Effect of ESG Financial Materiality on Financial Performance of Firms: Does ESG Transparency Matter?

Abstract

Transparency in ESG financial materiality disclosure by corporations is now in doubt due to the inconsistent ESG framework that governs ESG disclosures, particularly in developing nations like South Africa. This is evident in the financial performance of banks and manufacturing firms as a result of the higher rate of susceptibility to ESG issues. Hence, this study empirically investigated the effect of ESG financial materiality disclosure on the financial performance of banks and manufacturing firms in South Africa from 2015 to 2024. Also, the moderating role of ESG transparency on the relationship between ESG financial materiality disclosure and financial performance was investigated. Descriptive analysis, a correlation matrix, and panel regression analysis were employed for analysis purposes. The financial metrics include ROA, ROE, and Tobin’s Q, while ESG financial materiality disclosure and the ESG disclosure score of the firms were the independent variable and moderating variable, respectively. The results show that ESG financial materiality exerts a significant adverse impact on ROA and ROE but an insignificant positive effect on Tobin’s Q in banks. For manufacturing firms, the impact is insignificant and negative on ROA, ROE, and Tobin’s Q. Also, the interactive effect of transparency insignificantly weakens the effect of ESG financial materiality disclosure on financial performance in both banks and manufacturing firms. This concludes that the transparency in ESG financial materiality disclosure is not sufficient to improve financial performance in both sectors but should be integrated in the core business objectives of firms. Also, it suggests that over-disclosure and greenwashing of ESG reports should be avoided.

1. Introduction

The use of ESG financial materiality and ESG transparency has replaced the traditional method of evaluating an organization’s performance and viability through the prism of financial ratios or metrics. However, the quality of transparency in ESG disclosure by firms is a huge concern to stakeholders as a result of its possible effect on their investment decisions and overall performance of the firm. Although there is previous extant literature on the relationship between ESG disclosure and firm performance, the impact of ESG financial materiality issues on financial performance is underexplored, especially in developing countries like South Africa. More so, the moderating role of ESG transparency on the relationship between ESG financial materiality and financial performance has received little or no attention. Hence, this study investigates the direction of the impact of ESG financial materiality disclosure on financial performance and how well the quality (transparency) of ESG disclosure can affect the financial performance of banks and manufacturing firms in South Africa.

The era of assessing the performance and viability of an organization through the lens of financial ratios or metrics has encountered a paradigm shift to the utilization of ESG financial materiality and ESG transparency. Stakeholders are more concerned about the transparency of the environmental, social, and governance issues (ESG) reported by the firm (Sandu, 2023). Since businesses are an integral part of society, their operations without sustainable goals in view may truncate financial performance. The introduction of ESG disclosure to financial and non-financial firms is to make them environmentally and socially responsible with quality governance in their day-to-day operations so as to preserve the environment where they operate and improve financial performance (Clementino & Perkins, 2021). Particularly, the transparency in the ESG issues surrounding the financial materiality of firms’ financial statement can either boost or retard organizational growth. Therefore, ESG adoption and monitoring has become an area of concentration for firms, regulators, governments, and general stakeholders to bolster sound financial and investment decisions. However, the level of transparency of ESG disclosure is a major concern on the influence of financial materiality on corporate performance (Michelon et al., 2015).

This transparency issue entails an instance where firms avoid reporting environmental, social, and governance factors influencing the materiality of the financial records and overall corporate performance (Krishnan et al., 2024). The level of transparency of ESG disclosures will assist stakeholders have an accurate view of firms sustainability strategies which can impact financial decisions, investment decisions and financial stability (Worthington-Smith & Giamporcaro, 2021). However, the use of different and inconsistent reporting frameworks by international frameworks like the Global Reporting Initiative (GRI) and Integrated Reporting (IR) remains a significant obstacle to ensure a high level of transparency and assess its influence on the relationship between ESG financial materiality and firm performance (Khan et al., 2016). In South Africa, specifically, companies adopt different sustainability frameworks, which makes some companies provide detailed ESG information while some others give selective information. This makes it difficult to determine the influence of ESG materiality on corporate performance across sectors. Although some studies have been carried out on the relationship between ESG materiality disclosure and financial performance, little or no research has investigated the moderating role of ESG transparency in the relationship in emerging markets, especially in South Africa. The complexity of the South African socio-economic and regulatory framework has escalated the rate at which sustainability goals have been enforced and embraced by companies such as banking and manufacturing firms in a bid to ensure survival (Sandu, 2023). This also makes the South African economy a good case study for this study.

For instance, in recent times, the banking sector has become the major driver of ESG adoption in the South African economy as a result of pressures received from investors, regulators, and other stakeholders (Lamanda & Tamásné Vőneki, 2024). As custodians of the hard-earned funds of the populace, the need to embrace transparency in their ESG financial materiality score is paramount to building the confidence of existing and potential customers (Evans et al., 2023). Though some of these banks pursue the integration of ESG, the quality of consistency of their disclosures poses a great concern for transparency. Similarly, South African manufacturing firms are as important as the banks because of their significant contributions to the growth rate of the country; susceptibility to environmental, social, and governance factors; and the nature of their operations (Buallay, 2019). They include the automotive industries, chemical and plastic industries, consumer goods, health care industries, and some of the basic material firms. The activities of these firms have hazardous impacts on the environments where they operate in the form of carbon emissions, waste pollution, etc. This makes industries face the pressure of incorporating sustainability goals in their operations. Yet, the level of their transparency varies and exerts some implications on the financial performance of these firms. Therefore, the role of ESG transparency on the relationship between ESG financial materiality for better performance in both sectors calls for further investigation.

Hence, this study attempts to fill the identified gaps by investigating the moderating role of ESG disclosure transparency in the nexus of ESG financial materiality and corporate performance by comparing South African banks and manufacturing banks in a single study with the same time frame. Previous studies either combined sectors or focused on a sector in a single study. Comparing sectors will provide practical insights on the level of ESG transparency and its role in moderating the effect of ESG Financial materiality on financial performance in each sector. The rest of the paper is sectionalized as follows: Section 2, literature review; Section 3, research methodology; Section 4, results and discussions of findings; and Section 5, conclusions, practical implications of the findings, limitations of this study, and suggestions for further studies.

2. Literature Review

2.1. ESG Financial Materiality Versus ESG Disclosure

Many works in the extant literature have investigated the issues of environmental, social, and governance disclosures to assess the sustainability of firms in different dimensions. However, its observed that the terms “ESG Financial Materiality” and “ESG Disclosure” are used interchangeably in different studies to portray the same thing, whilst they have different meanings and areas of concentrations (Khan et al., 2016). Though ESG disclosure scores and ESG financial materiality are related, they are not to be assumed to mean the same thing. The major distinction between the two terms is the financial materiality of the disclosure (Katz & McIntosh, 2021).

ESG disclosure includes the environmental, social, and governance concerns that affect the overall operations of a firm which must be communicated to all the stakeholders of the firm. Some of the issues are carbon emissions, governance structures, and diversity initiatives, to mention but a few. On the other hand, ESG financial materiality involves the ESG issues that specifically affect the financial output or performance of firms. The disclosures include ESG risks that affect asset valuations, future cash flow, etc. (SASB, 2017). ESG disclosures can be used to moderate the relationship between ESG financial materiality disclosure and the financial performance of a firm by influencing the perceptions and responses of stakeholders on ESG financial materiality issues. When transparency is perceived in ESG disclosure, it builds stakeholders’ trust and reduces information asymmetry, which could influence investors’ confidence, the loyalty of customers, and staff engagement (Clarkson et al., 2011). Contrarily, ambiguity in ESG disclosure would adversely moderate ESG financial materiality disclosure even though the firm is conscientiously addressing ESG issues surrounding financial materiality (Dhaliwal et al., 2012).

2.2. Empirical Review and Hypothesis Development

Undoubtedly, several scholars, academics, and researchers have delved into investigating how ESG disclosure by firms affect their corporate performance; however, they have obtained divergent results. These contradicting outcomes also have support or validation from various theories, which either validate or criticize the findings of previous studies. Some of these theories are stakeholder theory, legitimate theory, agency theory, the neoclassical school of thought, etc. These inconclusive assertions ignite the need for further exploration by this study.

