Sustainable Development Goal (SDG) Disclosure and Firm Value: Empirical Evidence from Southeast Asia

Abstract

Amid growing global attention to corporate sustainability and responsible investment, the disclosure of Sustainable Development Goals (SDGs) has emerged as an important component of non-financial reporting. However, the extent to which SDG disclosure contributes to firm value remains underexplored, particularly in emerging markets. This study examines the association between SDG disclosure in corporate reports and firm value among 660 publicly listed companies across four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. SDG disclosure is measured using 17 SDG indicators derived from the Refinitiv database and should be interpreted as a measure of disclosure breadth rather than disclosure quality or depth. The analysis begins with descriptive statistics to illustrate the distribution of key variables, followed by ANOVA to assess differences in SDG disclosure across countries and industries. Hypothesis testing is then conducted using multiple regression analysis with robust standard errors, with firm value proxied by price-to-book value (PBV). Several robustness checks are performed, including winsorised regression, year-by-year regressions, and regression models incorporating country and industry dummy variables. The results indicate that SDG disclosure is positively associated with firm value, although the relationship is interpreted as correlational rather than causal because of the short observation period and potential endogeneity. The findings also show that SDG disclosure is unevenly distributed across goals and countries, with SDG 8 and SDG 13 receiving the highest attention, while SDG 2 and SDG 14 remain among the least disclosed. These results highlight the importance of sustainability transparency in shaping market valuation and underscore the need for more balanced, comparable, and quality-oriented sustainability reporting frameworks across the region.

1. Introduction

The implementation and assessment of Sustainable Development Goals (SDGs) within corporate entities have garnered considerable attention in recent years, indicating an increasing focus on sustainable business practices. This development is closely aligned with Environmental, Social, and Governance (ESG) performance metrics, as both frameworks seek to address global challenges and foster responsible corporate conduct. The implementation of SDGs offers companies a structured methodology to contribute to global sustainability objectives while simultaneously enhancing their ESG performance (Nicolo’ et al., 2023). By aligning business strategies with SDG targets, corporations can demonstrate their commitment to sustainable development, potentially enhancing their reputation, attracting socially conscious investors, and mitigating long-term risks (Ordonez-Ponce & Khare, 2020). The integration of SDGs into corporate reporting and decision-making processes facilitates a more comprehensive evaluation of sustainability efforts, which can, in turn, positively influence ESG ratings and the overall firm value (Santos & Silva Bastos, 2020).

Corporate reporting on the Sustainable Development Goals (SDGs) has gained prominence as a means of demonstrating a company’s dedication to sustainability and social responsibility (SR). Typically, the disclosure process involves integrating SDG-related information into existing reporting frameworks, such as annual, sustainability, or integrated reports (Awuah et al., 2023). Companies often begin by identifying the SDGs that are most pertinent to their business operations and strategic objectives. Subsequently, they delineated specific targets, initiatives, and key performance indicators (KPIs) aligned with these goals (Moyeen & Mehjabeen, 2024). The role of SDG disclosure in corporate reporting is multifaceted: it enhances transparency, enables stakeholders to evaluate a company’s sustainability efforts, facilitates comparisons between organisations, and illustrates the company’s contribution to global sustainability objectives (Beyne, 2020). Furthermore, this reporting practice can assist companies in identifying new business opportunities, managing risks, and attracting socially conscious investors and customers (Domingo-Posada et al., 2024). By incorporating SDGs into their reporting, companies can effectively communicate their sustainability strategies and progress while contributing to the broader global agenda for sustainable development.

