Herding Behavior, ESG Disclosure, and Financial Performance: Rethinking Sustainability Reporting to Address Climate-Related Risks in ASEAN Firms

Abstract

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value.

1. Introduction

In the face of climate change and growing environmental challenges, corporate sustainability and responsible financial decision-making have become central to business strategy. Sustainability reporting, especially under standards like Global Reporting Initiative (GRI)-G4 (GRI, 2013), provides a structured way for firms to communicate their environmental, social, and governance (ESG) efforts (Bocken et al., 2015; Lee & Raschke, 2020). While ESG disclosure reflects a broader commitment to transparency, sustainability reporting serves as a key operational tool. Across global markets, ESG disclosures and sustainability reporting are increasingly used not just for compliance, but as strategic tools to manage climate risk, strengthen resilience, and build long-term stakeholder value (Greenwood & Warren, 2022; K. Wang et al., 2023; N. Wang et al., 2024).

Over the past three decades, sustainability reporting has shifted from a voluntary initiative to a formalized requirement in many jurisdictions, driven by regulatory reforms, investor expectations, and social pressures (Alsahali & Malagueño, 2022; Wakibi et al., 2024). This transition is especially evident in ASEAN economies, such as Indonesia, Malaysia, Thailand, the Philippines, and Singapore, where governments and capital markets increasingly demand transparent reporting aligned with ESG principles (Handoyo & Anas, 2024; Rudyanto & Siregar, 2018).

Firms disclose sustainability-related information to demonstrate accountability, enhance reputational capital, and attract long-term investors (Negera et al., 2025). Empirical studies have shown that comprehensive reporting can positively influence firm performance—particularly in indicators like Return on Assets (ROA) and Tobin’s Q—by improving transparency and strategic positioning (Bansal et al., 2021; Dincer et al., 2023; Xie et al., 2020). However, the relationship between sustainability reporting and profitability metrics such as Net Profit Margin (NPM) remains inconsistent (Handoyo & Anas, 2024; Yilmaz, 2021), suggesting a need for deeper analysis into the financial implications of sustainability practices.

Behavioral dynamics such as herding behavior further complicate this relationship. Firms often mimic the financial strategies and disclosure practices of industry peers, especially under regulatory uncertainty or market pressure (Bikhchandani & Sharma, 2000; Brendea & Pop, 2019). While this imitation may expedite the adoption of sustainability initiatives, it also risks fostering symbolic compliance rather than substantive change (Gavrilakis & Floros, 2023; Q. Wang, 2023). Industry leaders tend to shape disclosure norms and benefit disproportionately from reputational and financial gains, whereas follower firms may merely replicate practices without achieving a similar impact (Liu et al., 2023; Misani, 2010; Saeed et al., 2024).

Despite increasing attention to ESG and sustainability reporting practices, few studies explore how herding behavior influences sustainability reporting and capital structure decisions, particularly within non-financial firms in emerging markets like those in ASEAN. Existing research often centers on herding in investor behavior or stock trading (Ahmad & Wu, 2022; Vieito et al., 2024), leaving a gap in understanding how such behavior operates at the firm level and affects long-term strategic outcomes (Chiang & Zheng, 2010).

Within ASEAN, diverse regulatory capacities further amplify this complexity. While some member states have adopted advanced ESG disclosure frameworks, including formal sustainability reporting standards, others face persistent governance challenges (Ang, 2024; Ramadhani, 2019). These disparities contribute to uneven adoption and may reinforce herding behavior as firms navigate unclear or evolving reporting expectations.

This study addresses these gaps by investigating the relationship of herding behavior on capital structure and sustainability reporting in non-financial firms across ASEAN. It also examines how firm characteristics—specifically size and age—relate to disclosure practices, and evaluates how sustainability reporting relates to financial performance using ROA, NPM, and Tobin’s Q. Finally, the study explores whether herding behavior moderates the relationship between sustainability reporting and firm performance, distinguishing between industry leaders and followers. Therefore, the central research question guiding this study is: “How does herding behavior in capital structure decisions influence the relationship between sustainability reporting and financial performance (ROA, NPM, and Tobin’s Q) among non-financial firms in ASEAN, considering the distinction between industry leaders and followers?”

To capture these complex relationships, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA), which allow for nuanced analysis of both direct and moderating effects. This research offers significant contributions to both theory and practice. Theoretically, it advances the literature on behavioral finance and corporate sustainability by integrating herding behavior as a moderating factor in the link between sustainability reporting and corporate financial performance, and deepens the understanding of mimetic isomorphism and information asymmetry in corporate disclosure strategies. Practically, the findings are expected to provide actionable insights for regulators to design more effective ESG disclosure frameworks, for corporate strategists to integrate sustainability into financially disciplined strategies, and for investors to better assess the authenticity of climate action in emerging markets.

The remainder of this paper is organized as follows. Section 2 reviews the relevant theoretical foundations and empirical literature, developing the research hypotheses. Section 3 describes the research methodology, including the data collection, variable measurement, and the application of PLS-SEM and Multigroup Analysis. Section 4 presents the empirical results. Section 5 provides a detailed discussion of the findings. Finally, Section 6 concludes the study, outlining its implications, limitations, and suggestions for future research.

2. Literature Review, Theoretical Framework, and Hypothesis

2.1. Herding Behavior in Capital Structure Decisions

Herding behavior refers to the tendency of firms to imitate the financial decisions of their peers rather than rely on firm-specific analysis (Jirasakuldech & Emekter, 2021; Vieito et al., 2024). This phenomenon is well documented across financial settings, including investment management, market trading, and corporate finance (Ahmad & Wu, 2022; Cai et al., 2019). In capital markets, investors often disregard private signals to follow collective patterns, resulting in price inefficiencies and heightened volatility (Aharon, 2021). Similarly, in corporate finance, firms may align their capital structure decisions—such as debt-to-equity ratios—with industry norms due to competitive pressure, uncertainty, or institutional mimicry (Ezeoha, 2011; Youssef, 2022). Specifically, this imitation behavior aligns with the concept of mimetic isomorphism within institutional theory, where organizations mimic successful or legitimate practices of other organizations, especially in conditions of uncertainty, to reduce risk and enhance their legitimacy (DiMaggio & Powell, 1983; Meyer & Rowan, 1977).

Such imitation behavior tends to intensify during periods of market instability. Empirical studies confirm that herding increases in times of declining markets, financial stress, or high trading volume, especially in emerging economies where investor sentiment strongly influences managerial decisions (Economou et al., 2018; Ghorbel et al., 2023; Jirasakuldech & Emekter, 2021). Under these conditions, firms often replicate peers’ financing strategies as a form of risk aversion or strategic signaling, even when it diverges from their optimal capital needs (Chiang & Zheng, 2010; M. U. D. Shah et al., 2017).

