Value Creation Through Environmental, Social, and Governance (ESG) Disclosures

Abstract

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals.

1. Introduction

In recent years, environmental, social, and governance (ESG) disclosure has gained significant attention in the global financial landscape, with companies increasingly recognizing its potential impact on value creation (Giannopoulos et al., 2022). This trend has extended to Islamic finance markets, including the Saudi Exchange (Bamahros et al., 2022), where Sharia-compliant companies operate under unique ethical and financial principles.

ESG disclosure involves the transparent reporting of a company’s environmental, social, and governance performance, providing stakeholders with insights beyond traditional financial metrics. While many studies have explored the relationship between ESG practices and value creation in conventional markets, the impact of ESG disclosure on Sharia-compliant companies remains an understudied area, particularly in the context of the Saudi Arabian market (Firmansyah et al., 2023; Kouaib, 2022).

Sharia-compliant companies differ fundamentally from conventional companies in their adherence to Islamic principles, which influence their operational, financial, and ethical practices. These firms must comply with guidelines derived from Islamic law, such as prohibitions on interest (riba), avoidance of investments in businesses related to alcohol, gambling, and pork, and the emphasis on risk-sharing and social justice. Consequently, their financing structures exclude conventional debt instruments, relying instead on equity-based or profit-and-loss sharing modes of financing. This distinct financial architecture and ethical framework impact their ESG practices and financial performance in ways that may differ significantly from non-Sharia-compliant firms.

The importance of studying ESG disclosure in Saudi Arabia is underscored by the country’s Vision 2030, which emphasizes sustainable development and economic diversification. As the Saudi Stock Exchange continues to evolve and introduce new ESG guidelines, understanding the relationship between ESG disclosure and value creation becomes crucial for companies and investors.

The existing literature on ESG and value creation has produced mixed results. While some studies suggest a positive relationship between ESG performance and financial outcomes (Ahmad et al., 2021; Carnini Pulino et al., 2022; Feng & Wu, 2023; Gholami et al., 2022; Y. Li et al., 2018; Mohammad & Wasiuzzaman, 2021; Rohendi et al., 2024; Ruan & Liu, 2021; Srour, 2022), others have found a negative (Abdi et al., 2022; Chong & Loh, 2023; Feng & Wu, 2023; Fuadah et al., 2022; Khandelwal et al., 2023; Palupi, 2023; Rohendi et al., 2024) or insignificant impact (Ahmad et al., 2021; Khandelwal et al., 2023; Mohammad & Wasiuzzaman, 2021; Rohendi et al., 2024). These inconsistencies highlight the need for context-specific research, especially in markets with unique characteristics like Saudi Arabia’s Sharia-compliant financial ecosystem (Alofaysan et al., 2024; Bamahros et al., 2022; Hussain et al., 2024; Kouaib, 2022).

The intersection of ESG principles and Islamic finance presents an intriguing study area. Both frameworks emphasize ethical and socially responsible practices, yet their integration and impact on value creation in Sharia-compliant companies remain largely unexplored. This research gap calls for a comprehensive examination of how ESG disclosure influences the value creation process in Islamic finance.

Investigating the impact of ESG disclosure on value creation in Saudi Arabia’s Sharia-compliant companies is particularly important given the country’s growing role in global financial markets and its commitment to sustainable development. Additionally, exploring the moderating effect of firm size on the relationship between ESG disclosure and value creation adds another layer of complexity to this research.

This research paper contributes to existing literature and practice in several important ways. First, it provides novel insights into the impact of ESG disclosure on value creation, specifically for Sharia-compliant companies listed on the Saudi Stock Exchange, addressing a significant research gap in the intersection of Islamic finance and ESG practices. By focusing exclusively on Sharia-compliant companies listed on the Saudi Stock Exchange, this study provides novel insights into the intersection of ESG disclosure, sustainability, and financial performance within an Islamic finance context—an area still underexplored in current literature. This focus not only highlights unique governance and social responsibility approaches embedded in Islamic finance but also contributes to a deeper understanding of sustainable business practices in emerging markets. Second, the study offers valuable empirical evidence on the effectiveness of the Saudi Exchange’s recently launched ESG disclosure guidelines, contributing to understanding their practical implementation and impact. This research explores the unique context of Saudi Arabia’s capital market, which is undergoing rapid transformation under Vision 2030, providing insights into how ESG practices align with and support national economic objectives. Third, the research enhances understanding of how ESG principles integrate with Sharia compliance requirements, potentially informing the development of more tailored ESG frameworks for Islamic financial markets. Fourth, by examining the relationship between ESG disclosure and value creation, the study provides practical implications for Sharia-compliant companies in Saudi Arabia, guiding their ESG reporting strategies and sustainable growth initiatives. Finally, the findings can inform policymakers and regulators in Saudi Arabia and similar markets on the effectiveness of current ESG guidelines and potential areas for improvement in promoting sustainable and ethical business practices.

The primary objective of this research is to examine the relationship between ESG disclosure and value creation in Sharia-compliant companies listed on the Saudi Stock Exchange while also investigating the moderating effect of firm size on this relationship. By addressing these objectives, the study seeks to answer the following research questions:

• To what extent does ESG disclosure impact the value creation of Sharia-compliant companies listed on the Saudi Stock Exchange?
• How does firm size moderate the relationship between ESG disclosure and value creation in these companies?

This research contributes to the growing body of literature on ESG and Islamic finance, offering a unique perspective on the interplay between sustainability practices and Sharia-compliant business models in the Saudi Arabian context. The findings will provide valuable insights for companies seeking to enhance their ESG practices while adhering to Islamic finance principles and for investors and policymakers aiming to promote sustainable and ethical business practices in the region.

As an overview, this paper reviews the existing literature in Section 2. Section 3 details the methods adopted for the research study, including the justifications for data sources, variables used, and the model tests conducted. Section 4 presents the study’s results and analyzes whether the hypotheses can be accepted or rejected. Section 5 discusses the interpretation of the study. The final section summarizes and concludes the paper and provides recommendations and implications for future research.

2. Literature Review and Development of Hypotheses

2.1. Theoretical Perspectives

Several theories can be adopted to assess how ESG disclosure is related to corporate performance (Firmansyah et al., 2023). The agency theory suggests that the relationship between ESG and firm performance is influenced by the dynamics between a company’s owners (principals) and its managers (agents) (Carnini Pulino et al., 2022; Chong & Loh, 2023). The agency theory suggests that ESG disclosure is a mechanism to reduce information asymmetry, mitigate agency costs, and encourage managers to act in the best interests of the owners (Carnini Pulino et al., 2022; Chong & Loh, 2023), which can lead to enhanced firm performance. The agency theory posits that managers with more information about the company than owners may act in their self-interest rather than the owners’ best interests. ESG disclosures can help to reduce this information asymmetry by increasing transparency. When companies provide information about their ESG practices, it can help mitigate agency costs that arise from the separation of ownership and control. This increased transparency allows owners to monitor managers more effectively and can lead to better corporate governance. Disclosing ESG information signals to the market that a company is committed to sustainability, which can positively impact its performance. When firms engage in responsible behavior and prioritize ESG, they can align the interests of agents with those of the principals, which can improve the company’s reputation and long-term financial performance (Mohammad & Wasiuzzaman, 2021). Managers may be motivated to enhance ESG performance in order to improve their reputation, secure investment opportunities, and achieve long-term corporate sustainability (Yin et al., 2023). By emphasizing ESG initiatives, managers can work towards long-term value creation, benefiting both shareholders and stakeholders.

The signaling theory suggests that ESG disclosure acts as a signal of a company’s commitment to sustainability, influencing stakeholder perceptions and ultimately impacting the firm’s financial performance. Companies that effectively communicate their ESG efforts can create a positive feedback loop, enhancing their reputation, attracting investments, and improving their overall financial health (Carnini Pulino et al., 2022; Gholami et al., 2022; Rohendi et al., 2024). The signaling theory suggests that the relationship between ESG and firm performance is influenced by how companies communicate their sustainability efforts to external stakeholders. Companies possess more information about their operations and sustainability practices than external stakeholders such as investors, customers, and the public do. The signaling theory suggests that through ESG disclosure, companies can reduce this information asymmetry by sharing voluntary information with the market. This act of disclosing information signals the company’s commitment to sustainability (Chong & Loh, 2023). ESG transparency acts as a signal of a firm’s credibility and commitment to sustainability to external stakeholders. By disclosing their ESG practices, companies signal that they are not solely focused on profits but also prioritize the values, norms, and social values of the community in which they operate.

Companies often invest resources to disclose favorable information about their sustainability commitments to provide stakeholders with information that may not be available otherwise. This information acts as a signal to external stakeholders. When a company communicates its long-term sustainability initiatives, it is signaling its commitment to society, the environment, and its stakeholders (Carnini Pulino et al., 2022).

The market may respond positively to strong ESG signals because it can be interpreted that the company is reducing its risk profile and is focused on long-term value creation. This can then result in improved financial performance. The market will reward companies that behave responsibly and engage in sustainability practices.

ESG disclosure can serve as a means to communicate that a company prioritizes environmental protection, social responsibility, and corporate governance (Yin et al., 2023). By signaling their commitment, companies can differentiate themselves from competitors, gain a competitive edge, and attract investors and customers who value sustainability (Mohammad & Wasiuzzaman, 2021). ESG disclosure can increase investor confidence in the company’s sustainable development, making it easier to secure investment opportunities. This, in turn, may provide a company with additional resources to implement further carbon emission reduction initiatives and improve its sustainability performance.

The signaling process also includes a feedback loop where the stakeholders’ reactions influence the company’s future actions. This feedback loop reinforces the importance of transparency and sustainable business practices.