For instance, the stakeholder theory developed by Edward Freeman in 1984 proposed that the long-term survival of a firm is dependent on the importance given to its stakeholders. This suggests that the ethical consideration of employees, customers, suppliers, investors, and communities including the shareholders will improve the performance of the firm and its sustainable practices. This is supported by Johnson (2020), who noted that ESG exposure increases the cost of capital of debt providers, enhances firms’ efficiency, and boosts stock price performances (Ha et al., 2024). The legitimate theory also corroborates the assertions of Edward in 1984 by confirming that ESG disclosure influences financial performance positively by securing market access and averting costly disruptions (Michelon et al., 2015). The theory further posits that ESG disclosures legitimize a firm’s relationship with the environment it operates in for better performance (Canli & Sercemeli, 2025). As such, the stakeholder theory and legitimate theory assume that the effect of ESG financial materiality is significantly positive on firm performance. Sandu (2023) supports these theories by discovering that low ESG scores affect the portfolio outcomes of South African firms. Comparing the banking and manufacturing sectors, Buallay (2019) reports that the effect of ESG disclosure on the financial performance of manufacturing companies is significantly positive on operational (ROA), financial (ROE), and market (Tobin’s Q) performance. Thi Thu Loan et al. (2024) also investigated the effect of ESG disclosure on commercial banks in Vietnam between 2018 and 2022. The result showed that ESG has a positive and significant effect on ROA, ROE, and net interest margin (NIM). Carnini Pulino et al. (2022) posit further that the investment of firms in sustainable practices increases their revenue value and overall performances.

Contrarily, the neoclassical school of thought is of the belief that the use of sustainability practices would increase costs incurred by firms and thereby reduce the wealth of shareholders. The theory further focuses majorly on profit maximization and views ESG issues as immaterial to financial performance due to the additional costs that would be incurred (Kumaria Puri, 2022). Agency theory also supports this notion by emphasizing that organizational goals should be shareholder-centric since the managers of a firm are expected to act in the best interest of the shareholders. Hence, the inclusion of ESG practices is viewed to reduce profits generated by the firm. Agency theory and the neoclassical school of thought have the support of some researchers that ESG financial materiality has no impact on firm performance. These include (Buallay, 2019; Kumar et al., 2022; Lamanda & Tamásné Vőneki, 2024; and Matemane & Wentzel, 2019), who posit that the effect is adversely insignificant on banks’ financial performance. The study of Evans et al. (2023) on the South African mining industry from 2008 to 2020 shows that the effect is non-substantial on the financial performance of mining firms. Kumar et al. (2022) reports that among several companies in Zealand, the impact of ESG disclosure is significantly negative on measures of performance but positively insignificant on company size.

Junius et al. (2020) examined the effect of ESG disclosure on the firm performance of four ASEAN countries from 2013 to 2017. Using panel regression analysis, the study showed that ESG score has no significant impact on return on assets, ROE, and Tobin’s Q, though it exerts a positive impact on ROE and Tobin’s Q. This shows that the ESG scores of companies in ASEAN countries are yet to be employed for the performance measurement of companies. Also, the level of sustainable practices by the firms could be very weak to improve firm value. Almeyda and Darmansya (2019) support the findings of Junius et al. (2020) that most countries are yet to embrace ESG practices as a measure of performance for firms. The study showed that social and governance factors have no significant influence on the financial performance of firms in the G7; however, environmental factors proved to have significant and positive effects on return on assets and capital employed. Maji and Lohia (2023) employed simultaneous quartile regression and ordinary least square analysis to ascertain how ESG performance can translate to financial performance for 222 of Indian companies. The study revealed that most of the firms concentrated on governance and social sustainability issues which improved the firm’s financial outcome as a result of thd rate of response to the demands of the societies and norms of where the firms operate. In line with the findings of Maji and Lohia (2023), national culture was employed as a moderating factor on the relationship between ESG disclosure and firm performance in the works of Wasiuzzaman et al. (2023). The results showed that some cultural dimensions significantly moderate the relationship. This indicates that the consideration of the societal cultural orientation peculiar to where a firm operates can bolster financial performance while strategizing towards sustainability practices.

Investigations on manufacturing firms shows that there is growing debate, though scant, on the ESG issues around its operations as a result of challenges peculiar to the sector, such as a lack of consistency in ESG data integration (Aziz & Alshdaifat, 2024), high carbon emissions and environmental compliance costs (Ye & Xu, 2023), and the existence of different numbers of divisions (Buallay, 2019). These challenges and more faced by manufacturing firms pose some uncertainty on the quality of the ESG disclosures, which has ripple effect on the level of performance. This necessitates the need to investigate the effectiveness of ESG disclosures on the financial performance of manufacturing firms. However, studies on the relationship between ESG financial materiality and financial performance have received little attention (Buallay, 2019), especially in emerging markets. In addition, the pivotal role of banks in an economy is so germane as to neglect the sustainability issues faced by this industry and the frequent country-specific changes that affect their operations. Though there are related previous studies on banks, the effect of ESG financial materiality on financial performance remains underexplored in the South African context, considering country-specific factors.

Therefore, considering the various arguments and the need to ensure sustainable development, this study assumes a relationship on the standpoint of the stakeholder and legitimate theories. More so, the paucity of this subject matter in the area of comparison between banking and manufacturing firms, especially in South Africa, necessitates the need to contribute to the existing literature. As such, this study hypothesizes the following:

H1. 

ESG financial materiality has a significant positive effect on the financial performance of banks and manufacturing firms in South Africa.

H1_a. 

ESG financial materiality has a significant positive effect on the ROA of banks in South Africa.

H1_b. 

ESG financial materiality has a significant positive effect on the ROE of banks in South Africa.

H1_c. 

ESG financial materiality has a significant positive effect on the Tobin’s Q of banks in South Africa.

H1_d. 

ESG financial materiality has a significant positive effect on the ROA of manufacturing firms in South Africa.

H1_e. 

ESG financial materiality has a significant positive effect on the ROE of manufacturing firms in South Africa.

H1_f. 

ESG financial materiality has a significant positive effect on the Tobin’s Q of manufacturing firms in South Africa.

In a bid to increase the significance of ESG disclosure on financial performance, some previous studies suggested moderating factors such as Stakeholder engagement, financial constraint, and religiosity (Ho et al., 2024). Findings revealed that stakeholder engagement positively moderated the effect of ESG disclosure for the understudied firms. Also, financial slack is a significant intervening factor that can improve the relationship between ESG disclosure and financial performance (Kumar et al., 2022). This suggests that sufficient financial resources in a firm encourage the adoption of ESG practices and result in improved performance. The study of Yeye and Egbunike (2023) revealed that profitability is not a good intervening factor to bolster this relationship in manufacturing firms. Other moderating factors that are understudied include sustainability management and top management commitment (Rahman et al., 2023), board diversity (Alodat & Hao, 2025), and ownership structure (Wu et al., 2022). However, little or no studies have considered employing the moderating role of ESG transparency on the impact of ESG financial materiality on financial performance.

The moderating role of ESG transparency on the relationship between ESG financial materiality is theoretically underpinned by stakeholder theory, which assumes that the role of a firm in relation to stakeholders should not be concentrated on shareholders only. It is relatable to ESG issues on the ground that the transparency of ESG practices by firms has a direct effect on the perception of stakeholder and increases the financial benefits of ESG policies. Stakeholders are more inclined to support a company when ESG disclosures are transparent, consistent, and credible (Yu et al., 2018). The legitimacy theory added that since firms are concerned about the social implications of their operations, the level of transparency in ESG reporting makes them reliable. The perceived legitimacy by stakeholders can protect businesses from unfavorable market conditions, especially in risky industries. As such, transparency in ESG disclosures helps reduce the costs related to ESG-related investments (Michelon et al., 2015).

To corroborate the need to investigate the intervening role of ESG transparency, Yu et al. (2018) examined the effect of ESG transparency on firm value. The ESG disclosure score of large companies represented ESG transparency, while Tobin’s Q captured firm value. The result showed that the higher the level of ESG transparency for a firm, the better its performance. This suggests that ESG transparency reduces information asymmetry and agency costs for investors. Also, Gerged et al. (2023) identified that the use of global sustainability reports, certification, and integrated global transparency by firms bolsters market value. Haggin (2024) assessed the interaction between of ESG reporting and deceptive language usage among companies listed by the US Securities and Exchange Commission. Findings show that the presence of deceptive language influenced the effect of ESG disclosures on the market valuation of the firms. This suggests that a lack of credibility in ESG reporting may result in outrageous perception in the ESG performance of firms. Schwoy et al. (2025) added that the quality of ESG disclosure by firms is not satisfactory, especially in pharmaceutical companies, as about 26% of the controversies around their ESG issues are not reported. Yu et al. (2020) also opined that the act of greenwashing ESG reports by firms is a barrier to ESG integration while making investment decisions. The role of sustainability disclosure on performance was investigated in the works of Hummel & Schlick (2016), who concluded that high-quality disclosure results in better performance and vice versa. Numerous studies have discovered evidence of ESG disclosure’s inconsistency, accuracy, and deceit, indicating that businesses may employ it as a tactical tool to influence or deceive their stakeholders or regulatory authorities, hence the need for further investigations as an intervening factor.