Although the 17 Sustainable Development Goals (SDGs) offer a comprehensive framework for global advancement, not all corporations have effectively implemented programs to support them. Common challenges include the absence of clear metrics for assessing impact, difficulties in aligning business objectives with SDG targets, and limited resources for implementation, particularly among smaller enterprises (Tsalis et al., 2020). Many companies encounter challenges in integrating SDG initiatives into their core business strategies, often treating them as separate corporate social responsibility projects. Expanding on these challenges, companies frequently find it difficult to quantify and report their SDG-related progress due to the lack of standardised measurement frameworks (Diaz-Sarachaga, 2021). This lack of clarity can impede effective decision-making and stakeholder communication. Furthermore, the complexity of aligning profit-driven business models with broader societal goals often results in superficial or disconnected sustainability efforts rather than transformative changes that could significantly contribute to SDG achievement (Avrampou et al., 2019). Additionally, the complexity and interconnectedness of the SDGs can be overwhelming, leading to a focus on only a few goals rather than a holistic approach to sustainability. Insufficient stakeholder engagement, inadequate government support, and a short-term profit-oriented mindset hinder widespread adoption (Rosati & Faria, 2019). Moreover, the varying relevance of specific SDGs across industries and geographical regions complicates uniform implementation, resulting in uneven progress towards these global objectives.

The current state of Sustainable Development Goal (SDG) disclosure among companies in Southeast Asia is undergoing transformation, with varying degrees of adoption and reporting across different nations within the region (Tran et al., 2021). While some leading corporations have fully embraced SDG reporting, numerous others remain in the nascent stages of integrating these goals into their corporate strategies and disclosures (Sekarlangit & Wardhani, 2021). Analysing and comparing SDG disclosure practices in Southeast Asia with those in developed countries is essential for several reasons. First, it aids in identifying gaps and opportunities for enhancing sustainability reporting within the region (Suriyankietkaew & Nimsai, 2021). Second, such comparisons can facilitate the transfer of knowledge and sharing of best practices between developed and developing economies (Hamad et al., 2022). Finally, as global investors increasingly prioritise sustainability factors in their decision-making processes, improving SDG disclosure among Southeast Asian companies can attract more foreign investment and promote sustainable economic growth in the region (Phan, 2024).

This regional focus is important because existing SDG disclosure studies often concentrate on developed markets, single-country settings, or broad sustainability reporting practices without fully examining how investors in emerging Southeast Asian capital markets value SDG disclosure. Prior studies provide useful evidence on SDG reporting adoption, governance determinants, and sustainability communication. However, the empirical link between the breadth of SDG disclosure and market-based firm value remains less settled, especially in countries where regulatory requirements, investor expectations, and reporting maturity differ substantially (Sekarlangit & Wardhani, 2021; Tran et al., 2021; Awuah et al., 2023). This study contributes by comparing firms across Indonesia, Malaysia, Singapore, and Thailand and by showing how SDG disclosure varies not only across goals but also across country and industry contexts. Using a standardised Refinitiv-based measure, this study provides cross-firm comparability while acknowledging that standardised binary indicators do not fully capture disclosure quality, narrative credibility, or substantive SDG performance.

This study investigates the implementation and disclosure of Sustainable Development Goals (SDGs) in corporate reporting practices, focusing on Southeast Asian companies in response to the increasing global emphasis on sustainability and responsible business conduct. Specifically, this study examines whether broader SDG disclosure is associated with higher firm value, thereby assessing whether sustainability transparency is reflected in market valuation. This study does not claim that disclosure automatically creates value; rather, it examines whether the market rewards firms that provide more visible SDG-related information. This study contributes to the literature in three ways. First, it provides regional evidence from four Southeast Asian markets that are economically important but institutionally diverse in their ownership structures. Second, it offers a goal-level profile of SDG disclosure, identifying which SDGs receive stronger or weaker corporate attention. Third, it evaluates the relationship between SDG disclosure and PBV while controlling for firm size, profitability, and leverage, and tests the stability of the results through several robustness specifications. These contributions are expected to inform policy recommendations, support stakeholder decision-making, and promote comparable sustainability reporting practices in emerging economies.

The structure of this paper is as follows: Section 2 reviews the relevant literature and develops a theoretical argument concerning SDG disclosure, legitimacy, signalling, institutional pressure, and firm value. Section 3 explains the research methodology, including the data sources, sample selection, variable measurements, and analytical techniques. Section 4 presents the empirical results, robustness tests, and a discussion of the findings. Section 5 provides concluding remarks, managerial and policy implications, limitations, and future research directions.

