2023). The findings are also in alignment with the perspective of legitimacy theory. The findings show that a sound ESG performance serves beyond only being a symbolistic instrument for legitimization, also being a substantive process by which companies uphold societal legitimacy. Thus, when companies show adherence to environmental and societal norms and expectations, they minimize the legitimacy gap and maximize trust of firms among stakeholders (Suchman, 1995; Deegan, 2019). In the GCC region, where institutional sustainability pressures continue to rise, this implies that ESG efforts are part of how companies establish reputational legitimacy and fulfill international governance expectations.

These findings are in line with previous studies conducted on emerging markets that observe that ESG engagement results in superior ethical conformity and superior anti-corruption outcomes (Chen et al., 2022). But this conclusion differs from the view of Albitar et al. (2023) and Marzouki et al. (2024) on “ethics-washing”, proposing that companies in heavily corrupt contexts utilize ESG reporting to hide unethical behaviors. Consistency in positive impacts across both continuous and binary measures of CCRM implies that in the GCC regional setting, ESG performance has gone from symbolic conformity to increasingly substantive governance change. This could be due to superior regulatory reform, increased investor diligence, and regional conformation with international sustainability agendas, and reflects serious commitment to the future economic visions of these countries.

The findings also suggest that having a stand-alone RMC significantly supports the positive impact of ESG performance on CCRM, in line with H2 of the research. This supports both the theory of agency and that of legitimacy. From the agency point of view, RMCs function as a dedicated governance tool that minimizes information asymmetry, amplifies the ability of the board to oversee, and guarantees that ESG practices have tangible anti-corruption control measures in place (Subramaniam et al., 2009; Nahar et al., 2020). Accordingly, through the use of attention and expertise on risk oversight, beyond the limitations of overwhelmed audit committees, RMCs strengthen the monitoring of non-financial and ethical risks, thus limiting the opportunistic behavior of managers, and consequently, enhancing accountability (Brown et al., 2009; Karim et al., 2024). On the other hand, the results emerging from the perspective of legitimacy theory also provide insight on the presence of RMCs as a legitimization tool, demonstrating efficient governance and ethical governance, and communicating this to external parties. The positive moderating effect of the presence of RMCs also provides insight that with efficient ESG practices, together with sound risk oversight, organizations can obtain legitimacy, especially when the institutional and anti-corruption environment is undergoing changes in the GCC (Peters & Romi, 2014; Ardianto et al., 2024).

Importantly, the findings of the moderation effects of the main model and the robustness model also imply that the role of RMCs not only amplifies the effectiveness of the governance structure and control, but also places the ethical risk management processes of the corporation in the foundations of institutionalized practices. This particular result offers concrete empirical support for the assertion made by Ng et al. (2012) and Jia et al. (2019), which proposed that the presence of stand-alone RMCs reveals the exceptional potential for dealing with diverse risk profiles, along with the enhancement of the quality of disclosures. This also refutes the skepticism brought forth by Elamer and Benyazid (2018) and Abubakar et al. (2018), suggesting the limited or negative impact of RMCs on business performance, because when modernization of the governance structures takes precedence, RMCs play a critical intermediating role between the concepts of sustainability and integrity.

5. Conclusions, Contributions, Implications, Limitations, and Future Research

5.1. Conclusions

This study advances understanding of how sustainability performance interacts with corporate governance structures to mitigate corruption-related risks in emerging markets, especially GCC countries. Drawing on agency and legitimacy theories, we examined the influence of ESG performance on corporate corruption risk management and the moderating role of the risk management committee among non-financial firms across GCC countries from 2015 to 2024. The empirical findings provide robust evidence that firms with stronger ESG performance demonstrate higher levels of corruption risk management, reflecting improved ethical accountability, transparency, and stakeholder alignment. Moreover, the results confirm that the presence of an RMC amplifies this positive relationship, highlighting its role as a specialized governance mechanism that strengthens oversight and ensures that sustainability practices are effectively translated into risk mitigation strategies.

The study contributes to the growing body of literature that bridges ESG performance, anti-corruption governance, and risk oversight. It underscores the need for firms and regulators in emerging markets to move beyond symbolic ESG commitments and to institutionalize governance mechanisms, such as stand-alone RMCs, that enhance integrity, transparency, and sustainable value creation.