In corporate finance, optimal capital structure decisions play a critical role in determining a company’s financial stability and long-term value. While firms should ideally optimize their mix of debt and equity to minimize capital costs (Fandella et al., 2023; Pais, 2017), herding behavior may lead them to replicate industry norms rather than tailoring financial strategies to their unique conditions. This imitation can result in inefficient capital structures, reducing flexibility and increasing vulnerability to market fluctuations.

To assess the presence of herding behavior in capital structure management, this study adopts the Herding Manager Index developed by Bo et al. (2016), which assigns a score of one to firms that display herd-like tendencies in financing decisions and zero otherwise. Prior research by S. S. H. Shah et al. (2019) confirms that these patterns are especially prevalent during periods of market uncertainty, when firms tend to follow the financing decisions of perceived industry leaders. However, limited research has examined how such behavior interacts with sustainability reporting practices—a gap this study seeks to address in the context of ASEAN economies.

2.2. Company Characteristics and Sustainability Reporting

Sustainability reporting functions as a key mechanism through which firms operationalize and disclose their ESG activities. By communicating their economic, environmental, and social performance, companies enhance transparency and accountability (Gallo & Christensen, 2011; Godha & Jain, 2015; Herbert & Graham, 2022). Guided by frameworks such as the Global Reporting Initiative (GRI), sustainability reporting enables organizations to detail their impact on sustainability-related issues in a structured manner, serving as a practical implementation of ESG disclosure (Githaiga & Kosgei, 2023; GRI, 2021). From an institutional theory perspective, while sustainability reporting can be voluntary, its increasing formalization is driven by coercive pressures (e.g., regulations), normative pressures (e.g., industry best practices), and mimetic pressures (e.g., imitating successful peers) from various stakeholders (DiMaggio & Powell, 1983; Higgins & Larrinaga, 2014). This reporting practice is increasingly shaped by stakeholder expectations and regulatory developments across jurisdictions (Benameur et al., 2024; Farisyi et al., 2022).

Among the key drivers of sustainability reporting are company-specific characteristics, particularly size and age. These characteristics influence a firm’s capacity, visibility, and motivation to disclose non-financial information (Ali & Abdelfettah, 2019; L. Wang, 2023). Larger firms, due to their public exposure and stakeholder pressure, are more likely to report comprehensively on sustainability matters (Bhatia & Tuli, 2017; Desai, 2022). They also have more financial and human resources to support sustainability initiatives and reporting infrastructure (Jamil et al., 2021; Orazalin & Mahmood, 2020). While most studies indicate a positive relationship between firm size and the extent of sustainability reporting (Al-Qudah & Houcine, 2024; Gallo & Christensen, 2011), others suggest a nonlinear pattern, where medium-sized firms are the most active reporters (Agarwala et al., 2024; Haladu & Bin-Nashwan, 2022).

Company age is also positively associated with sustainability disclosure. Older firms often have more experience with reporting standards and have developed reputational capital that they aim to protect through consistent disclosures (Bhatia & Tuli, 2017; Orazalin & Mahmood, 2020). These firms are typically more institutionalized, with formalized reporting structures and long-term stakeholder engagement strategies (Herbert & Graham, 2022; Kumar et al., 2023). Studies indicate that firm age contributes to the quality and comprehensiveness of sustainability reports (Correa-Garcia et al., 2020; Prashar, 2023).

2.3. Sustainability Reporting and Corporate Financial Performance

Sustainability reporting has emerged as a key mechanism for reducing information asymmetry, allowing investors to better evaluate corporate risks, long-term cash flows, and overall credibility (Al Natour et al., 2022; Upaa & Iorlaha, 2023). From a signaling theory perspective, comprehensive sustainability disclosures act as credible signals to the market, indicating a firm’s commitment to good governance, reduced environmental and social risks, and long-term value creation (Friske et al., 2023; Spence, 1973). This can attract more favorable investor attention, reduce perceived risk, and potentially lower the cost of capital. A growing body of empirical research supports the notion that structured sustainability disclosures contribute to improved profitability and operational performance (Al Hawaj & Buallay, 2022; Benameur et al., 2024; Mihai & Aleca, 2023).

To fully understand the value of sustainability reporting, it is essential to examine its impact on corporate financial performance. As companies increasingly embed sustainability into their core strategies, stakeholders—particularly investors—demand evidence that such disclosures lead to measurable financial benefits (Christensen et al., 2021; Fisch, 2018). In terms of profitability, companies that provide transparent sustainability reports often record higher net profit margins, supported by operational efficiencies and strengthened stakeholder relationships (Buallay et al., 2021). Reports aligned with international standards such as the GRI reinforce corporate legitimacy, improve investor trust, and ultimately contribute to both revenue growth and cost savings (Mihai & Aleca, 2023).

Sustainability practices are also positively associated with return on assets. Firms that consistently disclose their environmental and social impacts tend to manage their resources more effectively, resulting in stronger asset utilization and financial outcomes (Alodat et al., 2024; Gavrilakis & Floros, 2023). Evidence from both developed and emerging markets—including smart cities and sustainability-oriented sectors—demonstrates the robustness of this relationship (Buallay et al., 2021; Chung et al., 2024).

Beyond profitability and efficiency, sustainability reporting also plays a critical role in shaping firm value, particularly as measured by Tobin’s Q. Transparent and strategic disclosures enhance investor confidence, signaling long-term orientation and lower investment risk (Swarnapali, 2020; Younis, 2023). Companies that maintain credible and consistent sustainability reporting practices tend to attract long-term investors and benefit from favorable market valuations (Kim & Kim, 2018). This aligns with agency theory, as robust sustainability reporting can mitigate agency problems by increasing transparency and accountability of management to shareholders, thereby reducing information asymmetry and potentially leading to higher firm valuation (Jensen & Meckling, 1976).

2.4. Herding Behavior’s Moderating Role in Sustainability Reporting and Corporate Financial Performance

Herding behavior refers to the tendency of companies to imitate the decisions of other firms in their industry rather than making independent strategic choices. This behavior often emerges in environments characterized by uncertainty and information asymmetry, where following industry norms is perceived as safer and more acceptable to stakeholders (Camara, 2017). Companies may engage in herding to reduce the risk of poor decision-making, gain legitimacy, or signal stability and reliability to investors (Komalasari et al., 2022). While firms are ideally expected to optimize their capital structure based on internal financial conditions, herding behavior can lead them to adopt financing decisions that mimic peer companies instead of reflecting their unique needs (Brendea & Pop, 2019). However, in some contexts, such imitation can create value. For example, He and Wang (2020) find that in China, firms that followed industry leaders in financial decisions reported improved long-term performance after regulatory reforms.