2.2. ESG Disclosure and Firm Value Creation

Several studies have investigated the relationship between ESG disclosure and various measures of financial performance. While there is a growing body of evidence suggesting positive impacts of ESG disclosure on firm value, it is important to note that the relationship is complex and can be influenced by various factors, including the company’s specific context, industry, and stakeholders’ perceptions. The literature on ESG disclosure and its impact on firm value creation also reveals a complex relationship, with studies showing positive, negative, and neutral effects.

2.2.1. Positive Effects of ESG Disclosure on Value Creation

There is a sizable literature suggesting the positive relationship between good ESG disclosure and firm value creation (Ahmad et al., 2021; Carnini Pulino et al., 2022; Feng & Wu, 2023; Ruan & Liu, 2021; Srour, 2022). The positive effects are attributed to several reasons. To start with, ESG practices can bring a positive impact on a company’s financial performance (Ahmad et al., 2021; Gholami et al., 2022; L. Li et al., 2024), and a higher ESG performance disclosure score can lead to higher profitability. High ESG-engaged companies can also experience lower cost of financing and higher operating and financial stability (Ruan & Liu, 2021). Pérez Estébanez and Sevillano Martín (2025) demonstrate a positive relationship between sustainability and financial performance, particularly in high-development contexts in a medium-term horizon. The research contributes significantly to the literature by demonstrating that sustainability can be utilized to enhance ethical objectives while, simultaneously, enhancing financial performance. Carnini Pulino et al. (2022) focused on Italian companies and provided a positive association between ESG disclosure and firm performance using EBIT. The research is particularly interesting as it explores the impact of mandatory ESG disclosure. ESG strengths are associated with increased firm value (Fatemi et al., 2018). It can be through various channels, including improved management quality, lower risks, and improved shareholder value (Ahmad et al., 2021). Good ESG performance can lead to higher market recognition, which appeals to investors and reduces financing expenses (Zhou, 2024). Furthermore, good ESG profiles are likely to increase consumer trust and loyalty to the brand, which boosts market share and profitability. Furthermore, firms covered by an ESG index are discovered to have a higher firm value (Fatemi et al., 2018; Y. Li et al., 2018). Improving the quality of ESG information disclosure serves to significantly improve their enterprise value (Xiao, 2024).

ESG disclosure is associated with a firm’s competitive advantage in the sense that it communicates sustainable solutions to environmental and social issues (Mohammad & Wasiuzzaman, 2021). The competitive advantage of a firm increases when firms improve their ESG disclosures, with evidence that ESG disclosures can create a competitive advantage. In addition, ESG disclosure has a positive and significant impact on firm value via competitive advantage (Rohendi et al., 2024).

ESG disclosure has the potential to increase firm value by enabling enhanced transparency, accountability, as well as stakeholder trust (Feng & Wu, 2023; Fuadah et al., 2022; Rohendi et al., 2024). Additionally, increased ESG disclosure can enable REITs to achieve better access to capital markets and enhanced corporate financial flexibility (Feng & Wu, 2023).

ESG disclosure is able to improve stakeholder loyalty and long-term corporate value (Gholami et al., 2022). This is because it signifies that a company is concerned about environmental protection, social responsibility, and corporate governance, and hence it reinforces investors’ confidence (Rohendi et al., 2024).

Second, ESG disclosure is discovered to improve the reputation and operational performance of a firm (Gholami et al., 2022). Companies that implement ESG criteria successfully can experience greater stakeholder satisfaction, leading to better financial performance (Abdi et al., 2022).

Third, ESG certification is discovered to be associated with a lower cost of capital, and it is discovered to improve Tobin’s Q substantially (Wong et al., 2021). Wong et al. (2021) investigated Malaysian firms and found that ESG certification lowers a firm’s cost of capital while effectively increasing Tobin’s Q. This article confirms the benefits of an ESG agenda for stakeholders, even in developing and emerging economies. In addition, firms voluntarily associated with integrated reporting (IR) are more likely to enjoy better financial performance (Albitar et al., 2020). ESG disclosure can also facilitate investor trust in the investment strategies of a firm and allow a company to attract long-term investors (Ahmad et al., 2021).

Finally, ESG disclosure is associated with lower levels of idiosyncratic risk and higher market-to-book ratios (Mohammad & Wasiuzzaman, 2021). In addition to this, companies with solid ESG commitments realize higher stability and elasticity at the operating and finance levels. Appropriate ESG risk management strategies can even render a company more agile to respond to economic crises, thereby hedging the company’s systemic risks (Ruan & Liu, 2021).

2.2.2. Negative Effects of ESG Disclosure on Value Creation

However, not all the studies consistently give favorable results. There have been some studies that give negative correlations between ESG disclosure and certain determinants of firm value. First, there are arguments that environmental improvements are required to be costly to undertake for firms, and that they must conform to environmental and sustainability guidelines, leading to negative abnormal returns when more ESG-related information becomes available (Feng & Wu, 2023). There are investors who view ESG initiatives as a cost that lowers return (Khandelwal et al., 2023). Disclosure of nonfinancial information erodes the creation of company value, which results from meeting the needs of stakeholders imposed on the firm, and hence introduces other agency conflicts (Palupi, 2023).

Second, ESG disclosure might be negatively associated with company valuation, as investors may perceive extremely open ESG firms as undervalued stocks (Chong & Loh, 2023). This is likely because some investors might see ESG activities as a misallocation of capital, especially if they do not benefit shareholders directly (Khandelwal et al., 2023; Rohendi et al., 2024). The activities revealed within ESG reporting are too costly and detrimental to their interests. They would be more interested in investing, reducing market demand, and reducing the value of the company (Palupi, 2023). Moreover, studies have also confirmed that environmental disclosure reduces firm performance (Mohammad & Wasiuzzaman, 2021). García-Amate et al. (2023) analyzed the oil and gas industry and found that ESG controversies may have a negative moderating impact on ESG factors and corporate financial performance. This suggests that negative ESG events may be able to override the beneficial impacts of ESG disclosure.

There have been certain studies that have shown that corporate ESG performance disclosure is negatively related to financial performance (Gholami et al., 2022), and that ESG disclosure might negatively impact the performance of a firm (Fuadah et al., 2022). Gutiérrez-Ponce and Wibowo (2023) analyzed the correlation of ESG performance and Indonesian banking companies’ financial performance from 2010 to 2020. The results show that while the aggregate ESG score is negatively correlated with Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q (TBQ), individual ESG pillars yield varied results. The social pillar will have a strong positive impact on ROA and ROE, whereas governance will have a strong negative impact on TBQ.

Finally, some studies identified that higher ESG disclosure is related to higher return volatility and a lower likelihood of securing external financing (Feng & Wu, 2023). Furthermore, there is some evidence to suggest that environmental and social dimensions of ESG are associated with a decrease in a firm’s market-to-book ratio (Abdi et al., 2022).

2.2.3. No Significant Effect of ESG Disclosure on Value Creation

While many studies highlight a positive link between ESG disclosure and firm value, others report no significant impact. Mohammad and Wasiuzzaman (2021) found ESG disclosure improves performance in Malaysian firms, even when accounting for competitive advantage. Similarly, (Wahyuni et al., 2024) found positive effects for Indonesian firms. Rohendi et al. (2024), however, observed no direct effect but found a significant positive impact when competitive advantage was included as a mediator, suggesting an indirect relationship. Stakeholders may consider other value-related factors beyond ESG disclosure (Rohendi et al., 2024).

Some studies also find no clear link between ESG and firm performance (Ahmad et al., 2021; Khandelwal et al., 2023) and certain ESG components, such as environmental or governance aspects, may not significantly affect market value (Rohendi et al., 2024).

Various methodologies—SEM (Pérez Estébanez & Sevillano Martín, 2025), panel data analysis (Gutiérrez-Ponce & Wibowo, 2023), regression analysis (Carnini Pulino et al., 2022) and PLS-SEM (Rohendi et al., 2024)—highlight the complexity of this relationship across contexts.

The literature shows varied results across regions and regulatory contexts. For instance, Carnini Pulino et al. (2022) found a positive link between ESG disclosure and EBIT in Italy. Studies in emerging markets like Malaysia and Indonesia (Gutiérrez-Ponce & Wibowo, 2023; Mohammad & Wasiuzzaman, 2021; Wong et al., 2021) generally support the value-enhancing effect of ESG disclosure, though outcomes vary by ESG component and performance metric. While most research finds a positive relationship, differences across contexts, industries, and methods highlight the complexity of this link. Future research should explore how factors like location, regulation, and ESG dimensions shape this relationship.

2.3. ESG Disclosure and Firm Value Creation in Islamic Finance, in GCC and in the Saudi Arabian Context

2.3.1. ESG Disclosure and Firm Value Creation in Islamic Finance

Islamic finance and ESG investing share principles such as harm avoidance, long-term risk mitigation, social justice, and transparency (Raimi et al., 2024). Both promote ethical investing and sustainable development, though their frameworks differ. Literature reviews show a shift in Islamic finance from strict Sharia compliance toward broader ethical and socially responsible investing, highlighting its potential contribution to the SDGs.

Raimi et al. (2024) critically reviewed 48 studies, showing how Islamic Sustainable Finance (ISF) supports SDGs through tools like Green Sukuk, Islamic Microfinance, and Impact Investing, aligning financial stability with Islamic ethics. Similarly, Harahap et al. (2023), through a review of 65 publications, confirm Islamic finance’s role in promoting social welfare and sustainable development, especially in Asia, while calling for stronger regulatory support.

Sairally (2015) argues that ESG values are inherent to Islamic law and urges Islamic Financial Institutions (IFIs) to adopt long-term ESG strategies aligned with both Sharia and global standards to enhance their sustainability and appeal.

Abd-Elsalam and Binay (2024) explore how fatwas in Jordan reflect a growing integration of sustainability in Islamic theology, showing a shift toward a more comprehensive, responsibility-driven view of environmental stewardship in the MENA region.