2. Literature Review and Hypothesis Development

2.1. Sustainable Development Goals in Corporations

The implementation of the Sustainable Development Goals (SDGs) in corporations involves integrating these global objectives into various facets of corporate operations, particularly focusing on governance, strategy, risk management, and reporting (Megawati & Pratama, 2024). In the context of governance, corporate governance mechanisms play a crucial role in achieving SDGs by ensuring that the internal governance structure aligns with sustainable practices. Internal governance strength, such as CEO independence, board composition, and attendance, impacts a firm’s commitment to SDG reporting, suggesting that robust governance frameworks facilitate accountability in sustainability initiatives (Martínez-Ferrero & García-Meca, 2020). Board characteristics, especially board independence and size, alongside the presence of a corporate social responsibility (CSR) committee, enhance SDG-related reporting, thereby linking governance structures to the successful integration of SDGs into strategic corporate initiatives (Jiang et al., 2023).

Aligning business strategies with the SDGs demands integrated thinking that incorporates sustainability into corporate strategy, fostering long-term value creation through sustainable business models (Ahmed, 2023). Strategically, businesses must forge sustainable partnerships, practices, and policies that address contemporary challenges such as environmental sustainability, reflecting an organisation’s adaptability to integrate SDGs into its strategic framework (Mahajan et al., 2024). Furthermore, institutional investors promote corporate transparency and drive strategic alignment with the SDGs, emphasising the necessary integration of these goals into corporate strategies to meet diverse stakeholder expectations (García-Sánchez et al., 2020).

In risk management, corporate governance mechanisms, particularly those related to risk management committees, affect how firms document risk in integrated reports (Ahmed, 2023). Effective risk management involves identifying potential sustainability-related risks and integrating them into the overall risk management framework, thereby allowing for proactive measures to address these challenges. Strong governance structures that facilitate risk disclosure behaviour encourage companies to go beyond mandatory disclosures, potentially mitigating the risks associated with SDG implementation (Lajili, 2009).

Finally, in terms of reporting, achieving alignment in sustainability reporting using frameworks such as the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) helps corporations address SDG-related challenges (Kücükgül et al., 2021). Optimising SDG reporting involves a structural alignment approach to harmonise various SDG guides, ensuring that corporations effectively communicate their achievements and challenges related to the SDGs. However, there are shortcomings in current reporting practices, such as inconsistencies and a lack of standardisation, which need to be addressed to enhance the quality and reliability of corporate sustainability reports (Diaz-Sarachaga, 2021).

2.2. Disclosure Theory of SDG

In recent years, the integration of Sustainable Development Goals (SDGs) into corporate reporting has gained significant attention in both academic and business communities. Accounting and disclosure theories play an important role in explaining why organisations report their contributions to these global goals and how such disclosures may be associated with firm value. In this study, SDG disclosure is understood through several complementary theoretical perspectives: stakeholder theory, agency theory, legitimacy theory, and institutional theory.

Stakeholder theory explains SDG disclosure as a response to the information needs and expectations of various stakeholder groups, including investors, regulators, creditors, employees, customers, communities, and society (Jun & Kim, 2021). From this perspective, firms disclose SDG-related information to demonstrate how their activities contribute to sustainable development and maintain constructive relationships with stakeholders. As stakeholders increasingly evaluate firms using both financial and non-financial information, SDG disclosure functions as a mechanism for stakeholder engagement, accountability, and reputation management (Lin et al., 2024). In this context, companies that communicate their SDG-related activities extensively may strengthen stakeholder trust and improve external perceptions of their long-term orientation.