Herding is also common in the context of sustainability reporting. Firms frequently align their environmental, social, and governance (ESG) disclosure practices with those of their peers to maintain competitiveness, respond to stakeholder expectations, and meet evolving regulatory requirements (Saeed et al., 2024). This pattern is particularly evident in ASEAN countries, where companies increasingly adopt similar sustainability strategies to strengthen their standing in ESG ratings and sustainability indices. Gavrilakis and Floros (2023) observe similar trends in European markets, where ESG investment behaviors often cluster around dominant industry norms. These findings suggest that sustainability reporting can serve not only as a strategic communication tool but also as a means for companies to conform to industry-wide expectations.

The financial effects of herding behavior remain debated. Some studies suggest that companies that resist herding outperform others by making more efficient and firm-specific investment decisions (Jiang & Verardo, 2018). In contrast, other research shows that moderate herding, especially when firms follow credible industry leaders, can enhance financial outcomes such as return on assets (ROA) and market valuation (S. S. H. Shah et al., 2024). However, excessive reliance on imitation may lead to inefficient allocation of resources and ultimately weaken performance if peer strategies are not aligned with a firm’s specific context.

Given this complex dynamic, it is essential to examine whether herding behavior moderates the relationship between sustainability reporting and corporate financial performance. In this study, financial performance is measured using net profit margin (NPM), ROA, and Tobin’s Q. By exploring this moderating role, the research seeks to understand whether herding amplifies or diminishes the financial benefits of sustainability reporting among non-financial companies in ASEAN countries. This moderating effect is critical as it highlights the interplay between behavioral aspects (herding, as explored by behavioral finance and institutional theory) and information transparency (sustainability reporting, as explained by signaling theory) in influencing firm outcomes. Managerial decisions, influenced by peer imitation, can impact the efficiency of resource allocation and the effectiveness of disclosure in conveying genuine commitment and performance (Brunner & Ostermaier, 2019).

2.5. Theoretical Framework and Hypotheses

This study is primarily grounded in institutional theory, complemented by insights from behavioral finance, signaling theory, and agency theory, to provide a comprehensive understanding of the interplay between herding behavior, sustainability reporting, and corporate financial performance. Institutional theory, particularly its concept of mimetic isomorphism, explains why firms tend to imitate the strategies and practices of their peers, especially under conditions of uncertainty or when seeking legitimacy (DiMaggio & Powell, 1983; Meyer & Rowan, 1977). This perspective is crucial for understanding herding behavior in capital structure decisions and the adoption of sustainability reporting practices. Behavioral finance provides the foundational understanding of herding as a cognitive bias, where managers’ decisions deviate from pure rationality due to social influence (Shiller, 2003).

Furthermore, signaling theory illuminates how firms use sustainability reporting as a credible signal to reduce information asymmetry with stakeholders, thereby potentially improving financial performance (Connelly et al., 2010; Spence, 1973). Finally, agency theory helps to explain how transparency through sustainability reporting can mitigate conflicts of interest between managers and shareholders, leading to better resource allocation and firm valuation (Jensen & Meckling, 1976).

Building on this integrated theoretical framework and the prior literature, this study investigates the role of herding behavior and company characteristics in shaping sustainability reporting and its relationship with corporate financial performance. Herding behavior, viewed through the lens of mimetic isomorphism and behavioral biases, reflects a tendency among managers to align their decisions with those of peers rather than relying solely on firm-specific analysis. This tendency may particularly manifest in capital structure decisions and the adoption of disclosure norms, especially during periods of economic uncertainty or regulatory shifts. Additionally, company characteristics such as firm size and firm age are established drivers of sustainability reporting, influencing firms’ capacities and incentives to disclose non-financial information, often driven by institutional pressures. This study further explores how sustainability reporting affects corporate financial performance—measured through net profit margin (NPM), return on assets (ROA), and firm value (Tobin’s Q), consistent with signaling and agency theory—while assessing whether herding behavior moderates these relationships.

The proposed research model (Figure 1) integrates these elements, capturing direct and moderating effects to provide a comprehensive understanding of how behavioral and structural factors intersect in the context of corporate sustainability and financial performance. The following hypotheses are developed to guide the empirical investigation:

H1. 

Herding behavior exists in capital structure management among non-financial companies that publish sustainability reports in five ASEAN countries;

H2. 

company size has a positive relationship with sustainability reporting among non-financial companies in five ASEAN countries;

H3. 

company age has a positive relationship with sustainability reporting among non-financial companies in five ASEAN countries;

H4. 

sustainability reporting has a positive relationship with net profit margin (NPM) among non-financial companies in five ASEAN countries;

H5. 

sustainability reporting has a positive relationship with return on assets (ROA) among non-financial companies in five ASEAN countries;

H6. 

sustainability reporting has a positive relationship with Tobin’s Q among non-financial companies in five ASEAN countries;

H7. 

herding behavior moderates the relationship between sustainability reporting and company performance (NPM, ROA, Tobin’s Q) among non-financial companies in five ASEAN countries.

3. Materials and Methods

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and herding behavior in capital structure decisions among non-financial companies in five ASEAN countries: Indonesia, Malaysia, Thailand, the Philippines, and Singapore. A quantitative approach is employed, focusing on firms listed on the respective national stock exchanges that issued sustainability reports in line with the Global Reporting Initiative (GRI-G4) framework between 2018 and 2023.

To ensure analytical robustness, a purposive sampling strategy was applied with two criteria: (1) the company must be non-financial and listed on one of the five ASEAN stock exchanges; and (2) it must have published both complete audited financial statements and sustainability reports adhering to the GRI-G4 standards. The period from 2018 to 2023 was chosen to capture recent trends in sustainability reporting and climate-related risk awareness in ASEAN. While the resulting sample size of 125 firms (as detailed in

Table 1. Sample distribution across industries.

Industry	Frequency	Percentage	Cumulative
Basic materials	44	35.2%	35.2%
Consumer goods	14	11.2%	46.4%
Consumer services	5	4.0%	50.4%
Industrials	48	38.4%	88.8%
Oil and gas	8	6.4%	95.2%
Healthcare	1	0.8%	96.0%
Real estate	1	0.8%	96.8%
Technology	1	0.8%	97.6%
Utilities	3	2.4%	100.0%
Total	125	100.0%

) might appear modest, it is precisely due to these stringent criteria necessary to ensure the quality, consistency, and comparability of both financial and GRI-G4-compliant sustainability data across five diverse emerging markets. Many firms in these regions do not consistently publish standardized sustainability reports, making this focused dataset representative of the high-quality data available for this specific research scope.