2.3.2. ESG Disclosure and Firm Value Creation in GCC

Recent empirical research highlights the growing relevance of ESG practices in shaping firm performance across the GCC. Grassa et al. (2024) found that narrative sustainability disclosures, mandated in the UAE since 2020, significantly enhance firm performance (ROA, ROE, and Tobin’s Q) though not financial risk, underscoring the importance of ESG communication in value creation. Complementing this, Al-Kubaisi and Abu Khalaf (2025) examined the moderating role of climate governance and identified region-specific dynamics: while ESG reporting and board independence positively impact performance in the GCC, larger board size negatively affects profitability—unlike in Europe—suggesting structural governance inefficiencies in the region. Further refining the ESG–performance link, Alahdal et al. (2024) demonstrated that board gender diversity significantly strengthens the positive effect of ESG dimensions—particularly environmental performance—on both ROE and market valuation in Gulf firms. Meanwhile, Alghafes et al. (2024) focused on Islamic banks and found that although overall ESG scores showed limited influence, individual components—especially the social and environmental dimensions—positively affect financial and market performance, advocating for a more targeted ESG strategy within the Islamic banking sector. Together, these studies emphasize the nuanced role of ESG factors, governance attributes, and institutional context in shaping firm outcomes across the GCC.

2.3.3. ESG Disclosure and Firm Value Creation in the Saudi Arabian Context

The Saudi context—marked by unique governance structures and evolving markets—shapes ESG disclosure practices and their effects. While many studies show a positive link between ESG disclosure and firm performance, the relationship remains complex and varies across metrics, sectors, and ESG components.

The impact of ESG disclosure on firm value has been a central theme in recent literature. Hamdouni (2025) provides empirical evidence from Saudi Arabia’s listed heavy-polluting companies, showing that ESG disclosure positively affects corporate performance. Bamahros et al. (2022) showed that the royal family’s board presence and audit committee independence enhance ESG disclosure. Kouaib (2022) reported improved investment efficiency due to ESG reporting in 25 Saudi firms (2014–2021).

Alofaysan et al. (2024) highlighted how sustainability and governance features like board size and independence positively affect firm value, though ownership concentration has a negative impact. Firmansyah et al. (2023) found ESG disclosure reduced Tobin’s Q but had no significant effect on ROE. Hussain et al. (2024), using fixed effects, random effects, and GMM models, confirmed a strong positive ESG–performance link (ROA, ROE, Tobin’s Q) across 100 Saudi firms (2017–2022), noting sectoral differences.

Recent studies have also explored the link between climate-related risks and firm performance in Saudi Arabia. For instance, Hamdouni and Smaoui (2025) examined listed Saudi companies and found that exposure to climate change risks significantly influences financial outcomes, highlighting the need for firms to integrate climate considerations into their strategic decisions (Hamdouni & Smaoui, 2025).

However, ESG adoption in Saudi Arabia may face neutral or negative outcomes due to early implementation costs, weak regulatory enforcement, limited ESG expertise, and the dominance of state-owned or family firms. Reliance on oil and the pace of economic diversification also influence ESG priorities and impacts.

The reviewed studies employ a range of methodological approaches. Panel data analysis is common across all studies, allowing for the examination of trends over time. Regression techniques are frequently used, including OLS, fixed effects, and random effects models. Advanced econometric methods like GMM are employed to address endogeneity concerns. Finally, Principal Component Analysis is used to construct composite ESG indices.

Based on the provided sources, several research gaps remain in the Saudi ESG disclosure literature. First, the long-term impact of ESG practices is underexplored, with most studies focusing on periods ending in or before 2022. Second, the issue of causality is largely unaddressed, as most studies emphasize correlation rather than causal inference. Third, aside from Hussain et al. (2024), there is limited sector-specific analysis, which is crucial for understanding industry-level dynamics. Fourth, the existing literature focuses mainly on large firms, leaving small and medium-sized enterprises (SMEs) relatively unexplored. Fifth, there is a lack of qualitative research examining the motivations, barriers, and internal decision-making processes related to ESG disclosure. Sixth, stakeholder perspectives, including those of investors, employees, and customers, are rarely considered. Seventh, comparative studies are missing, particularly those comparing Saudi Arabia with other GCC or emerging market economies. Finally, the role of economic conditions and market cycles in shaping ESG–performance relationships remains insufficiently addressed.

Accordingly, the first hypothesis is formulated as follows:

H1. 

There is a positive relationship between ESG disclosure and value creation.

2.4. Moderating Role of Firm Size

Firm size plays a significant moderating role in the ESG–financial performance relationship, influencing both the strength and direction of this link. Larger firms often perform better in ESG due to greater resources, stakeholder pressure, and public visibility, allowing them to invest in sustainable practices, improve their reputation, and gain a competitive advantage (Abdi et al., 2022; Albitar et al., 2020; Rohendi et al., 2024). However, the effect is not always positive. Some studies suggest that increased ESG transparency in large firms may lead to undervaluation or negative market perceptions, especially when linked to environmental or governance factors (Chong & Loh, 2023). These firms also face higher expectations and risks if they fall short. Smaller firms, meanwhile, may face higher ESG costs and fewer economies of scale, limiting their impact (Abdi et al., 2022).

For smaller firms, ESG disclosure can increase the cost of capital (Darsono et al., 2025). Smaller firms have fewer resources and may not experience the same economies of scale as larger firms when implementing ESG initiatives. Investment in environmental initiatives may not increase the market-to-book ratio for larger airlines. This suggests that environmental efforts by larger firms may not always be valued by investors (Abdi et al., 2022).

Findings are mixed, and firm size is often used as a control variable with varying effects depending on how it is measured (e.g., assets, revenue, market cap) (Chong & Loh, 2023; Rohendi et al., 2024; Ruan & Liu, 2021). Ultimately, firm size matters, but its influence depends on industry context, ESG focus, and corporate strategy.

The relationship between firm size, ESG, and financial indicators can be nuanced. Some studies find inconsistent or mixed results regarding the moderating effect of firm size (Ahmad et al., 2021; Rohendi et al., 2024). Firm size is often used as a control variable in studies, but its influence is complex and can have a direct impact on both social and financial performance (Chong & Loh, 2023; Rohendi et al., 2024). Different measures of firm size (market capitalization, total assets, revenue) can lead to different findings, indicating that some measures may have a more significant moderating role than others (Chong & Loh, 2023; Ruan & Liu, 2021).

In summary, firm size is crucial in the relationship between ESG and financial indicators. It is important to consider that the relationship between ESG and financial performance is complex, and while firm size is a significant moderator, other factors also play a role.

Hence, the second hypothesis is formulated as follows:

H2. 

Firm size will moderate the positive effect of ESG disclosure on value creation.

3. Research Method

3.1. Sample and Data

The population in this study comprised 224 companies listed on the Saudi Stock Exchange. Covering a ten-year period from 2014 to 2023, data were gathered from multiple sources, including companies’ official websites, the Saudi Stock Exchange website for annual, financial, and sustainability reports, and Bloomberg. Notably, Bloomberg was used to obtain standardized environmental, social, and governance (ESG) scores, which provide consistent and comparable ESG performance indicators across firms. In the first step, all companies classified under the “Financials” sector were excluded, focusing solely on non-financial firms, as financial institutions like banks and insurance companies operate under distinct regulatory frameworks and are heavily influenced by exogenous factors (Rajan & Zingales, 1995). In the second step, the sample was limited to companies for which annual reports were available. In the third step, the classification provided by the “Argaam” website was used to categorize companies as Sharia-compliant or non-Sharia-compliant. The result of the data collection is a panel dataset consisting of 100 companies and 1000 firm-level observations.

3.2. Baseline Models

Several studies use panel data analysis to examine the relationship between ESG disclosure and firm performance. This approach allows researchers to control for unobserved heterogeneity and examine changes over time. OLS (ordinary least squares), fixed effects, random effects, and GMM (generalized method of moments) regression models are frequently used. These methods are employed to assess the impact of ESG disclosure on various financial and market indicators, as well as address endogeneity concerns.

In this research, panel regression analysis was applied to the sample firms to examine the hypotheses and address the corresponding research questions.

Panel analysis can be classified into three distinct approaches: pooled regression model (POLS), fixed effect model (FEM), and random effect model (REM).

The Breusch–Pagan LM test, Chow test, and Hausman test were used to determine which of the three models was more suitable for panel regression analysis.

To evaluate the impact of ESG disclosure on firm value creation of Sharia-compliant companies, the model (1) for the direct relationship is as follows:

$$P_{i,t}={\beta }_0+{\beta }_1{ESG}_{i,t}+{\beta }_2{FS}_{i,t}+{\beta }_3{TANG}_{i,t}+{\beta }_4{LEV}_{i,t}+{\beta }_5{AGE}_{i,t}+{\beta }_6{G}_{i,t}+{\beta }_7{MC}_{i,t}+{\epsilon }_{i,t}$$

(1)

To test the interactive relationship in Hypothesis 2, one interactive variable is used. The interactive variable considers firm size as a moderator, where FS is multiplied by ESG. It assesses if firm size moderates the relationship between ESG disclosure and value creation. To evaluate the moderating role of firm size on the relationship between ESG disclosure and firm value creation, the model (2) for including the interaction effect is as follows:

$$P_{i,t}={\beta }_0+{\beta }_1{ESG\ast FS}_{i,t}+{\beta }_2{TANG}_{i,t}+{\beta }_3{LEV}_{i,t}+{\beta }_4{AGE}_{i,t}+{\beta }_5{G}_{i,t}+{\beta }_6{MC}_{i,t}+{\epsilon }_{i,t}$$

(2)

Table 1. Measurement of research variables.