Hence, the second hypothesis for this study is stated thus:

H2. 

The moderating role of ESG transparency disclosure is significant on the relationship between ESG financial materiality and a firm’s financial performance.

H2_a. 

The moderating role of ESG transparency disclosure is significant on the relationship between ESG financial materiality and financial performance of banks in South Africa.

H2_b. 

The moderating role of ESG transparency disclosure is significant on the relationship between ESG financial materiality and financial performance of manufacturing firms in South Africa.

3. Materials and Method

In a bid to achieve the objectives of this study, the population of this study constitutes all the 44 companies listed in the FTSE/JSE responsible investment index as of the year 2024. However, the sample of this study includes all the manufacturing firms and banks with headquarters in South Africa. The period of investigation spans from 2015 to 2024. The choice of the year 2015 is because it was the year the JSE made arrangement with the FTSE to act as the platform that monitors the ESG goal compliance by firms in South Africa, while the year 2024 is the latest period for which annual reports of the companies could be obtained.

In order to analyze the relationship between ESG financial materiality and the financial performance of manufacturing firms and banks, this study employs descriptive statistics, correlation analysis, and the panel regression method, which includes the fixed-effect (FEM) and random-effect models (REM). The descriptive analysis and correlational matrix were employed as preliminary tests, which is important for analyzing the trend among the variables and for the development of models. The panel regression model is appropriate for analyzing the models of this study as it accounts for both time series and cross-sectional differences with robust estimation. It also takes into account unobservable heterogeneity and adjusts for time-invariant firm-specific characteristics by using the fixed or random effect with cluster option.

However, the robust Hausman test was employed to choose an appropriate model suitable for this study between fixed effects and random effects. The Driscoll and Kraay estimator was employed to account for any presence of heteroskedasticity, auto-correlation, and cross-sectional dependency in the model, as suggested in the works of (Hoechle, 2007).

The models for this study are specified thus:

Hypothesis 1:

$$RO{A}_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}MCA{P}_{it}+{\beta }_{3}DE{R}_{it}+{\beta }_{4}IN{F}_{it}+{\epsilon }_t$$

(1)

$$RO{E}_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}MCA{P}_{it}+{\beta }_{3}DE{R}_{it}+{\beta }_{4}IN{F}_{it}+{\epsilon }_t$$

(2)

$$Tobin Q_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}MCA{P}_{it}+{\beta }_{3}DE{R}_{it}+{\beta }_{4}IN{F}_{it}+{\epsilon }_{it}$$

(3)

Hypothesis 2:

$$RO{A}_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}ES{G}_{it}\times TR{S}_{it}+{\beta }_{3}MCA{P}_{it}+{\beta }_{4}DE{R}_{it}+{\beta }_{5}IN{F}_{it}+{\epsilon }_t$$

(4)

$$RO{E}_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}ES{G}_{it}\times TR{S}_{it}+{\beta }_{3}MCA{P}_{it}+{\beta }_{4}DE{R}_{it}+{\beta }_{5}IN{F}_{it}+{\epsilon }_t$$

(5)

$$Tobin Q_{it}={\beta }_0+{\beta }_{1}ES{G}_{it}+{\beta }_{2}ES{G}_{it}\times TR{S}_{it}+{\beta }_{3}MCA{P}_{it}+{\beta }_{4}DE{R}_{it}+{\beta }_{5}IN{F}_{it}+{\epsilon }_t$$

(6)

According to the

Table 1. Sources and measurement of variables.

Variables	Measurement	Sources
Return on Assets (ROA)	Profit before interest and tax(PBIT) Total assets	Bloomberg terminal
Return on Equity (ROE)	Profit before interest and tax (PBIT) Total equity	Bloomberg terminal
Tobin’s Q	MCAP + total debt Total assets	Bloomberg terminal
MCAP	Market capitalization	Bloomberg terminal
DER	Debt-to-equity ratio	Bloomberg terminal
INF	Inflation rate	Bloomberg terminal
ESG	ESG financial materiality disclosure score	Bloomberg terminal
ESG Transparency (TRS)	Overall ESG disclosure score	Bloomberg terminal

ROA = PBIT divided by Total asset; ROE = PBIT/Total equity; Tobin Q = (MCAP+ total debt)/Total asset. Source: Authors’ design (2025).

, the dependent variables (ROA_it, ROE_it, Tobin Q_it) are the measures of financial performance of the banks and manufacturing firms at time t, while the independent variable (ESG_it) is the proxy for the overall ESG financial materiality disclosure of each firm (i) at time (t). The firm-specific variables are the debt-to-equity ratio (DER_it) and market capitalization (MCAP_it) of each firm (i) at time (t). Inflation (INF_it) represents the country-specific variables. The firm- and country-specific variables, which are the control variables in the model, were included to capture the influence of firm- and country-related factors that affect the financial performance of firms. In addition, transparency (ESG_it × TRS_it) captures the moderating role of ESG transparency on the relationship between ESG financial materiality disclosure and firm performance. The financial metrics (ROA, ROE, Tobin’s Q) were employed as a result of their consistency in usage by previous studies to account for accounting and market-based performance, which also confirms the reliability of the variables (Buallay, 2019; Kumaria Puri, 2022; Thi Thu Loan et al., 2024). Based on the Sustainability Accounting Standards Board (SASB) framework, ESG-related variables are chosen with emphasis on each industry’s financially significant ESG disclosures. These factors make it possible to examine ESG transparency from a sector-sensitive perspective. Market capitalization is used as a control variable due to its influence on stakeholders’ perception of the firms. More so, prior studies found that the size of a firm could influence the relationship between the credibility of ESG practices and the performance of a firm (Yu et al., 2018). Also, Gerged et al. (2023) assert that larger organizations use credibility in their ESG disclosure as a signaling weapon to prove performance. Also, the debt-to-equity ratio determines the leverage level of a firm, which may either increase its financial risk or otherwise. As such, firms with a high debt profile may prefer meeting short-term obligations to long-term ESG goals, which may result in a decline in the influence of sustainability practices on firm performance and ESG transparency (Nurdiati et al., 2023). The inflation rate captures macro-economic variables that could affect profitability and investment decisions, especially in developing countries as South Africa.

4. Results and Discussion

The descriptive analysis in

Table 2. Descriptive statistics.

Banks					Manufacturing Firms
Variable	Mean	Maximum	Minimum	Std	Mean	Maximum	Minimum	Std
ROA	1.893	5.710	0.3700	1.537	6.058	120.81	−25.392	16.814
ROE	16.388	26.510	0.000	6.227	11.465	297.28	−128.065	44.168
TOBIN Q	0.890	0.921	0.777	0.045	0.541	0.874	0.270	0.1417
ESG	2.589	4.850	0.000	1.289	2.76	5.160	0.000	1.182
MCAP	7.900	467.03	0.0001	−0.320	9.749	580.57	0.0017	74.941
DER	112.44	303.95	0.000	81.72	83.391	419.66	2.140	75.737
INF	4.95	7.200	3.000	1.347	4.950	7.200	3.000	1.347
TRS	46.090	68.100	0.000	20.109	49.95	71.461	0.000	18.306

Source: Authors’ computation (2025).

for both sectors reveal some notable differences, as the average return on assets (ROA) of 6.508% for manufacturing firm is higher compared to the value for banks at 1.893%, suggesting that manufacturing firms are more efficient in the utilization of asset to generate returns. However, the range of variation in the ROA (16.814) for manufacturing firms is higher, indicating potential volatility in financial performance, while the ROA of banks is less volatile (1.53). With an average ROE of 16.388%, banks perform better than manufacturing firms, with an average of 11.465%.

However, manufacturing firms have a wider range and a higher standard deviation, suggesting that profitability varies within the industry. Both sectors have Tobin’s Q ratios below 1, with manufacturing companies at 0.541 and banks at 0.890. This suggests that the market values these companies below their replacement costs, which could be a sign of undervaluation or inefficiency.