The data used in this study were obtained from the Bloomberg Terminal, which provides standardized and reliable company-level financial and sustainability information. Since Bloomberg is a proprietary platform, direct access to the raw data is restricted; however, detailed variable descriptions and measurement procedures are included in

Table 2. Summary of variable measurement.

Variable	Proxy/Indicator	Measurement	Source	Reference
Herding behavior	Managerial Herding Ratio (MHR)	Measured using the absolute deviation of the investment ratio model, where herding is indicated if company managers follow the investment decisions of their peers. The investment ratio is proxied by the Debt-to-Equity Ratio (DER). A lower deviation from the industry average indicates herding behavior.	Bloomberg Terminal	Bo et al. (2016); S. S. H. Shah et al. (2019)
Debt-to-Equity Ratio (DER)	Capital structure measure	Calculated as total liabilities divided by shareholders’ equity. Used as a proxy for investment ratio.	Bloomberg Terminal	Modigliani and Miller (1958); Fandella et al. (2023)
Company Size	Total Assets	Measured as the natural logarithm of total assets.	Bloomberg Terminal	Al-Qudah and Houcine (2024); Gallo and Christensen (2011)
Company Age	Years since establishment	Calculated as the difference between the observation year and the year of establishment.	Bloomberg Terminal	Correa-Garcia et al. (2020); Prashar (2023)
Sustainability Reporting	Sustainability Report Disclosure Index (SRDI)	Measured by assessing the extent of disclosures based on GRI-G4 guidelines. Each disclosed item scores 1; undisclosed items score 0. The SRDI score is calculated by dividing the total disclosed indicators by the maximum of 91 indicators, then converting the result into a percentage.	Bloomberg Terminal	Di Leo et al. (2023); Pais (2017)
Net Profit Margin (NPM)	Profitability indicator	Measured as net profit divided by total revenue, expressed as a percentage.	Bloomberg Terminal	Buallay et al. (2021)
Return on Assets (ROA)	Financial performance indicator	Calculated as net income divided by total assets, expressed as a percentage.	Bloomberg Terminal	Buallay et al. (2021)
Tobin’s Q	Market-based performance indicator	Measured as the ratio of the firm’s market value (equity and debt) to total assets.	Bloomberg Terminal	Gutiérrez-Ponce and Wibowo (2024); Hejazi et al. (2016)
Moderating Variable (Leader– Follower Classification)	MHR-based and SRDI-based classification	MHR-based: companies with DER closest to the industry average are classified as leaders (Dummy = 1), while those with significantly different DER values are classified as followers (Dummy = 0). SRDI-based: companies ranked in the top 50% of SRDI scores are classified as leaders (Dummy = 1); those in the bottom 50% are followers (Dummy = 0).	Bloomberg Terminal	Bo et al. (2016); Di Leo et al. (2023); Pais (2017)

to ensure replicability.

This study employs seven key variables, chosen for their theoretical relevance and empirical support in the existing literature (as detailed in Section 2): herding behavior, company size, company age, sustainability reporting, net profit margin (NPM), return on assets (ROA), Tobin’s Q, and a moderating variable for leader–follower classification. Herding behavior is assessed using the managerial herding ratio (MHR), developed by Bo et al. (2016), which quantifies a firm’s tendency to mimic industry average capital structure (proxied by Debt-to-Equity Ratio—DER). This index was specifically chosen for its direct applicability to managerial decision-making and its robustness in detecting mimetic behavior, aligning with our focus on firm-level strategic choices (S. S. H. Shah et al., 2019).

Company size and company age serve as standard control variables, accounting for inherent firm characteristics that influence disclosure and performance. Sustainability reporting is operationalized via the Sustainability Report Disclosure Index (SRDI), a comprehensive measure based on 91 GRI-G4 indicators, ensuring an internationally recognized standard for disclosure assessment. Corporate financial performance is robustly captured by a combination of accounting-based (NPM and ROA) and market-based (Tobin’s Q) metrics, providing a holistic view of profitability, asset efficiency, and market valuation. The moderating variable uses dual classifications (MHR-based and SRDI-based) to distinguish between leader and follower firms, allowing for a nuanced examination of herding’s influence on the sustainability–performance relationship.

To identify herding behavior, the study employs the Managerial Herding Ratio (MHR), following Bo et al. (2016) and S. S. H. Shah et al. (2019). In this approach, the investment ratio—proxied by the Debt-to-Equity Ratio (DER)—is compared against the industry average. Herding is indicated when a firm’s investment ratio closely mimics the industry norm. The following equation is used to calculate MHR:

$${\mathrm{M}\mathrm{H}\mathrm{R}}_{\mathrm{t}}=\left|\left|{\displaystyle \frac{I}{K_{i,t}}}\right|-\left|\overline{{\displaystyle \frac{I}{K_{-i,t-1}}}}\right|\right|$$

(1)

where (I/K)_i,t is the investment ratio of company i at time t, and $\left(\overline{\mathrm{I}/\mathrm{K}}\right)$_−i,t−1 represents the average investment ratio of industry peers at t − 1. A dummy variable of 1 is assigned if herding behavior is detected (i.e., deviation is minimal), and 0 otherwise.

To assess the moderating effect of herding, Multi-Group Analysis (MGA) is conducted using two classification approaches. In the MHR-based classification, companies with DER values closest to the industry average are labeled as leaders (dummy = 1), while those with significantly higher or lower DER values are classified as followers (dummy = 0). The SRDI-based classification, adapted from Di Leo et al. (2023) and Pais (2017), ranks companies by their SRDI scores each year. Firms in the top 50% (Q1 and Q2) are classified as leaders (dummy = 1), while those in the bottom 50% (Q3 and Q4) are labeled as followers (dummy = 0).

To test Hypothesis 1, the presence of herding behavior is analyzed using the MHR model. Hypotheses 2 through 7 are evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM), conducted with WarpPLS software (version 8.0). PLS-SEM is chosen for its suitability in exploring complex path relationships, including moderation effects, particularly when data are non-normally distributed or when working with sample sizes reflective of stringent data collection criteria (Hair et al., 2017; Sarstedt et al., 2017). Its predictive orientation and robustness to non-normal data are key advantages for this study’s objectives. Furthermore, Multi-Group Analysis (MGA), a feature within PLS-SEM, is specifically employed to rigorously test our moderating hypothesis (H7) by comparing relationships across distinct leader and follower groups (Hair et al., 2017). Generative artificial intelligence tools, specifically Grammarly, were used solely for language refinement and clarity enhancement. They were not involved in the design, data processing, statistical analysis, or interpretation of the study.

4. Results

4.1. Descriptive Statistics

The first step in the analysis involves conducting a descriptive statistical assessment to summarize the distribution of each research variable. As shown in

Table 3. Descriptive statistics.