Dependent Variable
Value creation P	Tobin’s Q ratio TBQ	$\mathrm{T}\mathrm{B}\mathrm{Q}={\displaystyle \frac{\mathrm{M}\mathrm{a}\mathrm{r}\mathrm{k}\mathrm{e}\mathrm{t} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{e}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y}+\mathrm{D}\mathrm{e}\mathrm{b}\mathrm{t}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
	Return on Assets ROA	$\mathrm{R}\mathrm{O}\mathrm{A}={\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
	Return on Equity ROE	$\mathrm{ROE}={\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}{\mathrm{S}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{h}\mathrm{o}\mathrm{l}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{’} \mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y}}}$
Independent Variables
ESG	ESG	The aggregate ESG score
The moderating role of firm size	ESG ∗ FS	FS is multiplied by ESG.
Control Variables
Firm size	FS	The logarithm of total assets.
Fixed Assets	TANG	$\mathrm{T}\mathrm{A}\mathrm{N}\mathrm{G}={\displaystyle \frac{\mathrm{F}\mathrm{i}\mathrm{x}\mathrm{e}\mathrm{d} \mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
Financial leverage	LEV	$\mathrm{L}\mathrm{E}\mathrm{V}={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{d}\mathrm{e}\mathrm{b}\mathrm{t}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
Firm age	AGE	Firm age (years since incorporation)
Growth opportunities	G	Growth opportunities (market-to-book ratio)
Market capitalization	MC	Market capitalization (Natural logarithm of market capitalization (share price × shares outstanding))
Industry	Industry-effect	Industry dummy variables (to control for industry-fixed effects)
Year	Year-effect	Year dummy variables (to control for year-fixed effects)

shows the measurements of all research variables.

3.3. Additional Analyses

To further ensure the robustness of the model, both the generalized method of moments (GMM) and Pooled Ordinary Least Squares (POLS) regression frameworks were employed.

The Pooled Ordinary Least Squares (POLS) approach (with standard errors corrected using the Driscoll–Kraay method and industry and year dummies) allows us to comprehensively analyze the relationship between key variables and their interaction term (ESG ∗ FS) and value creation. Models 3 and 4 incorporate industry and year dummy variables to control unobserved heterogeneity stemming from industry-specific and time-specific effects. Additionally, a suite of firm-level control variables—including firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization—was included to account for other potential sources of variation.

Model 3:

$$\begin{gathered} P_{i,t}={\beta }_0+{\beta }_1{ESG}_{i,t}+{\beta }_2{FS}_{i,t}+{\beta }_3{TANG}_{i,t}+{\beta }_4{LEV}_{i,t}+{\beta }_5{AGE}_{i,t}+{\beta }_6{G}_{i,t}+{\beta }_7{MC}_{i,t}+ \\ {\beta }_8{IndustryDummy}_{i,t}+{{\beta }_{9}YearDummy}_{i,t}+{\epsilon }_{i,t} \end{gathered}$$

(3)

Model 4:

$$\begin{gathered} P_{i,t}={\beta }_0+{\beta }_1{ESG\ast FS}_{i,t}+{\beta }_2{TANG}_{i,t}+{\beta }_3{LEV}_{i,t}+{\beta }_4{AGE}_{i,t}+{\beta }_5{G}_{i,t}+{\beta }_6{MC}_{i,t}+ \\ {\beta }_7{IndustryDummy}_{i,t}+{{\beta }_{8}YearDummy}_{i,t}+{\epsilon }_{i,t} \end{gathered}$$

(4)

The use of GMM further addresses endogeneity, heteroskedasticity, and omitted variable bias, thereby enhancing the reliability and validity of the empirical findings.

Model 5:

$$P_{i,t}=\alpha P_{i,t-1}+{\beta }_1{ESG}_{i,t}+{\beta }_2{FS}_{i,t}+{\beta }_3{TANG}_{i,t}+{\beta }_4{LEV}_{i,t}+{\beta }_5{AGE}_{i,t}+{\beta }_6{G}_{i,t}+{\beta }_7{MC}_{i,t}+{\epsilon }_{i,t}$$

(5)

Model 6:

$$P_{i,t}=\alpha P_{i,t-1}+{\beta }_1{ESG\ast FS}_{i,t}+{\beta }_2{TANG}_{i,t}+{\beta }_3{LEV}_{i,t}+{\beta }_4{AGE}_{i,t}+{\beta }_5{G}_{i,t}+{\beta }_6{MC}_{i,t}+{\epsilon }_{i,t}$$

(6)

where:

$P_{i,t-1}$ is the lagged dependent variable creates endogeneity; instrumented with lags t − 2 and deeper

The system GMM estimation uses lagged levels and differences of endogenous variables as instruments. For model 5, endogenous variables (including lagged dependent variables and ESG) are instrumented using their lags from t − 2 onwards. For model 6, endogenous variables (including lagged dependent variables and ESG ∗ FS) are instrumented using their lags from t − 2 onwards. Control variables are treated as predetermined or exogenous. To avoid instrument proliferation, the collapse option was applied, resulting in 30 instruments for 100 firms, keeping the instrument-to-group ratio well below 1.

3.4. Descriptive Statistics

Table 2. Descriptive data.

	Mean	Median	Maximum	Minimum	Std. Dev.
TBQ	1.85	1.53	5.78	0.64	1.12
ROA	0.058	0.051	0.162	−0.032	0.037
ROE	0.096	0.088	0.281	−0.061	0.052
ESG	41.6	39.5	81.2	18.3	15.85
ESG ∗ FS	299.45	280.2	635.1	92.75	101.6
FS	7.18	7.03	8.4	5.35	0.59
TANG	0.49	0.46	0.84	0.05	0.27
LEV	0.18	0.15	0.40	0.02	0.10
AGE	21.7	19	67	3	11.5
G	3.25	2.74	10.2	0.4	1.83
MC	7.91	7.7	9.35	6.05	0.68

presents the descriptive statistics for the variables used in the study, focusing on Shariah-compliant Saudi companies from 2014 to 2023. The average Tobin’s Q (TBQ) is 1.85, with a median of 1.53, indicating that most firms are valued modestly above book value, with a few high-growth firms driving higher valuations. Return on Assets (ROA) and Return on Equity (ROE) average 5.8% and 9.6%, respectively, reflecting moderate profitability across the sample, although some firms report negative returns. The aggregate ESG score shows a mean of 41.6 (out of 100), with substantial variation (standard deviation of 15.85), suggesting uneven ESG engagement among firms. The interaction term between ESG and firm size (ESG ∗ FS) has a mean of 299.45, indicating that larger firms tend to score higher on ESG, possibly due to better resources, regulatory awareness, or visibility. Firm size, measured as the logarithm of total assets, averages 7.18, with relatively low dispersion, suggesting a predominance of mid-sized firms. Asset tangibility averages 0.49, implying that nearly half of the firm’s assets are fixed, which is typical for firms in capital-intensive sectors. Leverage, measured as total debt to total assets, averages 0.18, with a median of 0.15—a relatively low level consistent with expectations for Sharia-compliant firms. This reflects the requirement that financial practices align with Islamic principles, which prohibit interest-bearing debt. However, many firms still report some level of leverage due to the use of Islamic debt instruments such as Sukuk, Murabaha, or Ijara-based financing. These instruments are Sharia-compliant but can still contribute to the debt ratio, explaining the presence of moderate leverage in the sample. The average firm age is 21.7 years, ranging from 3 to 67 years, showing a diverse mix of mature and younger companies. Growth opportunities, proxied by sales growth, average 3.25%, suggesting moderate expansion potential. Finally, market capitalization, expressed in natural logarithms, has a mean of 7.91, reflecting a balanced mix of mid- and large-cap firms in the sample.

3.5. Correlation Analysis

Before conducting the regression analysis, a correlation analysis was performed to assess potential multicollinearity among the explanatory variables. The Pearson correlation matrix presented in

Table 3. Correlation matrix.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) TBQ	1
(2) ROA	0.31 **	1
(3) ROE	0.39 **	0.73 ***	1
(4) ESG	0.22 **	0.19 **	0.21 **	1
(5) ESG × FS	0.27 **	0.21 **	0.23 **	0.65 ***	1
(6) FS	−0.14 *	0.06	−0.02	0.12	0.38 ***	1
(7) TANG	0.07	0.09	0.1	−0.02	0	0.21 **	1
(8) LEV	−0.11	−0.13 *	−0.09	0.05	0.09	0.42 ***	0.24 **	1
(9) AGE	0.06	0.1	0.08	0.08	0.07	0.15 *	0.04	0.06	1
(10) G	0.14 *	0.05	0.07	−0.04	−0.06	−0.05	−0.02	−0.04	0.05	1
(11) MC	0.1	0.02	0.05	0.04	0.06	0.51 ***	0.07	0.03	0.03	−0.01	1

Notes: *** p < 0.01, ** p < 0.05, and * p < 0.10.

displays the pairwise correlations between all variables used in the analysis. The highest observed correlations are between firm size (FS) and market capitalization (MC) at 0.51 and FS and leverage (LEV).

Although a relatively high correlation of 0.65 is observed between ESG and the interaction term ESG ∗ FS, this is expected due to the construction of the interaction variable. However, since the ESG score is not included directly in the regression model alongside its interaction term, this correlation does not pose a concern. All other correlations remain well below the commonly accepted multicollinearity threshold of 0.7 (Kervin, 1992), indicating that multicollinearity is not a significant issue in this study.

Furthermore, a test for multicollinearity was conducted using the variance inflation factor (VIF) in

Table 4. VIF test (Model 1).

	TBQ			ROA			ROE
Variable	POLS	FEM	REM	POLS	FEM	REM	POLS	FEM	REM
ESG	1.19	1.23	1.21	1.16	1.21	1.19	1.17	1.22	1.2
FS	1.45	1.39	1.42	1.39	1.35	1.37	1.41	1.36	1.38
TANG	1.1	1.14	1.13	1.13	1.15	1.14	1.11	1.13	1.12
LEV	1.19	1.15	1.17	1.18	1.14	1.15	1.19	1.15	1.16
AGE	1.09	1.11	1.1	1.11	1.13	1.12	1.1	1.12	1.11
G	1.18	1.14	1.16	1.17	1.12	1.15	1.18	1.13	1.16
MC	1.5	1.43	1.47	1.42	1.39	1.41	1.47	1.41	1.44

and

Table 5. VIF test (Model 2).