Agency theory further explains SDG disclosure from the perspective of information asymmetry between managers and capital providers. Managers possess more internal information about corporate strategy, sustainability risks, and long-term resource allocation than external investors (Sun et al., 2022). Broader SDG disclosure may therefore help reduce information asymmetry and monitoring costs by providing investors with additional non-financial information regarding managerial commitment to sustainability, risk management, and responsible resource allocation (Bose et al., 2023). This argument is also consistent with signalling theory, which suggests that structured SDG disclosure may function as a signal of long-term orientation, risk awareness, and strategic responsiveness, particularly because investors cannot directly observe all aspects of a firm’s sustainability strategy (Guidi et al., 2025). Institutional investors also have a substantial influence on corporate sustainability transparency, as their interest in sustainability can encourage firms to improve their reporting of SDG-related activities. Moreover, the presence and concentration of different types of institutional investors, such as pension funds and foreign investors, may contribute to variations in quality and transparency (García-Sánchez et al., 2020).

Legitimacy theory provides another important explanation for SDG disclosures. According to legitimacy theory, organisations disclose information related to SDGs to maintain or strengthen their legitimacy in the eyes of their stakeholders (Manes-Rossi & Nicolo’, 2022; Di Vaio et al., 2022). Because SDGs represent globally recognised development priorities, firms may use SDG disclosure to show that their activities align with broader social values and sustainable development norms. Under this view, SDG disclosure can improve public accountability and strengthen investor confidence when stakeholders perceive the disclosure as credible and aligned with corporate actions (Hummel & Szekely, 2021). However, legitimacy theory also recognises that disclosure may be symbolic, selective, or compliance-oriented. Firms may engage in symbolic reporting by projecting an image of responsibility without substantial changes in their underlying operations, creating a possible gap between disclosed commitment and actual practice (Zampone & Guidi, 2024). Therefore, the relationship between SDG disclosure and firm value should not be treated as automatic but should be examined empirically.

Institutional theory complements these perspectives by explaining how external pressures from the institutional environment shape SDG disclosure. Firms may disclose SDG-related information because of coercive pressures from regulations, normative pressures from professional and reporting standards, and mimetic pressures from peer companies (Herold, 2018). Institutional and signalling perspectives suggest that external pressures, including institutional investors and regulatory frameworks, influence organisations’ disclosure of SDG-related activities (Giordino et al., 2023; Krasodomska et al., 2023). The challenges and evolution of SDG reporting differ across sectors and regions. For example, in the European Union, the level of SDG implementation and the use of reporting standards, such as the Global Reporting Initiative (GRI), can influence how organisations report their SDG contributions (Izzo et al., 2020; Gutiérrez-Ponce, 2023). Additionally, national policies and organizational preferences may determine the extent and depth of SDG reporting (Chagas et al., 2022). Therefore, institutional theory helps explain why SDG disclosure practices may vary across countries, industries, and reporting environments.

2.3. Hypothesis Development

SDG disclosure can be positioned as a form of non-financial information that may enter investors’ assessments of firm value. Stakeholder theory suggests that firms disclose sustainability-related information to respond to the expectations of parties affected by or interested in corporate activities (Saeed et al., 2025). Agency theory further implies that such disclosure may reduce information asymmetry between managers and investors by providing additional information on corporate priorities, risk awareness, and long-term orientation (De Silva Lokuwaduge et al., 2022). From the perspective of legitimacy theory, SDG disclosure may help firms demonstrate that their activities align with broader social expectations and globally accepted sustainability goals (Al Lawati & Hussainey, 2022). Institutional theory also indicates that firms’ disclosure practices are shaped by regulatory, normative, and peer pressures that increasingly emphasise sustainability transparency (Grassa et al., 2025). These theoretical arguments provide the basis for explaining why SDG disclosure may be linked to firm value through transparency, investor perceptions, and corporate legitimacy.

The possible effect of Sustainable Development Goal (SDG) disclosure on firm value is based on the relationship between transparency, investor perception, and corporate legitimacy. Companies are increasingly aligning their CSR and sustainability strategies with the SDGs to respond to investors’ interest in corporate contributions to the 2030 Agenda. Foreign investors and pension funds may increase the demand for SDG-related information and encourage companies to adopt more systematic disclosure strategies (García-Sánchez et al., 2020). From a market perspective, SDG disclosure can help investors evaluate whether firms are exposed to sustainability risks, whether they are prepared for regulatory and stakeholder pressure, and whether their strategies are aligned with long-term value creation. Several studies have provided insights into this relationship, showing that SDG-related activities can influence investor behaviour, institutional ownership, transparency, and performance outcomes (Paetzold et al., 2022; Giordino et al., 2023; Vallet-Bellmunt et al., 2022).