Variable	Mean	Minimum	Maximum	Std. Deviation
Size (in billion)	29.0781	0.4339	43.7756	5.9707
Age (years)	50.2491	6.0000	195.0000	28.7374
Sustainability Report (SRDI, %)	51.6484	5.4945	91.2088	16.1538
NPM (%)	8.0836	−90.1960	636.2842	26.9092
ROA (%)	5.0962	−53.3193	132.5552	9.3536
Tobin’s Q	1.6523	−0.3806	7.4046	1.1236

, the results reveal substantial variation in company characteristics, sustainability reporting practices, and financial performance among the sampled firms. This variability reflects the inherent heterogeneity of the non-financial sector across ASEAN markets during the 2018–2023 period, influenced not only by general market characteristics but also significantly by the diverse country-specific contexts and the particular sectors of activity to which these companies belong.

The sample of 125 firms is drawn from five ASEAN countries, with a distribution as follows: Indonesia contributes 35 firms, Malaysia 18 firms, the Philippines 11 firms, Singapore 30 firms, and Thailand 31 firms. This distribution ensures representation from key emerging and developed ASEAN economies with varying regulatory environments for sustainability. Furthermore, as detailed in Table 1 (in Section 3), the sample is notably diversified across industries, with a heavy weighting towards Industrials (48 firms) and Basic Materials (44 firms), which are typically capital-intensive sectors. Differences in firm size, age, disclosure levels, and profitability measures, as presented in Table 3, further indicate the heterogeneity of the non-financial sector within this regional context.

4.2. Herding Behavior Analysis

To examine the existence of herding behavior, the Managerial Herding Index (MHR) was applied to five industries with sufficient firm representation (at least five firms per industry): Basic Materials, Consumer Goods, Consumer Services, Industrials, and Oil and Gas.

The MHR quantifies the presence of herding by measuring the absolute deviation of a firm’s Debt-to-Equity Ratio (DER) from its industry average. Herding is identified when this deviation is minimal, indicating that a firm’s capital structure decisions closely mimic the industry norm rather than being solely driven by firm-specific or broad economic conditions. This method allows us to isolate the mimetic behavioral component from general market influences and understand conformity in financing decisions (Bo et al., 2016; S. S. H. Shah et al., 2019). The MHR provides a robust measure for assessing herding as a distinct behavioral phenomenon, thereby validating its role in the model and allowing conclusions about the behavior in question.

As shown in

Table 4. Managers Herding (MHR) Index (2018–2023).

Years	Basic Materials (Total 44)		Consumer Goods (Total 14)		Consumer Services (Total 5)		Industrials (Total 48)		Oil and Gas (Total 8)
	1	0	1	0	1	0	1	0	1	0
2018	8	36	4	10	3	2	14	34	3	5
2019	11	33	5	9	2	3	14	34	2	6
2020	17	27	6	8	2	3	15	33	1	7
2021	15	29	4	10	2	3	12	36	2	6
2022	11	33	7	7	2	3	14	34	2	6
2023	11	33	6	8	3	2	10	38	1	7

Note: 1 = herding; 0 = non-herding.

, herding behavior varies both across sectors and over time. In the Basic Materials industry, herding activity peaked in 2020 with 17 firms, then declined steadily in the following years. The Consumer Goods sector exhibits a relatively stable pattern, with between 4 and 7 companies exhibiting herding behavior annually. The Consumer Services sector—comprising the fewest firms—shows the lowest herding intensity, with only 2 to 3 firms engaging in herding each year.

The Industrials sector demonstrates moderate yet consistent herding, with the highest participation observed in 2018 and 2019 (14 companies), followed by a slight decline in subsequent years. In contrast, the Oil and Gas industry displays limited herding, with only one to three firms per year conforming to peer behavior.

These findings provide empirical support for Hypothesis 1, which posits the existence of herding behavior in capital structure management among non-financial firms. The observed variations highlight the contextual nature of mimetic behavior. For instance, herding is more pronounced in capital-intensive and highly regulated sectors such as Basic Materials and Industrials. Conversely, Consumer Services and Oil and Gas demonstrate weaker tendencies toward herding. Moreover, the fluctuation of herding behavior across years suggests that broader economic and market conditions, while not the sole drivers, may influence the extent of managerial conformity in financing decisions.

4.3. Measurement Model Evaluation

This study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships between company characteristics, sustainability reporting, corporate financial performance, and the moderating role of herding behavior. Before assessing the structural model, the measurement model is first evaluated for validity and reliability.

Discriminant validity was assessed through latent variable correlation analysis. As shown in

Table 5. Latent variable correlation.

	1	2	3	4	5	6
1. Size	1.000
2. Age	0.051	1.000
3. Sustainability Report	−0.063	0.046	1.000
4. NPM	−0.026	−0.032	0.038	1.000
5. ROA	−0.063	−0.052	0.151 ***	0.242 ***	1.000
6. Tobin’s Q	−0.089 **	−0.045	0.271 ***	0.067	0.210 ***	1.000

Note: ** p < 0.05; *** p < 0.01.

, all inter-construct correlations fall below the 0.70 threshold, indicating adequate discriminant validity (MacKenzie et al., 2005). This confirms that the latent constructs are statistically distinct from one another and suitable for further analysis.

Subsequently, model fit indicators were examined to evaluate the structural model’s adequacy (

Table 6. PLS-SEM model fit and measurement indicators.

Indicator	Result	Criteria	Information
APC	0.164 (p = <0.01)	p < 0.05	Accepted
ARS	0.045 (p = 0.054)	p < 0.05	Not Accepted
AARS	0.043 (p = 0.058)	p < 0.05	Not Accepted
AVIF	1.021	3.3 ≤ AVIF ≤ 5	Accepted
AFVIF	1.071	3.3 ≤ AFVIF ≤ 5	Accepted
GOF	0.212	0.10 ≤ GOF ≤ 0.36	Small
SPR	1.000	SPR = 1 or ≥0.7	Accepted
RSCR	1.000	RSCR = 1 or ≥0.7	Accepted
SSR	1.000	≥0.7	Accepted
R-Squares	0.164 (<0.01)	<0.05	Accepted
Sustainability Report	0.057	0.25 ≤ Rs ≤ 0.70	Weak
NPM	0.002	0.25 ≤ Rs ≤ 0.70	Weak
ROA	0.031	0.25 ≤ Rs ≤ 0.70	Weak
Tobin’s Q	0.089	0.25 ≤ Rs ≤ 0.70	Weak
Adjusted R-Squares			Kock and Lynn (2012)
Sustainability Report	0.055	0.25 ≤ Rs ≤ 0.70	Weak
NPM	0.001	0.25 ≤ Rs ≤ 0.70	Weak
ROA	0.030	0.25 ≤ Rs ≤ 0.70	Weak
Tobin’s Q	0.088	0.25 ≤ Rs ≤ 0.70	Weak
Q2 Predictive			Stone (1974)
Sustainability Report	0.056	>0	Predictive Value
NPM	0.005	>0	Predictive Value
ROA	0.033	>0	Predictive Value
Tobin’s Q	0.091	>0	Predictive Value
Full Collinearity VIFs
Size	1.014	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
Age	1.011	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
Sustainability Report	1.097	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
NPM	1.063	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
ROA	1.121	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
Tobin’s Q	1.123	3.3 ≤ VIFs ≤ 5	Multicollinearity Free
Effect Size			Cohen (1988)
Size—Sustainability Report	0.047	≥0.02	Weak Effect
Age—Sustainability Report	0.010	≥0.02	Weak Effect
Sustainability Report—NPM	0.002	≥0.02	Weak Effect
Sustainability Report—ROA	0.031	≥0.02	Weak Effect
Sustainability Report—Tobin’s Q	0.089	≥0.02	Weak Effect