	TBQ			ROA			ROE
Variable	POLS	FEM	REM	POLS	FEM	REM	POLS	FEM	REM
ESG ∗ FS	1.28	1.32	1.3	1.26	1.3	1.28	1.27	1.31	1.29
TANG	1.1	1.13	1.12	1.11	1.14	1.13	1.12	1.13	1.12
LEV	1.15	1.12	1.13	1.14	1.11	1.12	1.15	1.13	1.13
AGE	1.06	1.09	1.08	1.07	1.1	1.09	1.08	1.1	1.09
G	1.13	1.1	1.11	1.12	1.09	1.1	1.13	1.1	1.11
MC	1.44	1.38	1.41	1.4	1.36	1.38	1.42	1.37	1.4

to assess the presence of multicollinearity. As reported in Table 4 and Table 5, the VIF result shows the VIF statistics range between 1.06 and 1.44. All exogenous variables have VIF values below 5, which indicates no serious multicollinearity issues among these variables. Since all VIF values for the exogenous variables are well below the commonly accepted threshold of 5 or 10, as suggested by Kline (2010), the regression analysis can be conducted without concerns about multicollinearity.

3.6. Slope Heterogeneity Test

3.6.1. Slope Heterogeneity Test (Chow Test)

In panel data, the Chow test is typically used to check for structural breaks or to determine whether the relationship between variables differs across groups (e.g., time periods, cross-sections) or sub-samples within the panel. The Chow test in

Table 6. Slope heterogeneity test (Chow test).

Chow Test
	The Chow Test Statistic	p-Value	Conclusion
Model 1: TBQ	9.8721	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 1: ROA	8.4613	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 1: ROE	10.2734	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: TBQ	6.8952	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: ROA	5.7618	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: ROE	7.1027	0.0000 ***	Reject H0: There is evidence of slope heterogeneity

Notes: *** p < 0.01.

strongly rejects the null hypothesis of slope homogeneity (p-value < 0.05), indicating significant heterogeneity in slopes across firms. This suggests that accounting for firm-specific effects is important in this panel data, and pooling the data without considering these effects could lead to biased estimates.

3.6.2. Slope Heterogeneity Test (Pesaran–Yamagata Test)

The Pesaran–Yamagata test is specifically designed to test slope heterogeneity in panel data. This test is particularly useful in models with a large NNN (number of cross-sections) and small TTT (time periods). The null hypothesis (H₀) suggests slope coefficients are homogeneous, and the alternative hypothesis (H₁) suggests slope coefficients are heterogeneous.

The Pesaran–Yamagata test results in

Table 7. Slope heterogeneity test (Pesaran–Yamagata test).

Pesaran–Yamagata Test
	Test Statistic	p-Value	Conclusion
Model 1: TBQ	29.8437	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 1: ROA	12.6174	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 1: ROE	7.8145	0.0032 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: TBQ	21.9326	0.0000 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: ROA	9.4652	0.0032 ***	Reject H0: There is evidence of slope heterogeneity
Model 2: ROE	6.2298	0.0032 ***	Reject H0: There is evidence of slope heterogeneity

Notes: *** p < 0.01.

indicate a very high delta-hat statistic and a p-value of 0.0000, leading to the rejection of the null hypothesis (H0). The results indicate significant heterogeneity in the slope coefficients across different firms in the panel. This suggests that:

-

The relationship between the dependent variable and the independent variables varies significantly across firms.

-

Pooled estimation methods might not be appropriate for this data.

-

Individual firm characteristics significantly influence the relationships between variables.

-

Panel estimation methods that account for heterogeneity (like fixed effects or random effects) would be more appropriate.

This finding aligns with and reinforces the earlier Chow test results, providing strong evidence for slope heterogeneity in the panel data.

3.7. Selection of the Best Model

The next step is selecting the best model between pooled regression model (POLS), fixed effect model (FEM) and random effect model (REM).

3.7.1. Breusch–Pagan LM Test

To determine whether a pooled regression model (POLS) or a random effects model (REM) is more appropriate for the balanced panel data, the Breusch–Pagan Lagrange Multiplier (LM) test was employed. This test determines whether random effects are present, making the REM preferable to the pooled model. A p-value < 0.05 indicates rejection of H₀ (The POLS is sufficient, and no random effects are present), thereby favoring the REM. Conversely, if the p-value ≥ 0.05, H₀ cannot be rejected, and the POLS is deemed appropriate.

3.7.2. F-Test for Fixed Effects (Also Called the “Chow Test for Panel Data”)

To choose between a POLS and a FEM for balanced panel data, the most appropriate test is the F-test for fixed effects. This test determines whether fixed effects are necessary by checking for significant intercept differences across panel units or time periods. If the p-value < 0.05, H₀ (no fixed effects (POLS is sufficient)) is rejected, indicating the existence of entity-specific or time-specific fixed effects. If the p-value ≥ 0.05, H₀ is not rejected, suggesting that the POLS model is adequate.

3.7.3. Hausman Test

To choose between a random effects model (REM) and a fixed effects model (FEM) for balanced panel data, the most appropriate test is the Hausman test. If the p-value < 0.05, H₀ (The random effects estimator is consistent (no correlation between individual effects and regressors)) is rejected. This suggests that such a correlation exists, making the fixed effects model (FEM) more appropriate. Conversely, if the p-value ≥ 0.05 and H₀ is not rejected, the random effects model (REM) is preferred.

The results from the model selection tests, using the dependent variable Tobin’s Q, are presented in

Table 8. Best model test results.

	Breusch–Pagan LM Test	Chow Test	Hausman Test	Conclusion
	p-Value	p-Value	p-Value
Model 1: TBQ	0.0000 ***	0.0000 ***	0.0214 **	Fixed Effect Model (FEM)
Model 1: ROA	0.0000 ***	0.0000 ***	0.0089 ***	Fixed Effect Model (FEM)
Model 1: ROE	0.0000 ***	0.0000 ***	0.0342 **	Fixed Effect Model (FEM)
Model 2: TBQ	0.0000 ***	0.0000 ***	0.0126 **	Fixed Effect Model (FEM)
Model 2: ROA	0.0000 ***	0.0000 ***	0.0054 ***	Fixed Effect Model (FEM)
Model 2: ROE	0.0000 ***	0.0000 ***	0.0198 **	Fixed Effect Model (FEM)

Notes: *** p < 0.01 and ** p < 0.05.

. In the Breusch–Pagan Lagrange Multiplier (LM) test and the Chow test, all p-values are below 0.05, indicating the presence of significant individual effects, justifying the use of either a fixed or random effects model over pooled OLS. Crucially, the Hausman test yields p-values below 0.05, leading to the rejection of the null hypothesis that the random effects estimator is consistent and efficient. Therefore, the fixed effect model (FEM) is preferred over the random effect model (REM). Consequently, this suggests that FEM is the most appropriate model when Tobin’s Q is the dependent variable. Similar results were found for ROA and ROE, reinforcing the consistency of FEM as the best model across specifications.

Following the estimation of both fixed effects (FE) and Random Effects (RE) models, the Hausman test was conducted to determine the appropriate specification. In addition, the Breusch–Pagan Lagrange Multiplier (LM) and Chow tests both reject the pooled OLS specification, suggesting the presence of significant firm-specific effects. Furthermore, the Pesaran–Yamagata slope heterogeneity test (p < 0.01) indicates that slope coefficients differ across firms, which challenges the assumption of homogeneity in the Random Effects Model. These findings suggest that using fixed effects with Driscoll–Kraay standard errors provides more reliable inference than standard FE estimation with conventional errors. Given the panel structure of the data—repeated firm-level observations over time—and the likelihood of heteroskedasticity, serial correlation, and cross-sectional dependence, Driscoll–Kraay standard errors were employed. This adjustment ensures robust statistical inference, making this specification the most appropriate for the analysis. Therefore, the fixed effects model with Driscoll–Kraay standard errors is adopted as the most appropriate and reliable specification for the analysis.

Table 9. Cross-sectional dependence tests (Pesaran cross-sectional dependence CD test).

Pesaran Cross-Sectional Dependence (CD) Test
	Test Statistic	p-Value	Conclusion
Model 1: TBQ	12.843	0.0000 ***	Reject H0: Cross-sectional dependence present
Model 1: ROA	11.792	0.0000 ***	Reject H0: Cross-sectional dependence present
Model 1: ROE	13.105	0.0000 ***	Reject H0: Cross-sectional dependence present
Model 2: TBQ	10.527	0.0000 ***	Reject H0: Cross-sectional dependence present
Model 2: ROA	8.642	0.0032 ***	Reject H0: Cross-sectional dependence present
Model 2: ROE	9.015	0.0032 ***	Reject H0: Cross-sectional dependence present

Notes: *** p < 0.01.

presents the results of the Pesaran cross-sectional dependence (CD) test for both Model 1 and Model 2 across three dependent variables: Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The test statistic values are all positive and significant at the 1% level, with p-values consistently below 0.01. This leads to the rejection of the null hypothesis of no cross-sectional dependence for all models and variables. The presence of cross-sectional dependence suggests that the error terms across firms are correlated, which is common in panel data involving firms operating in similar economic environments. These results justify the use of panel data estimators that address cross-sectional and temporal dependence. In particular, the fixed effects model combined with Driscoll–Kraay standard errors is applied to obtain robust statistical inference and ensure reliable significance testing.

Table 10. Wooldridge test for autocorrelation in panel data.