In terms of financial structure, the average debt-to-equity ratio (DER) for banks is greater at 112.44, which reflects higher leverage on deposits. In contrast, industrial enterprises have an average DER of 83.391, which indicates considerable but relatively lesser leverage. Manufacturing firms have a higher average market capitalization (MCAP) of 9.749 than banks, which have an MCAP of 7.900. However, the manufacturing sector’s high standard deviation indicates that firm sizes vary significantly within the sector. Both industries’ average inflation rates are 4.95%, which suggests a steady macroeconomic climate over the study period. The financial measurements of banks are generally more stable, whereas manufacturing firms exhibit greater fluctuation and larger potential returns, indicating that the risk and return profiles of the two sectors differ.

Both industries have reasonable averages for environmental, social, and governance (ESG) scores; manufacturing companies have slightly higher averages at 2.76 and banks have 2.589, on a scale where higher scores indicate better ESG performance. Since both sectors have 0 minimum scores, it is possible that some businesses may not have any ESG reporting or initiatives in some periods. ESG disclosures could be improved, as evidenced by the modest ESG transparency scores (TRS) of banks and manufacturing companies, which average 46.090 and 49.95, respectively. Firms with higher ESG scores tend to have greater returns and market valuation, which is consistent with this result, showing that strong ESG policies can improve financial performance.

The correlation matrix in

Table 3. Correlation analysis (banks).

Variables	ROA	ROE	TOBIN Q	ESG	MCAP	DER	INF	TRS
ROA	1
ROE	0.7859	1
TOBIN Q	−0.9560	−0.6092	1
ESG	−0.2916	−0.2907	0.2467	1
MCAP	−0.0273	0.1112	0.0834	0.1094	1
DER	−0.5499	−0.5923	0.4694	0.1251	0.1144	1
INF	0.00027	0.0332	−0.0359	0.3902	−0.1802	−0.013	1
TRS	−0.2087	−0.2627	0.1507	0.8908	0.077	0.2136	0.3070	1

Source: Authors’ computation (2025).

reveals that the correlation between the measures of financial performance for banks (ROA, ROE, TOBIN Q) and ESG (−0.2916, −0.2907, 0.2467), MCAP (−0.0273, 0.1112, 0.0834), DER (−0.5499, −0.5923, 0.4694), and INF (0.00027, 0.0332, −0.0359) is moderately low since the values are less than 0.80. This implies that the variables used for this study are free from the problem of multicollinearity, which could affect the outcome of this study.

The correlation matrix in

Table 4. Correlation analysis (manufacturing firms).

Variables	ROA	ROE	TOBIN Q	ESG	MCAP	DER	INF	TRS
ROA	1
ROE	0.9769	1
TOBIN Q	−0.2548	−0.2482	1
ESG	0.0834	0.0929	−0.1655	1
MCAP	−0.1873	−0.1821	0.2178	−0.014	1
DER	−0.3493	−0.4219	0.7153	−0.255	0.1969	1
INF	0.2794	0.2698	−0.0722	0.4478	−0.1969	−0.1721	1
TRS	−0.1297	−0.1250	−0.0776	0.7259	0.0233	0.0518	0.3996	1

Source: Authors’ computation (2025).

reveals that the correlation between the measures of financial performance (ROA, ROE, TOBIN Q) and ESG (0.0834, 0.0929, −0.1655), MCAP (0.1873, −0.1821, 0.2178), DER (−0.3493, −0.4219, 0.7153), and INF (0.2794, 0.2698, −0.0722) is also moderately low for manufacturing firms, since most of the values are less than 0.80. This suggests the absence of the problem of multicollinearity among the variables we used.

Table 5. Panel regression for banks using robust standard error with cluster option.

Variables	ROA		ROE		Tobinq
	FE	RE	FE	RE	FE	RE
ESG	−0.0930 *	−0.0947 ***	−1.0440 **	−1.0490 ***	0.0003	0.0004
	(0.0362)	(0.0278)	(0.3820)	(0.2760)	(0.0008)	(0.0008)
MCAP	0.0017 ***	0.0018 ***	0.0301 ***	0.0301 ***	1.64 × 10−5 **	1.56 × 10−5 ***
	(0.0003)	(0.0002)	(0.0020)	(0.0013)	(5.04 × 10−6)	(5.02 × 10−6)
DER	0.0009	−0.0008	0.0014	−0.0204 **	−1.52 × 10−6	1.58 × 10−5
	(0.0010)	(0.0011)	(0.0091)	(0.0091)	(5.14 × 10−5)	(4.51 × 10−5)
INF	0.0497	0.0493	0.7880	0.7710 *	−0.0012 *	−0.0012 *
	(0.0564)	(0.0513)	(0.4700)	(0.3970)	(0.0006)	(0.0006)
Constant	1.7710 ***	1.9680 **	14.800 ***	17.340 ***	0.8960 ***	0.8940 ***
	(0.2420)	(0.9300)	(2.4220)	(3.4120)	(0.0028)	(0.0204)
Robust Hausman test
Sargan-Hansen Statistic	636.056 *** (0.000)		97.442 *** (0.000)		119.207 *** (0.000)
Testing for Cross-Sectional Dependence on the preferred model (i.e., Fixed Effect (FE))
Pesaran CD test	4.156 *** (0.000)		3.541 *** (0.000)		1.478 (0.139)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. The robust Hausman test is a test of overidentifying restrictions (fixed vs. random effects) and the test is performed based on Sargan–Hansen statistic. Source: Authors’ computation (2025).

reveals the panel regression result for banks using standard error estimation with a cluster option on the effect ESC financial materiality on the performance of banks. The result of the robust Hausman test reveals that the null hypothesis that the random test is appropriate is rejected for the effect of ESG financial materiality on banks’ performance in this study. Therefore, the fixed-effect result is retained.

However, the result of the cross-sectional dependence indicates that the residuals are dependent across banks for ROA (p-value, 0.000) and ROE (p-value, 0.000) models, which may be attributable to regulatory or macroeconomic issues, while there is an absence of cross-sectional dependence across banks for Tobin’s Q (p-value, 0.139) model.

Therefore, as a result of the cross-sectional dependence in the models for ROA and ROE, the fixed-effect result for Tobin’s Q will only be retained for interpretation and discussion on the relationship between ESG financial materiality and banks’ performance. The models for ROA and ROE will be subjected to further estimation using the Driscoll–Kraay standard errors estimator to account for the presence of cross-sectional dependence among the variables to ensure reliable results.

The results in

Table 6. Panel regression results for banks using Driscoll–Kraay standard error for ROA and ROE.

Variables	ROA		ROE
	FE	RE	FE	RE
ESG	−0.0930 * (0.0426)	−0.0947 (0.0625)	−1.0440 * (0.5120)	−1.0490 (0.6380)
MCAP	0.0017 *** (0.0002)	0.0017 *** (0.0002)	0.0301 *** (0.0042)	0.0301 *** (0.0034)
DER	0.0009 * (0.0004)	−0.0007 (0.0016)	0.0013 (0.0046)	−0.0204 ** (0.0078)
INF	0.0497 (0.0374)	0.0493 (0.0391)	0.7880 (0.4500)	0.7710 (0.4330)
Constant	1.7710 *** (0.1570)	1.968 ** (0.658)	14.80 *** (1.718)	17.34 *** (3.079)
F-test	13.540 *** (0.000)		17.890 *** (0.000)
Wald-chi2		85.950 *** (0.000)		116.00 *** (0.000)
Hausman test (Chi2)	6.1002 ** (0.0266)		9.9500 ** (0.0412)

Note: The values in parentheses as related to the coefficients are standard errors, while they are probability values in the case of F-test and Wald-chi2 (*** p < 0.01, ** p < 0.05, * p < 0.1). Source: Authors’ computation (2025).

correct the presence of cross-sectional dependence presented in Table 5 for ROA and ROE by employing Driscoll–Kraay standard errors, which is more reliable and robust. The result of the Hausman test reveals that the models are statistically significant; as such, the null hypothesis that the random test is appropriate is rejected for the effect of ESG financial materiality on banks’ performance in this study. Therefore, the fixed effect is chosen for the interpretation of findings and discussion for ROA and ROE using the Driscoll–Kraay standard error estimator.

In a bid to determine the impact of ESG financial materiality disclosure on the financial performance (ROA, ROE, and Tobin’s Q) of banks, the fixed-effect result in

Table 7. Panel regression for banks (without moderation).