). The Average Path Coefficient (APC = 0.164, p < 0.01), Average Variance Inflation Factor (AVIF = 1.021), and Average Full Collinearity VIF (AFVIF = 1.071) all fall within acceptable thresholds, confirming acceptable model fit and the absence of multicollinearity issues. Similarly, other indices such as Simpson’s Paradox Ratio (SPR), R-Squared Contribution Ratio (RSCR), and Statistical Suppression Ratio (SSR) achieved ideal values (equal to 1.000), indicating a well-specified model.

However, both the Average R-squared (ARS = 0.045, p = 0.054) and Adjusted ARS (AARS = 0.043, p = 0.058) do not meet conventional significance thresholds, implying limited explanatory power. This observation is reinforced by relatively low R-squared values for the endogenous constructs—Sustainability Reporting, NPM, ROA, and Tobin’s Q—all of which fall below 0.10, suggesting that only a small proportion of variance in these variables is explained by the model.

Despite this, the Q² predictive relevance values for all constructs are greater than zero, indicating that the model retains predictive relevance (Stone, 1974). Furthermore, the Full Collinearity VIFs for all variables are below the critical value of 3.3, confirming no multicollinearity concerns (Kock & Lynn, 2012).

Finally, the calculated effect sizes (ƒ²) indicate weak relationships between the constructs. For instance, company size and sustainability reporting exhibit a weak effect (ƒ² = 0.047), as do the links between sustainability reporting and firm performance indicators, such as ROA (ƒ² = 0.031) and Tobin’s Q (ƒ² = 0.089). Although these values meet the threshold for statistical relevance (ƒ² > 0.02), the results suggest that the relationships are modest in strength (Cohen, 1988).

Based on these evaluations, the measurement and structural models are deemed acceptable for hypothesis testing, despite the weak explanatory power. The results suggest that while the conceptual model is valid and statistically sound, other unmeasured variables may play a more substantial role in determining the variance in sustainability reporting and financial performance.

4.4. Structural Model Results

The structural model analysis evaluates the direct relationships between company characteristics, sustainability reporting, and firm performance. The results are summarized in Figure 2 and

Table 7. Results of Hypothesis Testing in the Structural Model.

Hypothesis		Path Coeff.	p-Values	Decision
H2	Size → Sustainability Report	−0.210	<0.001	Not Supported
H3	Age → Sustainability Report	0.088	0.008	Supported
H4	Sustainability Report → NPM	0.049	0.090	Not Supported
H5	Sustainability Report → ROA	0.176	<0.001	Supported
H6	Sustainability Report → Tobin’s Q	0.299	<0.001	Supported

, providing insights into the significance and direction of each hypothesized path.

The analysis confirms support for three out of five direct effect hypotheses. Notably, Hypothesis 2 (H2) is statistically significant but yields a negative path coefficient (β = −0.210, p < 0.001), indicating that larger companies tend to disclose fewer sustainability-related indicators, which is contrary to expectations and the previous literature. This unexpected finding may reflect strategic opacity among large firms or variations in reporting motivations across sectors and jurisdictions.

Hypothesis 3 (H3) is supported, with a positive and significant path coefficient (β = 0.088, p = 0.008), suggesting that older companies are more likely to engage in sustainability reporting. This result aligns with the assumption that more established firms may have developed the organizational maturity and stakeholder pressures that drive transparent ESG disclosure practices.

In contrast, Hypothesis 4 (H4) is not supported, as sustainability reporting does not show a significant relationship with net profit margin (NPM) (β = 0.049, p = 0.090). This implies that while ESG disclosure may contribute to long-term strategic positioning, it does not yield immediate profitability gains.

However, Hypothesis 5 (H5) and Hypothesis 6 (H6) are both supported, revealing that sustainability reporting positively affects return on assets (ROA) (β = 0.176, p < 0.001) and Tobin’s Q (β = 0.299, p < 0.001). These results highlight the financial and market benefits of robust ESG disclosure practices, suggesting that transparent sustainability communication can enhance investor perception and operational efficiency.

4.5. Multigroup Analysis by Country

To further explore the heterogeneity of sustainability reporting outcomes across ASEAN countries, a multigroup analysis (MGA) was conducted. This analysis assessed whether the effect of sustainability reporting on corporate financial performance differs significantly between country pairs. As shown in

Table 8. Multigroup Analysis Results for Country-Level Differences in Sustainability Reporting Effects.

Pair	SR → NPM	SR → ROA	SR → Tobin’s Q
Malaysia–Indonesia	<0.001	0.050	0.024
Malaysia–Philippines	0.075	0.191	0.380
Malaysia–Singapore	0.247	0.059	<0.001
Malaysia–Thailand	0.002	0.111	<0.001
Indonesia–Philippines	0.146	0.227	0.047
Indonesia–Singapore	0.018	0.003	0.352
Indonesia–Thailand	<0.001	0.010	0.108
Philippines–Singapore	0.142	0.046	<0.001
Philippines–Thailand	<0.001	0.063	<0.001
Singapore–Thailand	0.441	0.188	0.122

, significant differences were observed in the impact of sustainability reporting on ROA and Tobin’s Q between several country pairs. The Philippines consistently showed the strongest effect, particularly in relation to ROA and Tobin’s Q, followed by Malaysia, Indonesia, Thailand, and Singapore. Fewer differences were found for NPM, though significant variations were identified in some pairwise comparisons. These results suggest that the financial and market responses to sustainability reporting are not uniform across countries.

4.6. Moderating Effect of Herding Behavior

This study also examines the moderating role of herding behavior in the relationship between sustainability reporting and company performance through multigroup analysis (MGA). As shown in

Table 9. Multigroup Analysis (MGA) results for herding behavior moderation.