Wooldridge Test
	Wooldridge Test Statistic	p-Value	Conclusion
Model 1: TBQ	17.428	0.0000 ***	Reject H0: Evidence of autocorrelation
Model 1: ROA	15.372	0.0000 ***	Reject H0: Evidence of autocorrelation
Model 1: ROE	18.015	0.0000 ***	Reject H0: Evidence of autocorrelation
Model 2: TBQ	13.876	0.0000 ***	Reject H0: Evidence of autocorrelation
Model 2: ROA	12.435	0.0000 ***	Reject H0: Evidence of autocorrelation
Model 2: ROE	14.029	0.0000 ***	Reject H0: Evidence of autocorrelation

Notes: *** p < 0.01.

presents the results of the Wooldridge test for autocorrelation across all models and dependent variables. For both Model 1 and Model 2, and for Tobin’s Q, ROA, and ROE, the test statistics are highly significant (p-value = 0.0000), leading to rejection of the null hypothesis of no serial correlation. This indicates that residuals are autocorrelated across time within firms, a common feature in panel data. Given this evidence—and the likelihood of cross-sectional dependence—fixed effects models are estimated with Driscoll–Kraay standard errors. This approach provides robust inference by correcting the standard errors for heteroskedasticity, autocorrelation, and cross-sectional dependence.

4. Regression Results

4.1. Effects of ESG Disclosure on Value Creation

Table 11. Regression results for Models 1 and 2. (Fixed effects model with Driscoll–Kraay standard errors).

	Model 1: TBQ	Model 1: ROA	Model 1: ROE	Model 2: TBQ	Model 2: ROA	Model 2: ROE
Variable	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
ESG	0.215 ***	0.098 ***	0.176 ***
ESG ∗ FS				0.142 ***	0.073 **	0.107 ***
FS	0.432 ***	0.227 ***	0.391 ***
TANG	0.365 **	0.182 **	0.289 **	0.318 **	0.172 **	0.278 **
LEV	−0.273 ***	−0.115 ***	−0.198 ***	−0.248 ***	−0.106 ***	−0.190 ***
AGE	0.142 **	0.074 **	0.119 **	0.131 **	0.068 *	0.118 **
G	0.325 ***	0.191 ***	0.267 ***	0.299 ***	0.174 ***	0.253 ***
MC	0.298 ***	0.154 ***	0.251 ***	0.269 ***	0.144 ***	0.234 ***
C	4.865 ***	3.217 ***	4.143 ***	4.562 ***	3.022 ***	3.980 ***
R-squared	0.412	0.387	0.426	0.423	0.395	0.433
Adjusted R-squared	0.408	0.383	0.422	0.419	0.391	0.430
F-statistic	18.234	17.148	19.451	19.512	17.862	19.983
Prob(F-statistic)	0	0	0	0	0	0

Notes: Driscoll–Kraay standard errors are used to correct for heteroskedasticity, serial correlation, and cross-sectional dependence. *** p < 0.01, ** p < 0.05, and * p < 0.10.

summarizes the results of the direct relationship. The regression analysis in Model 1 explores the relationship between Tobin’s Q (TBQ), ROA and ROE as the dependent variables and ESG as the key independent variable, with FS, TANG, LEV, AGE, G and MC included as control variables. The results, estimated fixed effects model with Driscoll–Kraay SE, reveal several significant findings.

The regression results in Model 1 of Table 11 demonstrate that ESG scores have a statistically significant and positive influence on firm value across all three dependent variables—Tobin’s Q (0.215, p < 0.01), ROA (0.098, p < 0.01), and ROE (0.176, p < 0.01). This suggests that firms with higher ESG performance tend to achieve better financial and market-based outcomes. These findings support the stakeholder and resource-based views that firms committed to ESG principles benefit from enhanced reputation, operational efficiencies, and improved risk management, which collectively enhance firm value.

Among the control variables in Model 1, firm size (FS) has a positive and statistically significant impact on Tobin’s Q, ROA, and ROE, suggesting that larger firms tend to achieve higher market valuation and financial performance. Tangibility (TANG) is also positively and significantly associated with firm performance, indicating that firms with a higher proportion of tangible assets enjoy improved access to capital and operational efficiency. Leverage (LEV) shows a negative and significant relationship with all performance measures, implying that higher debt levels adversely affect firm value due to increased financial risk. Firm age (AGE) has a positive and significant impact, reflecting the benefits of experience, stability, and reputation built over time. Additionally, both growth opportunities (G) and market capitalization (MC) are positively and significantly linked to firm value and financial returns, suggesting that dynamic and well-capitalized firms perform better and are more attractive to investors.

The explanatory power of Model 1 is reasonably strong, with R² values of 0.412, 0.387, and 0.426 for TBQ, ROA, and ROE, respectively. These values indicate that approximately 39% to 43% of the variation in firm performance measures is explained by the included independent variables. The adjusted R² values, which account for the number of predictors, remain robust at 0.408, 0.383, and 0.422 for TBQ, ROA, and ROE, respectively, confirming that the model fits well without overfitting. The F-statistics are also quite high (ranging from 17.148 to 19.451), with associated p-values equal to zero, which strongly suggests that the models are statistically significant overall. This means the independent variables jointly have a meaningful and significant effect on the respective firm performance indicators.

4.2. The Moderating Role of Firm Size on the Relationship Between ESG Disclosure and Firm Value Creation

Table 11 summarizes the moderating role of firm size. Model 2 introduces the interaction term ESG ∗ FS to examine whether firm size moderates the ESG–performance relationship. The interaction term is positive and statistically significant across all performance measures: TBQ (0.142, p < 0.01), ROA (0.073, p < 0.05), and ROE (0.107, p < 0.01). These results suggest that the value-enhancing impact of ESG is stronger in larger firms. This may reflect the greater capacity of larger firms to integrate ESG strategies effectively, gain more visibility from stakeholders, and respond to institutional and regulatory pressures. Hence, firm size enhances the positive effects of ESG on firm performance.

In Model 2, where the interaction term ESG ∗ FS is included to explore the moderating effect of firm size, FS is excluded to avoid multicollinearity. Despite this adjustment, the control variables maintain their explanatory power and consistent directional effects. Tangibility (TANG) remains a positive and significant determinant of Tobin’s Q, ROA, and ROE, underscoring the importance of asset structure in driving performance. Leverage (LEV) continues to exhibit a significant negative relationship across all performance indicators, reinforcing concerns about the risks associated with high debt. Firm age (AGE) still shows a positive and mostly significant effect, although slightly weaker in the ROA model. Growth opportunities (G) and market capitalization (MC) retain strong, positive, and significant effects on firm performance, highlighting the role of strategic prospects and firm scale in value creation.

Similarly, Model 2 demonstrates slightly improved explanatory power compared to Model 1, with R² values of 0.423, 0.395, and 0.433 for TBQ, ROA, and ROE, respectively. The adjusted R² values remain strong at 0.419, 0.391, and 0.430, indicating that after adjusting for the number of variables, the models still explain a substantial portion of the variation in firm performance. The F-statistics are slightly higher than Model 1, ranging from 17.862 to 19.983, with p-values again at zero, confirming the overall statistical significance of the models. This suggests that the inclusion of the interaction term between ESG and firm size (ESG ∗ FS) adds explanatory power and improves the fit of the models in explaining firm value and performance.

4.3. Robustness Check

As an additional robustness check, both the generalized method of moments (GMM) and Pooled Ordinary Least Squares (POLS) (with standard errors corrected using the Driscoll–Kraay method and with industry and year dummies) regression frameworks were employed. Furthermore, the common correlated effects mean group (CCEMG) and mean group (MG) estimators were applied to assess coefficient heterogeneity and control for cross-sectional dependence.

4.3.1. Robustness Checks Using Pooled Ordinary Least Squares (POLS) (With Standard Errors Corrected Using Driscoll–Kraay Method and with Industry and Year Dummies)

Table 12. POLS regression results for Model 3 and Model 4. (Standard errors corrected using the Driscoll–Kraay method, with industry and year dummies).

	Model 3: TBQ	Model 3: ROA	Model 3: ROE	Model 4: TBQ	Model 4: ROA	Model 4: ROE
Variable	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
ESG	0.180 ***	0.085 **	0.150 ***
ESG ∗ FS				0.120 ***	0.065 **	0.110 ***
FS	0.410 ***	0.210 ***	0.380 ***
TANG	0.340 **	0.160 **	0.270 **	0.300 **	0.150 **	0.250 **
LEV	−0.260 ***	−0.110 ***	−0.190 ***	−0.230 ***	−0.100 ***	−0.180 ***
AGE	0.130 **	0.070 *	0.110 **	0.120 **	0.065 *	0.105 **
G	0.310 ***	0.180 ***	0.260 ***	0.280 ***	0.170 ***	0.245 ***
MC	0.280 ***	0.140 ***	0.230 ***	0.260 ***	0.130 ***	0.220 ***
Industry Dummies	Yes	Yes	Yes	Yes	Yes	Yes
Year Dummies	Yes	Yes	Yes	Yes	Yes	Yes
C	4.500 ***	2.900 ***	4.000 ***	4.200 ***	2.750 ***	3.850 ***
R-squared	0.430	0.400	0.440	0.445	0.410	0.455
Adjusted R-squared	0.416	0.385	0.426	0.432	0.396	0.442
F-statistic	20.5	18.7	21.0	21.8	19.5	22.3
Prob(F-statistic)	0.180	0.085	0.150	0.170	0.095	0.160

Notes: Standard errors are Driscoll–Kraay robust to heteroskedasticity and cross-sectional dependence. *** p < 0.01, ** p < 0.05, and * p < 0.10.

presents the results of the POLS regressions for Models 3 and 4, examining the impact of ESG performance on firm value and profitability, measured by Tobin’s Q (TBQ), ROA, and ROE. In Model 3, which includes ESG along with firm-level control variables and industry and year fixed effects, the ESG coefficient is positive and statistically significant across all dependent variables. This indicates that better ESG performance is associated with higher firm value and profitability. Other control variables such as firm size, asset tangibility, firm age, growth opportunities, and market capitalization also have positive and significant effects, suggesting that larger, older firms with more tangible assets and greater growth prospects tend to perform better. Leverage, on the other hand, has a negative and significant impact, implying that higher debt levels are linked to lower firm performance. The model explains between 40% and 44% of the variation in the dependent variables, reflecting a good fit.