Variables	ROA	ROE	TOBIN Q
	Fixed Effect	Fixed Effect	Fixed Effect
ESG	−0.0930 *	−1.0440 *	0.0003
	(0.0426)	(0.5120)	(0.0008)
MCAP	0.0017 ***	0.0301 ***	1.64 × 10−5 **
	(0.0003)	(0.0042)	(5.04 × 10−6)
DER	0.0009 *	0.0014	−1.52 × 10−6
	(0.0005)	(0.0046)	(5.14 × 10−5)
INF	0.0497	0.7880	−0.0012 *
	(0.0374)	(0.4500)	(0.0006)
Constant	1.7710 ***	14.800 ***	0.8960 ***
	(0.1570)	(1.7180)	(0.0028)
Diagnostic test
Hausman (Chi2)	6.1002 ** (0.0266)	9.9500 ** (0.0412)	119.207 *** (0.000)

Note: Standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. Authors’ computation (2025).

shows that disclosure of ESG issues related to financial outcome have a negative and significant effect on return on assets (ROA) and return on equity (ROE), while it is positive but insignificant on Tobin’s Q. Regarding the effect of MCAP on financial performance, the result shows a positive and significant effect on ROA, ROE, and Tobin’s Q. The inflation rate (INF) exerts a insignificant effect on ROA and ROE but exerts only a negative and significant effect on Tobin’s Q. The debt-to-equity ratio, on the other hand, exerts an insignificant impact on all the measures of financial performance except ROA, while only Tobin’s Q has a negative relationship with DER.

Using robust standard error with the cluster option for manufacturing firms,

Table 8. Panel regression for manufacturing firms using robust standard error with cluster option.

Variables	ROA		ROE		TOBIN Q
	FE	RE	FE	RE	FE	RE
ESG	−2.5670	−2.1970	−6.4140	−5.7310	−0.0070	−0.0073
	(1.9390)	(1.3410)	(4.9830)	(3.557)	(0.0066)	(0.0074)
MCAP	−0.0110	−0.0137	−0.0200	−0.0248	0.0001 ***	0.0001 ***
	(0.0107)	(0.0087)	(0.0264)	(0.0209)	(2.06 × 10−5)	(2.65 × 10−5)
DER	−0.0612 **	−0.0662 ***	−0.2520 ***	−0.2450 ***	0.0007 *	0.0007 ***
	(0.0158)	(0.0137)	(0.0299)	(0.0360)	(0.0003)	(0.0003)
INF	3.7920	3.5710	8.7280	8.4730	0.0029	0.0040
	(2.7510)	(2.5260)	(6.8510)	(6.1930)	(0.0038)	(0.0041)
Constant	−0.5040	0.0299	6.9320	5.8540	0.4930 ***	0.4810 ***
	(8.6410)	(7.1580)	(23.950)	(19.610)	(0.0381)	(0.0495)
Robust Hausman test
Sargan-Hansen Statistic	76.540 *** (0.000)		12.474 ** (0.014)		119.207 *** (0.000)
Testing for Cross-Sectional Dependence on the preferred model (i.e., Fixed Effect (FE))
Pesaran CD test	0.829 (0.407)		0.322 (0.747)		3.450 (0.443)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. The robust Hausman test is a test of overidentifying restrictions. Source: authors’ computation (2025).

includes the result of the Hausman test, which depicts that all the models are statistically significant; as such, the null hypothesis that the random test is appropriate is rejected for the effect of ESG financial materiality on the performance of manufacturing firms in this study. Also, the result of the cross-sectional dependence indicates that the residuals are independent across manufacturing firms for all the models (ROA, ROE, Tobin’s Q), indicating the absence of cross-sectional dependence in the models for manufacturing firms.

Therefore, since there is an absence of cross-sectional dependency across the firms in the three models, the fixed effect is retained for the interpretation of findings and discussions (See

Table 9. Panel regression for manufacturing firms (without moderation).

Variables	ROA	ROE	TOBIN Q
	Fixed Effect	Fixed Effect	Fixed Effect
ESG	−2.5670	−6.4140	−0.0070
	(1.9390)	(4.9830)	(0.0066)
MCAP	−0.0110	−0.0200	0.0001 ***
	(0.0107)	(0.0264)	(2.06 × 10−5)
DER	−0.0612 **	−0.2520 ***	0.0007 *
	(0.0158)	(0.0299)	(0.0003)
INF	3.7920	8.7280	0.0029
	(2.7510)	(6.8510)	(0.0038)
Constant	−0.5040	6.9320	0.4930 ***
	(8.6410)	(23.950)	(0.0381)
Diagnostic test
Hausman (Chi2)	76.540 *** (0.000)	12.474 ** (0.014)	119.207 *** (0.000)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. Source: Authors’ computation (2025).

).

The result in Table 9 for manufacturing firms reveals that ESG financial materiality (ESG) has insignificant negative effect on ROA, ROE, and Tobin’s Q. Also, considering the size of the firms, market capitalization (MCAP) has insignificant and negative impact on ROA and ROE, while the effect is positive and significant on Tobin’s Q. The debt-to-equity ratio (DER) is discovered to have a significant impact on all the measures of performance, with only a positive effect on Tobin’s Q. Considering country-specific factors, the inflation rate positively influences the direction of all the financial metrics, though with insignificant impact.

Table 10. Panel regression for bank using robust standard error with cluster option (moderation effect).

Variables	ROA		ROE		TOBIN Q
	FE	RE	FE	RE	FE	RE
ESG	−0.0287	0.215	−0.606	5.848 ***	0.00243	0.000201
	(0.156)	(0.290)	(1.037)	(1.250)	(0.00410)	(0.00601)
ESG_TRS	−0.00105	−0.0050746	−0.00718	−0.121071 ***	−3.44 × 10−5	2.50 × 10−6
	(0.00239)	(0.0046)	(0.0162)	(0.0025)	(6.52 × 10−5)	(0.0009)
MCAP	0.00172 ***	0.00174 ***	0.0301 ***	0.0234 ***	1.63 × 10−5 **	1.59 × 10−5 ***
	(0.000276)	(0.000243)	(0.00203)	(0.00294)	(5.20 × 10−6)	(5.26 × 10−6)
DER	0.000804	−0.000762	0.000620	−0.0290 **	−5.22 × 10−6	8.68 × 10−6
	(0.000936)	(0.00130)	(0.00904)	(0.0118)	(4.90 × 10−5)	(4.50 × 10−5)
INF	0.0471	0.0374	0.770	0.614	−0.00128	−0.00119
	(0.0599)	(0.0636)	(0.468)	(0.374)	(0.000703)	(0.000778)
Constant	1.780 ***	1.944 **	14.86 ***	18.48 ***	0.896 ***	0.895 ***
	(0.253)	(0.939)	(2.432)	(3.181)	(0.00297)	(0.0206)
Robust Hausman test
Sargan-Hansen Statistic	1.1 × 104 *** (0.000)		17.622 *** (0.000)		589.425 *** (0.000)
Testing for Cross-Sectional Dependence on the preferred model (i.e., Fixed Effect (FE))
Pesaran CD test	4.190 *** (0.000)		3.545 *** (0.000)		1.469 (0.142)

Note: Robust standard errors in parentheses, while *** p < 0.01 and ** p < 0.05 denote 1%, 5%, and 10% levels of significance, respectively. The robust Hausman test is a test of overidentifying restrictions (fixed vs. random effects) and the test is performed based on the Sargan–Hansen statistic. Source: Authors’ computation (2025).

reveals the panel regression result for banks using standard error estimation with the cluster option on the moderating role of ESG transparency on the relationship between ESG financial materiality and the performance of banks. The result of the robust Hausman test reveals that the null hypothesis that the random test is appropriate is rejected for the moderating role of ESG transparency on the relationship between ESG financial materiality and the performance of banks. Therefore, the fixed-effect result is retained.

However, the result of the cross-sectional dependence indicates that the residuals are dependent across banks for ROA (p-value, 0.000) and ROE (p-value, 0.000) but not the Tobin’s Q (p-value, 0.142) model.

Therefore, as a result of the cross-sectional dependence in the models for all the financial metrics except Tobin’s Q, the fixed-effect result cannot be retained for interpretation and discussion on the moderating role of ESG transparency on the relationship between ESG financial materiality and banks’ performance for ROA and ROE. The models for ROA and ROE are as such subjected to further estimation using the Driscoll–Kraay standard error estimator to account for the presence of cross-sectional dependence among the variables to ensure reliable results. Albeit, the fixed effect is retained for Tobin’s Q using the robust standard error with the cluster option.

The results in

Table 11. Panel regression results for banks using Driscoll–Kraay standard error (moderator effect).