Path	Model I			Model II
	Leader	Follower	Difference	Leader	Follower	Difference
Size → Sustainability Report	0.062	0.102	−0.040	0.137	0.043	0.094
Age → Sustainability Report	0.186	0.154	0.032	0.289	0.144	0.145 *
Sustainability Report → NPM	0.228	0.294	−0.066	0.487	0.231	0.256 **
Sustainability Report → ROA	0.062	0.102	−0.040	0.137	0.043	0.094
Sustainability Report → Tobin’s Q	0.186	0.154	0.032	0.289	0.144	0.145 *

Note: * p-Value < 0.05; ** p-Value < 0.01.

, two models were employed to classify companies as leaders or followers. Model I uses the Sustainability Reporting Disclosure Index (SRDI) as the basis for classification, while Model II applies the Managerial Herding (MHR) Index.

In Model I (SRDI-based classification), the results reveal no significant differences between leader and follower companies in the influence of sustainability reporting on performance outcomes. This suggests that the extent of sustainability disclosure alone does not differentiate the financial benefits derived from such reporting.

In contrast, Model II (MHR-based classification) shows significant moderating effects. The influence of sustainability reporting on Return on Assets (ROA) is significantly stronger among leader firms—those whose capital structure closely aligns with the industry average—compared to followers. Similarly, the effect on Tobin’s Q is notably higher for leader companies than for followers, indicating that firms demonstrating more independent capital structure decisions benefit more from sustainability disclosures in terms of market valuation. Additionally, the impact of company age on sustainability reporting differs significantly between leaders and followers in this model, suggesting that more established firms tend to integrate sustainability practices more effectively when they maintain capital structure discipline.

These findings support Hypothesis 7 (H7), affirming that herding behavior, as measured by MHR, moderates the relationship between sustainability reporting and corporate financial performance. This underscores the importance of strategic independence in capital structure decisions as a catalyst for deriving greater value from sustainability efforts.

5. Discussion

This study explores how company characteristics influence sustainability reporting and how such reporting, in turn, affects company performance, while accounting for the moderating role of herding behavior in non-financial firms across five ASEAN countries. The findings offer key insights into sustainability finance and organizational behavior, particularly within emerging economies navigating climate-related disclosure demands and stakeholder pressures.

The confirmation of herding behavior in capital structure decisions (H1) reinforces the view that companies are not purely guided by internal fundamentals but often mirror peer behavior in response to market signals or perceived norms. This aligns with earlier evidence by Camara (2017) and Aharon (2021), who argue that herding intensifies during periods of uncertainty, acting as a form of strategic conformity. From an institutional theory perspective, this mimetic isomorphism helps firms gain legitimacy and reduce risk in ambiguous environments, especially in emerging markets with evolving regulatory landscapes (DiMaggio & Powell, 1983).

Importantly, this behavioral pattern underscores a double-edged implication: while mimicking market leaders may encourage broader ESG adoption, it risks diluting the strategic authenticity of climate action if firms merely follow disclosure trends without embedding them meaningfully into operations. Thus, herding can function as both an accelerator and a constraint in advancing corporate climate resilience.

The finding that larger firms are less likely to engage in sustainability reporting (H2 is not supported) challenges traditional assumptions that firm size correlates with higher transparency. While prior studies (Schreck & Raithel, 2015; Simoni et al., 2020) suggest that large firms disclose more to meet stakeholder expectations, this study finds a reverse pattern. This could reflect diminishing marginal incentives for firms that have already built substantial reputational legitimacy, or perhaps they possess sufficient market power to reduce external pressures for extensive disclosure. Alternatively, this may indicate strategic opacity, where larger, more complex firms selectively disclose to manage information flow, a phenomenon that warrants further qualitative investigation.

In contrast, company age has a positive relationship with sustainability reporting (H3 is supported), indicating that older firms are more likely to integrate ESG considerations into their disclosures. This result is consistent with institutional theory, suggesting that more established firms typically develop greater organizational maturity, formalized reporting structures, and long-term stakeholder engagement strategies that drive transparent ESG disclosure practices (Bhatia & Tuli, 2017; Farisyi et al., 2022; Meyer & Rowan, 1977).

Sustainability reporting has a significant relationship with financial and market-based performance measures—ROA and Tobin’s Q (H5 and H6 are supported)—but has no significant relationship to net profit margin (H4 is not supported). This indicates that while sustainability practices may boost operational efficiency and investor valuation, their financial benefits may not be immediate or visible in short-term profitability due to upfront costs or the long-term nature of climate investments (Alodat et al., 2024; Friede et al., 2015). From a signaling theory perspective, positive relationships with ROA and Tobin’s Q suggest that sustainability disclosures effectively signal a firm’s superior operational management and future value creation capabilities to the market and investors (Connelly et al., 2010; Spence, 1973).

However, the lack of relationship with NPM highlights that the benefits, while real, may not immediately translate into higher net income due to the initial investments required for ESG initiatives (Eccles et al., 2014; Khan et al., 2016). This reinforces the strategic value of sustainability as a long-term lever for competitive advantage rather than a short-term profitability driver. ESG-aligned firms tend to prioritize stakeholder trust, regulatory preparedness, and environmental resilience over short-term profitability metrics. As Clark et al. (2015) note, firms that embed ESG principles—particularly those addressing environmental and climate risks—are better positioned to withstand external shocks, attract long-term investors, and improve capital efficiency over time. Thus, while net profit margins may not reflect the benefits of sustainability reporting in the short run, these disclosures signal a firm’s forward-looking orientation and adaptive capability in the face of climate change. In this light, sustainability reporting is not merely a compliance tool but a strategic mechanism for signaling resilience, ensuring stakeholder alignment, and strengthening long-term financial performance through climate-conscious governance.

The multigroup analysis reveals that the strength of the sustainability–performance relationship varies substantially across countries. The Philippines exhibits the highest performance impact, followed by Malaysia, Indonesia, Thailand, and Singapore. These cross-country differences reflect disparities in regulatory environments, market maturity, and cultural engagement with ESG principles (Christensen et al., 2021; Ioannou & Serafeim, 2019). From the combined perspective of stakeholder theory and institutional theory, these differences can be interpreted as outcomes of differing levels of stakeholder salience and institutional pressure—both coercive and normative—that shape how sustainability practices are internalized and translated into performance outcomes (Frynas & Yamahaki, 2016; Marano et al., 2017).

In the case of the Philippines, the strongest relationship between sustainability reporting and company performance may stem from robust regulatory reforms—such as the enhanced disclosure requirements by the Securities and Exchange Commission—combined with cultural values that emphasize collectivism and strong societal expectations for corporate responsibility (Aruta & Paceño, 2021; Barral, 2024; Pauline et al., 2019). Malaysia also demonstrates a strong relationship, likely supported by its mature regulatory infrastructure through the Bursa Malaysia Sustainability Framework and active government initiatives promoting ESG integration, which help firms align sustainability practices with performance objectives (Mohammad & Wasiuzzaman, 2021; Tang, 2023).