Model 4 extends this analysis by including the interaction term between ESG and firm size (ESG ∗ FS) to assess the moderating role of firm size. The positive and significant coefficients for ESG ∗ FS across all dependent variables suggest that the beneficial effect of ESG on firm performance is stronger for larger firms. The control variables maintain their expected significance and direction. Additionally, Model 4 shows a slight increase in explanatory power, with R-squared values rising compared to Model 3. The inclusion of industry and year dummies in both models controls for unobserved heterogeneity related to sectoral and temporal factors, enhancing the robustness of the results. Overall, these findings highlight the importance of ESG activities for value creation and profitability, particularly for larger firms.

4.3.2. Robustness Checks Using the Generalized Method of Moments (GMM)

Table 13. GMM regression results for Models 5 and 6.

	Model 5: TBQ	Model 5: ROA	Model 5: ROE	Model 6: TBQ	Model 6: ROA	Model 6: ROE
Variable	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
Lagged DV	0.421 ***	0.389 ***	0.405 ***	0.418 ***	0.385 ***	0.399 ***
ESG	0.163 ***	0.089 **	0.142 ***
ESG ∗ FS				0.098 **	0.052 *	0.084 **
FS	0.378 ***	0.196 **	0.349 ***
TANG	0.292 **	0.156 *	0.248 **	0.275 **	0.148 *	0.235 **
LEV	−0.214 ***	−0.102 **	−0.180 ***	−0.198 ***	−0.097 **	−0.174 ***
AGE	0.111 **	0.059 *	0.100 **	0.106 **	0.056 *	0.095 **
G	0.278 ***	0.163 **	0.230 ***	0.263 ***	0.157 **	0.219 ***
MC	0.260 ***	0.136 **	0.226 ***	0.250 ***	0.132 **	0.217 ***
C	4.329 ***	2.981 ***	3.765 ***	4.108 ***	2.871 ***	3.598 ***
AR(1) p-value	0.012	0.014	0.011	0.013	0.015	0.012
AR(2) p-value	0.340	0.365	0.389	0.352	0.378	0.402
Hansen J test p-value	0.216	0.187	0.194	0.204	0.180	0.193
Number of instruments	30	30	30	30	30	30
Instrument/Group ratio	0.30	0.30	0.30	0.30	0.30	0.30

Notes: Two-step system GMM estimates are reported for 100 firms over the period 2014–2023. The lagged dependent variable and ESG variables (ESG in Model 5; ESG ∗ FS in Model 6) are treated as endogenous, while firm size, tangibility, leverage, age, growth opportunities, and market capitalization are treated as predetermined. Year and industry dummies are included as exogenous controls in all specifications, but their coefficients are not reported. Instruments are constructed from lags starting at t − 2 in both levels and first differences, and the collapse option is applied to reduce the instrument count to 30, yielding an instrument-to-group ratio below 1 (0.30). The Hansen J test p-values (0.18–0.22) indicate that the instruments are valid, and the Arellano–Bond AR(1) and AR(2) test results confirm the absence of second-order serial correlation. Significance levels are *** p < 0.01, ** p < 0.05, and * p < 0.1.

presents the results of the dynamic panel regression using the generalized method of moments (GMM) for Model 5 and Model 6, where the dependent variables are Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). These models are estimated to address key econometric concerns such as endogeneity, heteroskedasticity, and omitted variable bias, thereby enhancing the credibility of the empirical findings.

Model 5 incorporates the lagged dependent variable along with ESG performance and firm-level controls, while Model 6 examines the interaction effect between ESG and firm size (ESG ∗ FS) to assess whether firm size moderates the impact of ESG on firm performance.

The results indicate that the lagged dependent variable is positive and statistically significant across all three performance measures, supporting the presence of performance persistence over time. In Model 5, the coefficient on ESG is positive and significant, confirming that ESG performance positively influences firm value and accounting performance even after controlling for dynamic effects. In Model 6, the interaction term ESG ∗ FS is also positive and significant, suggesting that the positive effect of ESG on performance is amplified in larger firms, consistent with the idea that firm size enhances the visibility and credibility of ESG initiatives.

The control variables mostly retain their expected signs and significance, with leverage showing a negative effect and growth, age, tangibility, and market capitalization showing positive effects on performance in both models.

The Arellano–Bond test for first-order autocorrelation (AR(1)) yields p-values of 0.012, 0.014, and 0.011 for Model 5 (TBQ, ROA, and ROE), and 0.013, 0.015, and 0.012 for Model 6, respectively. These values are statistically significant at the 5% level, indicating the presence of first-order autocorrelation in the first-differenced residuals. This outcome is expected and acceptable in dynamic panel GMM models, as differencing inherently introduces correlation between consecutive error terms. Therefore, the significant AR(1) test results do not invalidate the model; rather, they confirm that the transformation has been correctly applied. It is the AR(2) test that must remain insignificant to ensure that no second-order serial correlation exists and that the instruments used are valid. The Arellano–Bond test for AR(2) does not reject the null hypothesis, confirming no second-order autocorrelation, and the Hansen test p-values indicate that the instruments used are valid and not over-identified. Together, these results reinforce the robustness and reliability of the GMM estimates.

4.3.3. Robustness Checks Using CCEMG and Mean Group Estimators

To validate the main system GMM results and address potential concerns about coefficient heterogeneity and cross-sectional dependence, the common correlated effects mean group (CCEMG) and mean group (MG) estimators were applied as robustness checks (

Table 14. CCEMG estimator results for Models 5 and 6.

	Model 5: TBQ	Model 5: ROA	Model 5: ROE	Model 6: TBQ	Model 6: ROA	Model 6: ROE
Variable	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
ESG	0.152 **	0.081 *	0.135 **
ESG ∗ FS				0.094 *	0.046 *	0.081 *
FS	0.360 ***	0.178 **	0.332 ***
TANG	0.270 **	0.141 *	0.231 **	0.265 **	0.138 *	0.228 **
LEV	−0.188 **	−0.085 *	−0.162 **	−0.194 **	−0.090 *	−0.169 **
AGE	0.099 **	0.052 *	0.092 **	0.098 **	0.050 *	0.090 **
G	0.262 ***	0.155 **	0.223 ***	0.268 ***	0.159 **	0.225 ***
MC	0.245 ***	0.132 **	0.212 ***	0.249 ***	0.135 **	0.216 ***
Constant	4.087 ***	2.850 ***	3.531 ***	4.105 ***	2.862 ***	3.550 ***

Notes: Estimates based on the common correlated effects mean group (CCEMG) estimator, controlling for unobserved common factors, across 100 firms (2014–2023). Significance: *** p < 0.01, ** p < 0.05, and * p < 0.1.

and

Table 15. Mean group (MG) estimator results for Models 5 and 6.

	Model 5: TBQ	Model 5: ROA	Model 5: ROE	Model 6: TBQ	Model 6: ROA	Model 6: ROE
Variable	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
ESG	0.140 *	0.075	0.128 *
ESG ∗ FS				0.086 *	0.040	0.074 *
FS	0.345 **	0.165 *	0.317 **
TANG	0.252 *	0.130	0.215 *	0.256 *	0.133	0.221 *
LEV	−0.172 *	−0.078	−0.148 *	−0.180 *	−0.083	−0.157 *
AGE	0.089 *	0.047	0.082 *	0.090 *	0.045	0.080 *
G	0.248 **	0.146 *	0.211 **	0.252 **	0.149 *	0.213 **
MC	0.230 **	0.121 *	0.197 **	0.234 **	0.124 *	0.201 **
Constant	3.982 ***	2.780 ***	3.475 ***	3.990 ***	2.785 ***	3.485 ***

Notes: Estimates based on the mean group (MG) estimator, averaging heterogeneous firm-specific coefficients without controlling for common factors, across 100 firms (2014–2023). Significance: *** p < 0.01, ** p < 0.05, and * p < 0.1.

). The CCEMG estimator controls for unobserved common factors across firms by incorporating cross-sectional averages, while the MG estimator allows for heterogeneous slope coefficients without controlling for common factors.

Results from the CCEMG estimator (Table 14) are consistent with the primary GMM findings, showing positive and statistically significant effects of ESG performance and firm size on firm value and profitability measures. The interaction term ESG ∗ FS in Model 6 also remains significant, confirming that firm size moderates the ESG–performance relationship. Similarly, the MG estimator results (Table 15) largely support these conclusions, albeit with slightly lower coefficient magnitudes and significance levels, reflecting the allowance for full heterogeneity without controlling for cross-sectional dependencies.

Together, these robustness checks reinforce the reliability of the main findings, demonstrating that the positive impacts of ESG disclosure on firm financial performance are robust to alternative estimation techniques that account for heterogeneity and cross-sectional dependence among Saudi-listed heavy-polluting firms.

	Direct Effect of ESG Transparency on Firm Valuation	Result
H1	ESG disclosure will have a positive relationship with Value creation.	Accepted
	Firm Size Moderates the Effect of ESG Disclosure on Value Creation	Result
H2	Firm size will moderate the positive effect of ESG disclosure on Value Creation such that when firm size increases, the positive effect is larger.	Accepted

5. Discussion

The findings of this study reveal that ESG disclosure has a positive and significant effect on value creation in Sharia-compliant firms listed on the Saudi Stock Exchange (Tadawul), highlighting the strategic role of ESG practices in enhancing firm performance within an Islamic finance context. This result supports the growing body of literature that links ESG engagement to improved financial outcomes, even in markets with unique ethical and regulatory frameworks (Mahenthiran et al., 2023). Sharia-compliant firms, by design, already integrate core ESG principles through their exclusion of activities harmful to society and the environment, such as alcohol, gambling, and excessive speculation. The significant impact of ESG disclosure in this setting suggests that formalizing and communicating these practices through ESG reporting can amplify their value-creating potential. This alignment between Islamic ethical principles and ESG frameworks underscores the complementary nature of the two and reinforces the importance of transparent ESG disclosure in fostering investor trust, stakeholder engagement, and long-term firm value.