Variables	ROA		ROE
	FE	RE	FE	RE
ESG	−0.0287	0.215	−0.606	5.848 ***
	(0.150)	(0.321)	(1.207)	(1.105)
ESG*TRS	−0.00105	−0.0051	−0.00718	−0.1211
	(0.00223)	(0.0053)	(0.0163)	(0.018) ***
MCAP	0.00172 ***	0.00174 ***	0.0301 ***	0.0234 ***
	(0.000302)	(0.000223)	(0.00425)	(0.00321)
DER	0.000804	−0.000762	0.000620	−0.0290 ***
	(0.000588)	(0.00156)	(0.00592)	(0.00536)
INF	0.0471	0.0374	0.770	0.614
	(0.0405)	(0.0444)	(0.472)	(0.442)
Constant	1.780 ***	1.944 **	14.86 ***	18.48 ***
	(0.172)	(0.734)	(1.833)	(1.977)
F-test	17.47		14.67
Prob > F	0.000214		0.000424
Wald-chi2		195.6		306
Prob > chi2		0.000		0.000
Hausman test (Chi2)	0.420 (0.9948)		189.41 (0.0000)

Note: The values in parentheses as related to the coefficients are standard errors, while they are probability values in the case of the Hausman test (*** p < 0.01, ** p < 0.05). Source: author’s computation (2025).

correct the presence of cross- sectional dependence presented for ROA and ROE by employing Driscoll-Kraay standard errors, which is more reliable and robust. The result of the Hausman test reveals that the models are statistically significant; as such, the null hypothesis that the random test is appropriate is rejected for the moderating role of ESG transparency on the relationship between ESG financial materiality and banks’ performance in this study. Therefore, the fixed effect is chosen for the interpretation of findings and discussion for ROA and ROE using the Driscoll–Kraay standard error estimator.

The Hausman test in Table 11 shows that the random-effect result is only appropriate for ROA for the purpose of discussion in this study, while the fixed-effect model is interpreted for ROE and Tobin’s Q (cluster option).

Table 12. Panel regression results for banks (moderator effect).

Variable	ROA	ROE	TOBIN Q
	Random Effect	Fixed Effect	Fixed Effect
ESG	0.215	−0.606	0.00243
	(0.290)	(1.037)	(0.00410)
ESG_TRS	−0.00507	−0.00718	−3.44 × 10−5
	(0.0046)	(0.0162)	(6.52 × 10−5)
MCAP	0.00174 ***	0.0301 ***	1.63 × 10−5 **
	(0.000243)	(0.00203)	(5.20 × 10−6)
DER	−0.000762	0.000620	−5.22 × 10−6
	(0.00130)	(0.00904)	(4.90 × 10−5)
INF	0.0374	0.770	−0.00128
	(0.0636)	(0.468)	(0.000703)
Constant	1.944 **	14.86 ***	0.896 ***
	(0.939)	(2.432)	(0.00297)
Diagnostic test
Hausman (Chi2)	0.420 (0.9948)	189.41 *** (0.000)	589.425 *** (0.000)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05 denote 1%, 5%, and 10% levels of significance, respectively. Source: Authors’ computation (2025).

further reports that with moderation, ESG financial materiality disclosure reported by banks has a positive but insignificant effect on ROA and Tobin’s Q, while the effect is negative and insignificant on ROE. Likewise, the moderation effect of ESG transparency (ESG*TRS) on ROA, ROE, and Tobin’s Q is also negatively insignificant. MCAP has a positive and significant effect on ROA, ROE, and Tobin’s Q. Also, DER as insignificant and negative effect on both ROA and Tobin’s Q but exerts an insignificant positive effect on ROE. The results further reveal that the inflation rate has insignificant impact on all the financial variables, but with a negative impact on only Tobin’s Q.

Table 13. Panel regression for manufacturing firms using robust standard error with cluster option (moderation effect).

Variables	ROA		ROE		TOBIN Q
	FE	RE	FE	RE	FE	RE
ESG	10.440	9.0730 **	21.710	20.790 *	−0.0487	−0.0206
	(7.4160)	(3.7980)	(17.040)	(12.580)	(0.0307)	(0.0212)
ESG_TRS	−0.2190	−0.1763 **	−0.4730	−0.4145	0.0007	0.0002
	(0.1570)	(0.0797)	(0.3430)	(0.2320)	(0.0004)	(0.0002)
MCAP	−0.0094	−0.0203 ***	−0.0166	−0.0424 **	0.0001 ***	0.0001 ***
	(0.0124)	(0.0076)	(0.0300)	(0.0198)	(2.14 × 10−5)	(3.07 × 10−5)
DER	−0.0503 **	−0.0672 ***	−0.2280***	−0.2240 ***	0.0006 *	0.0008 ***
	(0.0129)	(0.0083)	(0.0314)	(0.0646)	(0.0003)	(0.0003)
INF	3.9630	3.2300	9.0970	7.8650	0.00233	0.0044
	(3.0630)	(2.5870)	(7.5220)	(6.1090)	(0.0042)	(0.0044)
Constant	−4.5900	−2.1280	−1.9020	−2.2070	0.5060 ***	0.4780 ***
	(12.380)	(9.9220)	(32.220)	(27.550)	(0.0436)	(0.0522)
Robust Hausman test
Sargan-Hansen Statistic	71.919 *** (0.000)		10.311 ** (0.014)		112.472 *** (0.000)
Testing for Cross-Sectional Dependence on the preferred model (i.e., Fixed Effect (FE))
Pesaran CD test	−0.277 (0.781)		−0.456 (0.648)		3.195 (0.140)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. The robust Hausman test is a test of overidentifying restrictions (fixed vs. random effects) and the test is performed based on the Sargan–Hansen statistic. Source: Authors’ computation (2025).

includes the result of the Hausman test, which depicts that all the models are statistically significant; as such, the null hypothesis that the random test is appropriate is rejected for the moderating effect of ESG transparency on the relationship between ESG financial materiality and manufacturing firms’ performance in this study. In addition, the cross-sectional dependence result suggests that the residuals are independent across manufacturing firms for all the models, with ROA (p-value, 0.781), ROE (p = value 0.648), and Tobin’s Q (p = value, 0.140) indicating the absence of cross-sectional dependence across the firms.

Therefore, since there is an absence of cross-sectional dependency across the firms in the three models, the fixed effect is retained for the interpretation of findings and discussion (See

Table 14. Panel regression results for manufacturing firms (moderator effect).

Variable	ROA	ROE	TOBIN Q
	Fixed Effect	Fixed Effect	Fixed Effect
ESG	10.440	21.710	−0.0487
	(7.4160)	(17.040)	(0.0307)
ESG_TRS	−0.2190	−0.4730	0.0007
	(0.1570)	(0.3430)	(0.0004)
MCAP	−0.0094	−0.0166	0.0001 ***
	(0.0124)	(0.0300)	(2.14 × 10−5)
DER	−0.0503 **	−0.2280 ***	0.0006 *
	(0.0129)	(0.0314)	(0.0003)
INF	3.9630	9.0970	0.00233
	(3.0630)	(7.5220)	(0.0042)
Constant	−4.5900	−1.9020	0.5060 ***
	(12.380)	(32.220)	(0.0436)
Diagnostic test
Hausman (Chi2)	71.919 *** (0.000)	10.311 ** (0.014)	112.472 *** (0.000)

Note: Robust standard errors in parentheses, while *** p < 0.01, ** p < 0.05, and * p < 0.1 denote 1%, 5%, and 10% levels of significance, respectively. Source: Authors’ computation (2025).

).

The result in Table 14 displays the moderating role of the ESG transparency of manufacturing firms on the relationship between ESG financial materiality and financial performance. The result shows that with the inclusion of a moderation factor in the model, the effect of ESG financial materiality on financial performance is positively insignificant on ROA and ROE (β =10.440, β =21.71) compared with the previous result without moderation (β = −2.567 β = −6.414), while Tobin’s Q remains negative but insignificant. However, the moderation effect of ESG transparency negatively and insignificantly influences the relationship between ESG financial materiality and financial performance (ROA and ROE) but is insignificantly positive on Tobin’s Q. Further investigation reveals that MCAP exerts negative and insignificant effect on ROA and ROE, while the effect is positively significant on Tobin’s Q. DER is observed to have significant negative impact on ROA and ROE but positively significant on Tobin’s Q. However, inflation rate exerts positive and insignificant effect on all the measures of financial metrics.

4.1. Robustness Check

We conducted a robustness check on the interplay between ESG transparency, ESG financial materiality, and financial performance to account for the problem of endogeneity in the models specified for this study by employing two-stage least squares with instrumental variables (see Appendix A,

Table A1. Two-stage least square result for banks (without moderator).