The relationship between SDG disclosures and firm performance can also be understood through the lens of green innovation. While green product innovations positively impact returns on equity and investments, other types of green innovations may have mixed effects on financial performance (Khan et al., 2021). Therefore, the integration of green initiatives with SDG disclosures can be perceived positively by investors, reinforcing a company’s market value and competitive advantage (Toukabri & Mohamed Youssef, 2022).

ESG reporting is instrumental in communicating a company’s contribution to the SDGs and has been shown to influence corporate profitability and stakeholder evaluations (Treepongkaruna & Suttipun, 2024). Companies that effectively integrate ESG considerations into their reporting processes may attract stakeholders, improve their credibility, and maintain a competitive edge, which can influence both firm value and investor perception (Zhan & Santos-Paulino, 2021). SDG disclosure and sustainability reporting thus play a role in aligning corporate strategies with global sustainability goals while enhancing transparency and credibility (Pratama et al., 2024). However, this mechanism is not necessarily unidirectional. Firms with higher values may also have stronger resources to produce more complete sustainability reports, and some firms may disclose SDG information symbolically without substantive strategic integration. Therefore, this study interprets the empirical test as an examination of association rather than definitive causality. Based on legitimacy, signalling, and institutional perspectives, the following hypothesis is proposed:

H₁. 

SDG disclosure has a positive and significant association with firm value, as measured by the price-to-book value (PBV) ratio.

3. Method

This study adopts a quantitative research approach to examine the association between corporate Sustainable Development Goal (SDG) disclosure and firm value among listed companies in Southeast Asia. SDG disclosure is measured by identifying whether each firm reports information related to all 17 SDGs. Each SDG item is measured using a binary score, where a value of 1 is assigned if the company discloses information related to the relevant SDG and 0 if no such disclosure is identified. Therefore, the total SDG disclosure score represents the sum of the 17 SDG items disclosed by the company. This score is then used to indicate the extent of SDG disclosure, rather than its quality. This approach is appropriate because the SDGs themselves are not designed as a corporate disclosure standard but as a global development agenda. In addition, widely used sustainability reporting frameworks, such as the GRI Standards and IFRS Sustainability Disclosure Standards, do not prescribe a specific mandatory disclosure format for reporting all 17 SDGs. Consequently, assessing the depth, credibility, or narrative quality of SDG disclosure requires more subjective content analysis and may reduce comparability across firms and countries.

This study uses a sample of 660 publicly listed companies across four Southeast Asian countries: Indonesia (77 firms), Malaysia (333), Singapore (76), and Thailand (170). These countries were selected because they represent major Southeast Asian capital markets with sufficient Refinitiv ESG coverage and provide variation in regulatory environments, reporting maturity, and market development. Other Southeast Asian countries were not included because complete and comparable Refinitiv data for all variables were limited during the observation period. The relatively large Malaysian subsample reflects the availability of complete observations in the database and should be interpreted as a sample-composition feature rather than a deliberate weighting choice. To reduce the risk that the results are driven only by this imbalance, the study includes robustness tests with country and industry dummy variables.

The data span two fiscal years, 2022 and 2023, which were selected to ensure relatively current post-pandemic reporting and valuation. Earlier pandemic years were excluded to reduce the influence of extraordinary COVID-19 disruptions; however, the short timeframe limits the ability to infer long-term trends and is acknowledged as such. The Refinitiv ESG database is used as the primary source of SDG disclosure data because it provides standardised, comparable, and publicly traceable indicators derived from companies’ annual and sustainability reports.

To enhance industry-specific insights, companies were classified according to the Global Industry Classification Standard (GICS). This globally recognised taxonomy enables the consistent categorisation of companies into sectors and industries, thereby facilitating a meaningful analysis of disclosure trends across different economic sectors. The use of GICS allows this study to uncover whether firms in certain industries—such as energy, finance, or consumer goods—are more proactive or strategic in their SDG reporting practices, which may also relate to sector-specific stakeholder pressures and regulatory expectations.