5.2. Contribution and Implications for Policy and Practice

This research makes an original contribution by introducing the RMC as a moderating governance mechanism in the ESG–CCRM relationship. While previous studies have linked RMCs to general risk disclosure or environmental risk oversight, this study uniquely demonstrates that RMCs strengthen the translation of ESG commitments into effective corruption risk management. The moderating effect confirms that specialized board committees are not merely procedural structures but key instruments that institutionalize ethical accountability, thus advancing our understanding of RMCs as both an agency-control and legitimacy-enhancing mechanism. In doing so, the findings contribute to extending agency theory by evidencing how ESG practices reduce information asymmetry and managerial opportunism, while simultaneously enhancing stakeholder trust and legitimacy through transparent governance structures, which make a valuable contribution to legitimacy theory.

The study’s findings carry significant implications for policymakers, corporate boards, and investors seeking to enhance integrity, transparency, and sustainability across emerging markets. For policymakers and regulators, the results highlight the need to institutionalize and enforce specialized governance mechanisms, particularly stand-alone RMCs, within corporate governance frameworks. By formally embedding RMCs into corporate structures, regulatory bodies across GCC countries can improve the effectiveness of anti-corruption oversight and ESG integration. For corporate boards and executives, the findings underline the strategic value of integrating ESG performance into risk management systems. Firms should treat ESG not merely as a disclosure exercise but as a governance tool that mitigates ethical and reputational risks. Establishing well-resourced and independent RMCs can ensure that ESG goals are operationalized through rigorous internal controls and anti-corruption frameworks. Boards should also enhance coordination between RMCs and audit committees to prevent overlap and ensure comprehensive oversight of both financial and non-financial risks. For investors and stakeholders, the results provide empirical evidence that ESG performance is a reliable signal of ethical governance and reduced corruption exposure. Institutional investors can leverage ESG scores and RMC presence as part of their responsible investment screening criteria, differentiating between firms engaging in substantive sustainability practices and those engaging in symbolic “ethics-washing.” This has implications for investment risk assessment, portfolio diversification, and engagement strategies in markets undergoing governance transformation.

5.3. Limitations and Directions for Future Research

This empirical study provides valuable insights on the relationship between ESG performance, the management of corporate corruption risk, and the moderating effects of the existence of the RMC on corruption risk management. However, there are several limitations that highlight the relevance of future studies. Firstly, the study is based on secondary data provided by the Refinitiv Eikon database. Even though it is well-known for its level of reliability and comparability, it cannot provide the qualitative environment needed for the corruption management practices of the respective firms. Future studies should allow for the use of qualitative data, perhaps through surveys, for better evaluation of corruption management at the corporate level. Furthermore, the study only considers non-financial corporations within the GCC countries, which is considered an emerging environment characterized by specific institutional and governance features. Even though this consideration brings relevance, it limits the generalizability of the results for other regions. Cross-country investigation that considers developed and emerging markets could identify the role of institutional quality and regulatory development as moderators for the relationship between ESG and CCRM.

Additionally, the study uses panel regression estimators, namely FGLS and DGMM, to counter endogeneity and the problem of heteroskedasticity. However, these estimators make use of internal instruments that can potentially be used as a proxy for unseen corporate activity. Future studies should make use of exogenous instruments or quasi-natural experiments, for example, the enactment of the law in combating corruption or reforms of the ESG regulation. Additionally, the moderating variable of RMCs was explored specifically with regard to a structural approach, that is, the presence versus absence of the committee. Future studies could take the next step of analyzing the impacts of the attributes of the RMCs, for example, structure, independence, expertise, and frequency of meetings, for a better grasp of the relationship between the characteristics of RMCs and corruption risk management within the ESG context. Finally, future research could integrate behavioral and institutional perspectives to examine how board culture, national ethics norms, or leadership values shape the interplay between ESG practices and anti-corruption efforts. Such extensions would deepen theoretical insights and support the design of governance frameworks that not only comply with formal regulations but also internalize ethical and sustainability values across organizational levels.