Instrumental Variables	ESG(-1) ESG(-2) ESG(-3) ESG(-4) MCAP(-1) MCAP(-2) MCAP(-3)		ESG(-1) ESG(-2) MCAP(-1) MCAP(-2) MCAP(-3) MCAP(-4) INF(-1) INF(-2)
Variable	ROA		ROE		TOBIN-Q
	B	p-Value	B	p-Value
ESG	−0.1042	0.3430	−1.5376	0.0462	There is absence of the problem of endogeneity in this model.
MCAP	0.0009	0.8081	0.0340	0.0319
DER	0.0011	0.8878	0.0278	0.5869
INF	0.0445	0.757	1.091	0.1675
C	1.731	0.1218	11.435	0.0885
R-Square	0.9181		0.7161
F-test	30.647	0.0000	6.3953	0.00009

,

Table A2. Two-stage least squares for manufacturing firms (without moderator effect).

Instrumental Variables	DER(-1) DER(-2) DER(-3) INF(-1) INF(-2) INF(-3) INF(-4) INF(-5)		DER(-1) DER(-2) DER(-3) INF(-1) INF(-2) INF(-3)		DER(-1) DER(-2) DER(-3) DER(-4) DER(-5) DER(-6) DER(-7)
Variable	ROA		ROE		TOBIN-Q
	B	p-Value	B	p-Value	B	p-Value
ESG	−0.4561	0.7719	−5.822647	0.3003	0.0005	0.9026
MCAP	−0.0564	0.2161	−239.333	0.5527	−0.5446	0.0304
DER	−0.038	0.0239	−0.2022	0.0104	0.0006	0.0001
INF	0.7547	0.6148	6.420	0.2599	0.0028	0.5480
C	4.0811	0.4589	17.8113	0.6140	0.5453	0.0000
R-Square	0.3891	0.38	0.3179		0.864
F-test	2.87	0.043	2.3830	0.087	3.042	0.056

,

Table A3. Two-stage least squares for banks (moderator effect).

Instrumental Variables	MCAP(-1) MCAP(-2) MCAP(-3) MCAP(-4) MCAP(-5) MCAP(-6) MCAP(-7)		MCAP(-1) MCAP(-2) MCAP(-3) MCAP(-4) MCAP(-5) INF(-1) INF(-2) INF(-3)
Variable	ROA		ROE		TOBIN-Q
	B	p-Value	B	p-Value
ESG	0.0952	0.5508	−7.684	0.2621	There is absence of the problem of endogeneity in this model.
ESG*TRS	−0.0014	0.5324	0.0915	0.4313
MCAP	2.930	0.0388	0.0330	0.0063
DER	0.0018	0.1852	0.0278	0.6017
INF	0.0036	0.8869	1.8739	0.0265
C	1.3285	0.0058		1.476
R-Square	0.9992			0.7446
F-test	700.49	0.0000	7.098	0.0000

and

Table A4. Two-stage least squares for manufacturing firms (moderator effect).

Instrumental Variables	ESG*TRS(-1) ESG*TRS(-2) ESG*TRS(-3) DER (-1) DER(-2) DER(-3) INF(-1) INF(-2) INF(-3)		ESG*TRS(-1) ESG*TRS(-2) ESG*TRS(-3) DER(-1) DER(-2) DER(-3) INF(-1) INF(-2) INF(-3)		ESG*TRS(-1) ESG*TRS(-2) ESG*TRS(-3) DER(-1) DER(-2) DER(-3) INF(-1) INF(-2) INF(-3)
Variable	ROA		ROE		TOBIN-Q
	B	p-Value	B	p-Value	B	p-Value
ESG	1.7795	0.8506	−24.15875	0.3911	−0.1135	0.2366
ESG*TRS	−0.0646	0.4811	0.5361	0.4452	0.0011	0.2173
MCAP	−0.0539	0.0726	−0.2784	0.4067	0.0018	0.4395
DER	−0.0188	0.7192	−0.2784	0.1778	0.0011	0.0120
INF	0.3615	0.8106	−7.3970	0.6584	0.0517	0.3554
C	9.1717	0.5796	49.7323	0.3544	0.3394	0.0014
R-Square	0.2321		0.2095		0.2045
F-test	2.19	0.100	2.448	0.0274	8.417	0.0000

).

4.2. Discussion of Findings

In an attempt to fill the gaps, we identified, this study discovered that ESG financial materiality disclosures have a significant and negative influence on South African banks’ return on equity (ROE) and return on assets (ROA), while the effect on Tobin’s Q is positive but statistically negligible. This implies that although ESG disclosures would not lead to an immediate improvement in accounting-based performance metrics, the market may view them favorably, albeit not to a statistically significant degree. These findings are consistent with the works of Kumaria Puri (2022), which established a negative correlation between sustainability reporting and South African banks’ financial performance, and Jha and Rangarajan (2020), who noted that ESG had a detrimental influence on ROA and a negligible effect on Tobin’s Q. This may be attributable to initial higher operating costs or resource allocation for sustainability activities, which may cause the costs of ESG projects to exceed the financial advantages. As such, the hypothesis that the impact of ESG financial materiality disclosures is positively significant on banks’ financial performance (ROA, ROE, and Tobin’s Q) in South Africa is rejected in this study.

Regarding the positive and significant impact of market capitalization on returns on assets, return on equity, and Tobin’s Q with a positive but insignificant inflation effect, it suggests that larger banks and those functioning in inflationary environments may benefit financially. This might be the result of improved pricing power, economies of scale, or more successful inflation hedging techniques. However, the effect of the inflation rate on Tobin’s Q is significantly negative, indicating that a rise in inflation would reduce the market valuation of banks, which may discourage existing and potential investors (Alyousef et al., 2019). Additionally, the debt-to-equity ratio only seems to have a favorable and substantial impact on ROA and has a negligible effect on other financial performance metrics, suggesting that leverage is not a major factor in determining banks’ financial results in this situation.

For South African manufacturing firms, ESG financial materiality disclosures have a and insignificant adverse impact on return on assets (ROA), return on equity (ROE), and Tobin’s Q. Hence, we reject the null hypothesis that ESG financial materiality has significant positive effect on financial performance (ROA, ROE, and Tobin’s Q) in manufacturing firms. The outcome of the test implies that ESG activities relating to financial materiality substantially reduce manufacturing companies’ financial performance in the near future. This suggests that ESG implementation may involve incurring some costs, such as compliance and restructuring costs, that are more than the short-term financial returns, particularly in manufacturing firms, which are capital-intensive and cost-sensitive. This aligns with the discovery of Alduais (2023) that ESG may not significantly improve the returns generated by manufacturing firms from the utilization of the shareholders’ fund and total assets, especially at the initial stage of ESG implementation. Market capitalization (MCAP), a measure of firm size, has an insignificant and negative effect on all the financial performance metrics, except with a substantial and favorable effect only on Tobin’s Q. This suggests that, in the manufacturing industry, a greater firm size does not always translate into better financial success. This could be because managing larger operations can be complicated or inefficient. However, it also indicates that manufacturing firms attract more investors and improve market valuation when expansion and growth are perceived. The debt-to-equity ratio (DER), which has a favorable influence on Tobin’s Q and a substantial adverse impact on ROA and ROE, indicates that while greater leverage may raise market valuation, it does not always translate into increased profitability, maybe due to high interest payment obligations (Danevska et al., 2023). All the financial indicators are favorably impacted by the inflation rate, though with an insignificant impact, suggesting that moderate inflationary pressures may boost asset returns of manufacturing firms in South Africa.

Regarding the moderating role of ESG transparency in banks, the findings show that ESG financial materiality has a negative and significant impact on banks’ ROA and ROE when it is not moderated, but it is insignificant and positive (ROA) with moderation, though negative on ROE. This suggests that ESG initiatives may insignificantly result in short-term financial burdens from operational and compliance costs when transparency is embraced. This further suggests that transparent disclosure helps allay investor and stakeholder concerns, but not enough to significantly improve performance. Also, ESG is still positively insignificant on Tobin’s Q, indicating that the market value of South African banks is not yet highly responsive to ESG initiatives unless they are regarded as significant and reliable (Christensen et al., 2021; Khan et al., 2016). More so, the intervening effect of ESG transparency on ESG financial materiality insignificantly reduces the financial performance of banks in this study. This suggests that the level of transparency in ESG issues addressing financial materiality is perceived not to be substantive and reliable but rather symbolic by stakeholders (Yu et al., 2020).