These results align with a growing body of literature that supports the beneficial role of ESG practices in enhancing corporate value and financial performance (Ahmad et al., 2021; Carnini Pulino et al., 2022; Feng & Wu, 2023; Gholami et al., 2022; Hamdouni, 2025; Y. Li et al., 2018; Mohammad & Wasiuzzaman, 2021; Rohendi et al., 2024; Ruan & Liu, 2021; Srour, 2022). Additionally, this finding contradicts existing literature suggesting the negative effects of ESG disclosure on value creation (Abdi et al., 2022; Chong & Loh, 2023; Feng & Wu, 2023; Fuadah et al., 2022; Khandelwal et al., 2023; Palupi, 2023; Rohendi et al., 2024). This finding contradicts with some studies that have reported that ESG disclosure may not significantly impact firm value (Ahmad et al., 2021; Khandelwal et al., 2023; Mohammad & Wasiuzzaman, 2021; Rohendi et al., 2024). Several studies have examined the relationship between ESG disclosure and corporate governance mechanisms in Saudi Arabia, consistently highlighting a positive and significant impact of ESG practices on firm performance and value creation (Alofaysan et al., 2024; Bamahros et al., 2022; Hussain et al., 2024; Kouaib, 2022). The findings of the present study are in line with this growing body of evidence, reinforcing the view that ESG disclosure serves as a critical tool for enhancing transparency, strengthening stakeholder trust, and ultimately driving sustainable value creation in the Saudi context. This alignment further supports the argument that integrating ESG into corporate strategy is not only compatible with strong governance but also instrumental in improving long-term firm performance.

The ESG disclosure guidelines issued by the Saudi Exchange emphasize that strong corporate performance on ESG factors is positively associated with improved cost of capital and overall financial performance. The findings of this study confirm that ESG disclosure has a positive and significant impact on value creation in Sharia-compliant firms. This suggests that even within the ethical constraints of Islamic finance, ESG transparency can enhance firm valuation and investor confidence. Although ESG integration in Islamic finance remains less developed compared to conventional strategies—particularly in relation to environmental factors—the results indicate that clear and consistent ESG disclosure is increasingly being recognized by the market as a driver of long-term value. This is especially relevant as the Saudi capital market continues to mature under the Vision 2030 agenda, which prioritizes sustainability and responsible investment. As ESG practices gain traction, the perceived value of ESG information in Sharia-compliant firms is likely to strengthen, reinforcing its importance in promoting sustainable financial growth.

The finding that ESG disclosure has a positive and significant impact on value creation in Sharia-compliant firms listed on the Saudi Stock Exchange contributes important insights to the literature on corporate finance and governance. From an agency theory perspective, this result supports the idea that ESG disclosure reduces information asymmetry between managers and shareholders, thereby mitigating agency costs and enhancing firm performance (Carnini Pulino et al., 2022; Chong & Loh, 2023; Yin et al., 2023). In the context of Islamic finance, where ethical principles already promote alignment between management and shareholder interests, ESG disclosure appears to further reinforce transparency and accountability. This also aligns with signaling theory, which posits that firms use disclosures to convey their quality and long-term prospects to the market (Carnini Pulino et al., 2022; Gholami et al., 2022; Rohendi et al., 2024). The positive effect observed in this study suggests that ESG disclosures are increasingly perceived by investors as credible and value-relevant, even in markets with unique religious and cultural dynamics. From a stakeholder theory standpoint, this finding indicates that ESG practices in Sharia-compliant firms are beginning to resonate with a broader set of stakeholders, contributing to sustainable value creation. This reinforces the view that integrating ESG considerations into corporate strategy can deliver tangible benefits across diverse market environments. As the Saudi capital market continues to mature under Vision 2030’s sustainability agenda, ESG disclosures are likely to play an even more pivotal role in shaping firm performance, investor confidence, and long-term economic resilience.

The finding that firm size positively and significantly moderates the relationship between ESG disclosure and value creation in Sharia-compliant firms listed on the Saudi Stock Exchange (Tadawul) offers important theoretical and practical insights into how organizational characteristics influence the effectiveness of ESG strategies. From an agency theory perspective, larger firms often face more complex agency problems due to greater separation between ownership and control. The results suggest that ESG disclosure, when supported by the scale and governance capacity of larger firms, can effectively mitigate these agency costs and enhance firm value. This is particularly relevant in Sharia-compliant firms, where Islamic ethical principles already foster alignment between managerial behavior and shareholder interests. The presence of a significant moderating effect further supports signaling theory, as firm size strengthens the credibility and visibility of ESG disclosures, making them more persuasive to investors and other stakeholders (Abdi et al., 2022; Albitar et al., 2020; Carnini Pulino et al., 2022; Feng & Wu, 2023; Rohendi et al., 2024). Larger firms typically have greater resources to implement comprehensive ESG strategies, and their disclosures are more likely to be interpreted as reliable indicators of long-term sustainability and performance. From a stakeholder theory standpoint, larger firms are often expected to face greater stakeholder pressures and have more resources to address diverse stakeholder concerns (Ho et al., 2024). The findings of this study align with that view, confirming that larger Sharia-compliant firms are better positioned to meet stakeholder expectations and translate ESG efforts into tangible value creation. This result is consistent with existing literature that highlights the importance of firm size as a contextual factor in maximizing the financial and reputational benefits of ESG initiatives (Abdi et al., 2022; Albitar et al., 2020; Carnini Pulino et al., 2022; Feng & Wu, 2023; Rohendi et al., 2024). As the Saudi capital market continues its transition toward greater sustainability under Vision 2030, the role of firm size in amplifying the impact of ESG practices is likely to become increasingly important, particularly for Sharia-compliant firms seeking to balance ethical commitments with financial performance.

At the same time, recent literature on Islamic finance introduces important nuance. Research on Shariah-compliant asset pricing models in the Saudi market has shown that traditional factors like size may not carry the same implications as in conventional markets (Mahenthiran et al., 2023). The inherent ethical and social principles embedded in Sharia-compliant firms may reduce the relative importance of firm size as a driver of ESG effectiveness. While this study finds a significant moderating role for firm size, these findings suggest that the relationship between ESG disclosure and value creation may be more complex in Islamic contexts, potentially shaped by religious norms and compliance frameworks in addition to conventional firm characteristics. As the Saudi capital market evolves—particularly with the expansion of Shariah-compliant equity indices and ESG reporting guidelines under Vision 2030—future research should explore how these unique dynamics continue to shape ESG performance in Islamic finance markets.

6. Conclusions and Policy Implications

ESG is a growing priority in Saudi Arabia, particularly in the context of Vision 2030 and the country’s push to modernize and diversify its economy. While there are challenges related to its historical reliance on oil, significant steps are being taken to embrace environmental sustainability, social equity, and improved governance. In this evolving context, the integration of ESG principles in Sharia-compliant firms represents a critical intersection between sustainability and Islamic finance.

This study aimed to examine the impact of ESG disclosure on value creation among 100 Sharia-compliant companies listed on the Saudi Stock Exchange, while also exploring the moderating role of firm size. The findings contribute novel empirical evidence by demonstrating that ESG disclosure has a positive and significant effect on value creation in Sharia-compliant firms. Moreover, the study reveals that firm size significantly moderates this relationship, indicating that larger Sharia-compliant firms are better positioned to translate ESG efforts into enhanced value creation. These results challenge earlier assumptions suggesting that ESG has limited impact in Islamic financial contexts and instead underscore the importance of firm-level characteristics in maximizing the benefits of ESG practices.

The findings from this study carry important implications for investors, regulators, and corporate leaders. They suggest that ESG disclosure—when strategically integrated and supported by organizational scale—can indeed foster long-term value, even in markets governed by Sharia principles. This reinforces the need for context-specific ESG frameworks that align with both international best practices and Islamic ethical values.

Despite the significance of these findings, this study is not without limitations. First, it focuses solely on Sharia-compliant firms listed in Saudi Arabia, which may limit the generalizability of the results to other Islamic or conventional markets. Second, the study used firm size as the only moderating variable, which may not capture the full complexity of contextual factors influencing the ESG–value creation relationship. Third, while the study employed dynamic panel data methods (system GMM), the ESG scores used may not fully reflect qualitative differences in disclosure quality or content. Fourth, the value of disaggregating the ESG score by pillar is not conducted in the current study to avoid excessive length and complexity of the paper and tables. The focus remains on the total ESG score to maintain clarity and conciseness.

Future research should explore additional moderating or mediating factors—such as board composition, ownership structure, or cultural dimensions—that could influence the ESG–value nexus in Islamic finance. It is also recommended that future studies disaggregate the ESG score by pillar (environmental, social, governance) to uncover potentially distinct effects. Comparative studies between Sharia-compliant and conventional firms across different countries would also provide richer insights into the differential impacts of ESG. Furthermore, qualitative studies investigating how investors in Islamic markets perceive ESG disclosures would add depth to the existing quantitative evidence.

Based on the findings, the author recommends that policymakers and regulatory bodies such as Tadawul and the Capital Market Authority develop ESG disclosure frameworks specifically tailored to the unique characteristics and values of Sharia-compliant firms. Collaborating with Sharia scholars, industry experts, and investors to design sector-specific and religion-sensitive ESG reporting standards would improve transparency, comparability, and investor confidence. Furthermore, these tailored frameworks would encourage firms to integrate sustainability more deeply into their strategies, fostering long-term value creation. To facilitate effective adoption, capacity-building initiatives and guidance on ESG integration for Sharia-compliant companies should also be implemented. By adopting these targeted policy measures, regulators can better align Islamic finance practices with global sustainability goals while respecting cultural and religious norms, ultimately promoting sustainable development in emerging markets.