In contrast, Indonesia and Thailand show relatively weaker effects, possibly due to limited regulatory enforcement, emerging investor pressure, and less institutionalized stakeholder activism (Prisandani, 2022; Rahmaniati & Ekawati, 2024; Terdpaopong et al., 2025). Still, growing international scrutiny and evolving global standards may strengthen the influence of sustainability reporting in these countries over time. Singapore, despite its reputation for strong governance, records the weakest relationship—potentially reflecting a saturation point where sustainability disclosures are already expected and thus no longer generate significant performance differentials (Bansal et al., 2021; Barral, 2024; Chen, 2024).

The moderating role of herding behavior reveals important distinctions in how firms translate sustainability reporting into performance outcomes. When herding is measured through capital structure conformity (MHR), leader firms—those whose financing decisions closely align with industry norms—demonstrate significantly stronger positive relationships with sustainability reporting on ROA and Tobin’s Q than follower firms. This finding supports Hypothesis 7 and aligns with prior research (De Mendonca & Zhou, 2020; Do & Nguyen, 2020), highlighting the strategic value of financial consistency in amplifying the benefits of ESG practices. In contrast, no significant moderating effect is found when leader–follower classification is based solely on disclosure volume (SRDI), suggesting that behavioral alignment may be a more meaningful indicator of ESG integration than reporting intensity alone.

These findings align with institutional and signaling theories, which argue that organizations often adopt similar practices to gain legitimacy and reduce uncertainty (Bebbington et al., 2008; Clarkson et al., 2008; Dhaliwal et al., 2011). Firms that demonstrate financial discipline while pursuing sustainability are likely seen as more credible by investors and stakeholders, enhancing the signaling value of their ESG disclosures (Beyer et al., 2010). As Eccles et al. (2014) show, firms with integrated ESG strategies—not just high disclosure—tend to outperform peers in the long run. The observed leader firms may reflect this integration, using both financial and sustainability coherence as a competitive advantage.

Conversely, follower firms with inconsistent capital structures may engage in sustainability disclosure more reactively or mimetically, reducing the strategic depth and performance impact of their ESG efforts (Kusumawati, 2024; Zhao et al., 2025). This highlights the double-edged nature of herding: while it can accelerate ESG adoption across industries, it may also dilute its effectiveness if firms adopt sustainability practices superficially, as suggested by behavioral finance where mimetic behaviors can lead to sub-optimal decisions if not aligned with genuine strategic intent (Brunner & Ostermaier, 2019). For ESG to drive long-term value and climate resilience, firms must move beyond imitation and embed sustainability within core decision-making frameworks—a shift that regulators and investors should incentivize.

Collectively, these results underscore that sustainability reporting positively contributes to financial and market outcomes, but the magnitude and pathways vary across firm characteristics, countries, and behavioral dynamics. This highlights the critical need for nuanced regulatory and managerial strategies that account for contextual differences.

6. Conclusions

This study investigates how ESG disclosure—operationalized through sustainability reporting—relates to corporate financial performance and how herding behavior moderates this relationship among non-financial firms across five ASEAN countries. The results demonstrate that sustainability reporting has a positive relationship with return on assets (ROA) and Tobin’s Q, though it does not yield a significant relationship with net profit margin (NPM). Furthermore, the results also highlight that herding behavior, particularly when measured through capital structure alignment, moderates this relationship, with leader firms gaining more from sustainability efforts than followers. These outcomes emphasize the double-edged nature of herding: while it can accelerate ESG adoption, it may also dilute the strategic intent of climate action when driven by imitation. The study offers insights for advancing ESG finance as a mechanism to strengthen corporate climate resilience and long-term stakeholder value in emerging markets.

The study offers several practical implications for regulators, corporate strategists, and investors. For policymakers, the observed variations across countries suggest the need for tailored regulatory frameworks that not only enforce sustainability disclosure but also promote genuine climate-related action. In countries with weaker ESG-performance links, regulatory reform should move beyond compliance to incentivize meaningful, outcome-driven climate reporting.

For corporate leaders, the evidence underscores that sustainability reporting is most effective when embedded within a financially disciplined strategy. Firms that lead rather than follow are better positioned to convert ESG initiatives into long-term performance gains. This calls for greater integration of climate strategies into capital structure planning, risk management, and investment decisions. For investors, the findings highlight the importance of assessing not just the volume of ESG disclosure, but also the consistency of financial behavior and the authenticity of climate action, especially in emerging markets vulnerable to climate risks.

While this study provides valuable cross-country insights, several limitations should be noted. First, its focus on five ASEAN countries limits broader generalizability. Second, the data timeframe may not fully capture the delayed impacts of ESG activities, particularly regarding profitability. Third, although sustainability reporting was used as a proxy for ESG disclosure, it may not fully reflect firms’ environmental or social performance depth. Finally, while herding behavior was assessed using two models—disclosure-based (SRDI) and financial behavior-based (MHR)—the analysis was limited to leader–follower dynamics in sustainability reporting and capital structure.

It is important to acknowledge a key limitation of the model, particularly the relatively low R-squared values for our endogenous constructs (sustainability reporting, NPM, ROA, and Tobin’s Q), which were all below 0.10 (as shown in Table 6). While the model demonstrates predictive relevance (Q² > 0) and meets other validity criteria, these low R-squared values suggest that the variables included in our model explain only a small proportion of the variance in these outcome variables. This implies that other significant, unmeasured factors—such as broader macroeconomic conditions beyond general market volatility, more granular firm-specific governance structures, or sector-specific environmental risks—likely play a substantial role in determining sustainability reporting practices and financial performance in ASEAN firms.

Future studies should consider incorporating a broader range of variables to enhance the explanatory power of such models, ensuring a more comprehensive understanding of these complex relationships. Additionally, expanding the geographic scope beyond ASEAN could assess whether similar dynamics between ESG disclosure, herding behavior, and corporate financial performance hold in different institutional contexts. Longitudinal designs would be beneficial to capture the long-term financial implications of sustainability initiatives, especially concerning profitability metrics like net profit margin. Broader ESG measurement frameworks—incorporating environmental performance data, governance quality, and actual climate-related actions—could provide a more holistic view of corporate sustainability. Exploring other forms of herding behavior, such as imitation in green technology adoption or responses to climate-related regulations, could deepen our understanding of behavioral influences on ESG integration. Lastly, sector-specific analyses, particularly in high-emission industries, may also offer insights into how strategic ESG alignment varies across business models and regulatory exposure.