As such, the hypothesis that transparency significantly moderate the effect of ESG financial materiality on financial performance (ROA, ROE, and Tobin’s Q) of South African banks is rejected. Market capitalization (MCAP) has a positive and significant impact on all the financial metrics, suggesting that larger banks benefit from economies of scale and resilience. This is contrary to the findings of Fatemi et al. (2018) that firm size alone does not influence market perception. Leverage may increase short-term profitability, but it also raises questions about long-term financial risk, as evidenced by the positive and negative impact of the debt-to-equity ratio (DER) on ROA and Tobin’s Q. Finally, because of the market’s cautious assessment of inflation-related risks, inflation has a negative impact on Tobin’s Q, but it has a favorable impact on ROA and ROE, maybe as a result of greater interest margins, though insignificant.

The result of the moderation effect for manufacturing firms shows that ESG financial materiality has a positive and insignificant impact on return on assets (ROA and ROE) when transparency is introduced. ESG policies by themselves had a negative impact in the past on ROE, indicating that unreliable ESG reporting was insufficient to improve financial results. Hence, ESG activities become value-enhancing once transparency is implemented. This aligns with the study by Khan et al. (2016), who discovered that when stakeholders can see and trust a company’s sustainability efforts, meaningful ESG disclosures will result in better financial performance. Manufacturing companies can enhance their reputation and boost profitability by effectively communicating their ESG practices, as indicated by the large positive coefficient for ESG. Nonetheless, the insignificantly negative moderation effect of ESG transparency on ROA and ROE suggests that over-disclosure may backfire by reducing the efficiency of ESG financial materiality on performance. This is similar to findings of Christensen et al. (2021), which showed that although ESG policies can increase a company’s value, excessive or subpar disclosure may cause investors to become concerned about operational risks, expenses, or greenwashing, which could have a negative impact on financial results. Apart from the issue of greenwashing and over-disclosure, increasing the transparency of ESG issues may require more compliance costs or even expose the inefficiencies of the firm, which may result in a decline in financial outcomes when perceived by stakeholders (Hummel & Schlick, 2016). Also, ESG financial materiality has a negative and statistically negligible effect on Tobin’s Q, both with and without the moderating effect of transparency. This suggests that market valuation is not significantly impacted by ESG disclosures alone. The positive but negligible intervening effect of transparency suggests that the detrimental effects of ESG are only marginally mitigated. This corroborates the findings of Khan et al. (2016), who contend that investor perception is only substantially influenced by significant, reliable, and consistent ESG information. In addition, it may suggest that even when financial performance is uncertain, ESG disclosure is valued in the capital market. Therefore, the hypothesis that the moderating effect of ESG transparency on ESG financial materiality disclosures is significant on the financial performance of manufacturing firms in South Africa is rejected in this study.

High leverage reduces shareholder value and returns on asset utilization, particularly in ESG-sensitive contexts, as the debt-to-equity ratio has a significant negative association with ROA and ROE. Also, the debt-to-equity ratio significantly and favorably affects Tobin’s Q, suggesting that investors value moderate leverage (Ahmad et al., 2023). Market capitalization exerts an insignificant negative effect on ROA and ROE, respectively, but has a positive and significant impact on Tobin’s Q. This indicates that manufacturing firms with larger size are viewed in the market space as being more efficient and resilient, which in turn boost the confidence of investors and financial performance. It also suggests that market valuation is greatly impacted by market capitalization, indicating that ESG disclosures with increased openness can influence valuation for manufacturing enterprises when they are backed by solid financial underpinnings. Inflation has an insignificant beneficial impact on all the financial metrics, suggesting that financial returns maybe well protected during inflationary times.

All things considered, the results highlight that although ESG measures can boost financial success, transparency needs to be carefully controlled to prevent lower perceived value (Fatemi et al., 2018).

5. Conclusions, Practical Implication of Findings, Limitations, and Suggestion for Future Studies

5.1. Conclusions

In developing nations like South Africa, where ESG frameworks are still developing and market incentives for openness may not yet be completely in line with performance objectives, there is a need for further investigation on the effect of ESG financial materiality disclosure on the financial performance of banks and manufacturing firms, as they are more susceptible to environmental, social, and governance issues. More so, little or no attention has been given to the intervening role of ESG transparency on the relationship between ESG financial materiality disclosure and the financial performance of banks and manufacturing firms, especially in South Africa. Hence, this study contributes to existing knowledge by investigating the relationship in the South African context.

The findings of this study conclude that ESG material issues surrounding the financial outcomes of banks have an adverse and substantial impact on their shareholder’s wealth and returns generated from the utilization of banks’ total assets. However, the market perception of the value of these banks is favorable, though very weak. The effect on the financial performance of South African manufacturing firms is also a huge concern, as ESG financial materiality reduces the returns on total assets, shareholders’ wealth, and market-based performance, though their financial performance is not substantially impacted.

On the intervening role of ESG transparency, this study concludes that the quality of ESG reporting in banks and manufacturing firms in South Africa is questionable, as it significantly weakens the favorable impact of ESG financial materiality on measures of financial performance (ROA and ROE). However, the adverse impact of ESG transparency does not mean is not important, but it is a signal that ESG reporting by these firms may be more greenwashing, hence reducing the credibility of the reports and their impact on firms’ performance. Apart from the issue of greenwashing and over-disclosure, increasing the transparency of ESG issues may require more compliance costs or even expose the inefficiencies of the firm, which may result in a decline in financial outcomes when perceived by stakeholders. This suggests that banks and manufacturing firms in South Africa should not majorly view ESG transparency as a compliance tool but as a tool for transformation.

5.2. Practical Implication of Findings

The adverse effect and significant effect of ESG financial materiality disclosure on the return on assets and equity of banks implies that banks may incur some sustainability costs that may exceed their immediate financial outcome at the initial stages of implementation, though the market perception of banks may be enhanced insignificantly. This suggests that South African banks should embark on sustainability practices that will align with their major business strategies and improve the quality, clarity, and transparency of their ESG disclosure (considering the positive but insignificant effect on Tobin’s Q). For manufacturing firms, the negative and insignificant effect of ESG financial materiality disclosure on ROA, ROE, and Tobin’s Q suggests that the ESG activities of a firm may be resource-consuming and not aligned with the major goals of the firm. Hence, managers of manufacturing firms in South Africa should ensure regular examination of the impact of sustainability practices on their financial performance and focus on ESG material issues that are pertinent to the manufacturing sector and cost-effective.

The initial improvement effect on the performance of manufacturing firms when transparency was introduced, though insignificant, implies the need for credibility and quality in ESG reporting by firms. However, the moderating role of transparency weakens this positive impact of ESG financial materiality disclosure, which may be a result of over-disclosure or poorly structured overall ESG disclosure of the firm. Also, the cost of compliance to ensure transparency and misalignment of ESG practices with the major objectives of the firm may be detrimental to the financial outcomes of the firm. This suggest that manufacturing firms should strategically disclose ESG issues that are consistent and highly credible in order to build the confidence of investors. Also, ESG activities should not be seen as a standalone goal but should be integrated into the overall goals of manufacturing firms.

Considering the impact of these findings in the South African context, it can be inferred that in South Africa, it appears that investors prefer short-term gains to long-term ESG benefits, which has resulted in the insensitivity of firms to ESG transparency. More so, the economic issues of unemployment, inflation rate volatility, and load shedding exert more financial stress on firms, denying them gains from ESG practices. These further explain why ESG transparency has an adverse effect on the relationship between ESG materiality and the financial performance of banks and manufacturing firms in South Africa. Hence, the outcome of this study may not be applicable to ESG-forward and stabilized economies.

Therefore, inasmuch as transparency is paramount for financial growth, excessive disclosure can also increase ESG costs and risk that could deter profitability and growth. Managers in both banks and manufacturing companies in South Africa should endeavor to strike the balance between the identification of quality ESG financial materiality issues and the satisfaction of stakeholders. Also, the debt levels of firms should be well managed to avoid been highly leveraged, especially in manufacturing firms.

5.3. Limitations of the Study and Suggestion for Future Research

This study is majorly limited by the sample size of six banks and six manufacturing firms as a result of the inclusion criteria. This may limit the level of generalization of the findings of this study, though relevant. Future studies may increase the sample size for more generalization by including other non-bank financial institutions and other firms with holdings in manufacturing firms in South Africa. More so, this study only focused on investigating the moderating role of ESG transparency in overall ESG financial materiality without considering disaggregated ESG financial materiality disclosure scores. This constrains this study from identifying the contributions of each factor on financial performance when transparency is introduced. Potential studies could improve this study by making it a matter of discourse in order to determine the ESG issue that lacks transparency for financial performance.