Table 1. Operationalisation of variables.

Variable	Explanation
SDG Disclosures (X1)	Measured by checking whether the company disclosed information relevant to SDG 1 to SDG 17. The SDG disclosure data are taken from the Refinitiv Eikon database No Poverty; Zero Hunger; Good Health and Well-being; Quality Education; Gender Equality; Clean Water and Sanitation; Affordable and Clean Energy; Decent Work and Economic Growth; Industry, Innovation and Infrastructure; Reduced Inequalities; Sustainable Cities and Communities; Responsible Consumption and Production; Climate Action; Life Below Water; Life on Land; Peace, Justice and Strong Institutions; Partnerships for the Goals. Each disclosed item is scored 1, and each undisclosed item is scored 0. The total score is divided by 17 to produce a final value between 0 and 1. Equal weighting is applied because there is no established and widely accepted weighting scheme for assigning different importance to the 17 SDGs across countries and industries. Therefore, the index should be interpreted as a transparent and comparable measure of disclosure breadth, not as a direct measure of disclosure quality, depth, credibility, or SDG performance.
Company Size (X2)	Company size is calculated based on the Natural Logarithm (Ln) of the total asset value. The company size value is taken from Total Assets—Reported from the Refinitiv Eikon database.
Profitability (X3)	Profitability is calculated from the Return on Asset (ROA) value, which is net income divided by total assets. Net income is taken from Net Income After Taxes—Reported, and total assets from Total Assets—Reported in the Refinitiv Eikon database.
Leverage (X4)	Leverage is measured based on the Debt-to-Equity Ratio (DER), calculated by dividing total debt by total equity. Total debt and total equity are taken from Total Debt—Reported and Total Equity—Reported in the Refinitiv Eikon database.
Firm Value (Y)	Firm value is measured using Price to Book Value (PBV), calculated by dividing the company’s market price per share by its book value per share, as reported in Refinitiv Eikon.

explains the operationalisation of the variables used in this study.

The data analysis was conducted as follows:

• Descriptive statistics were employed to provide an overview of the distribution and characteristics of each variable. This includes calculations of the mean, standard deviation, minimum, and maximum values for all variables under observation, namely, SDG disclosures (X1), company size (X2), profitability (X3), leverage (X4), and firm value (Y). Beyond the general descriptive statistics, a more detailed examination was conducted specifically for the SDG disclosure variable (X1), where values were analysed by country (Indonesia, Malaysia, Singapore, and Thailand). This approach allows us to capture potential improvements in sustainability-related disclosures across different national and sectoral contexts, thereby offering a richer interpretation of corporate alignment with global SDGs.
• To determine whether significant differences exist in SDG disclosure and other variables across countries and industry sectors, this study employs Analysis of Variance (ANOVA). This statistical method is appropriate for identifying whether the means of variables differ significantly between multiple groups. In this case, ANOVA is used to compare firms’ performance on SDG disclosure and other financial indicators between Southeast Asian countries and across industry classifications. This step is essential for highlighting the structural variations and context-specific drivers of sustainability reporting practices.
• Finally, multiple regression analysis was conducted to test the research hypothesis and examine the association between SDG disclosure (X1) and firm value (Y), while controlling for company size (X2), profitability (X3), and leverage (X4). The dataset has a short panel structure covering 2022–2023. Because the time dimension is limited, and several disclosure variables may exhibit limited within-firm variation over two years, this study does not claim to estimate a dynamic causal panel model. Instead, the main analysis is presented as a pooled firm-level regression with robust standard errors, supplemented by year-specific regressions and dummy variable specifications. Given the presence of outliers and heteroscedasticity in the data, robust standard errors were applied to improve the reliability of the statistical inference. Robust standard errors are preferred because they correct the estimated standard errors without changing the coefficient estimates, making them suitable for examining associations in firm-level financial data with unequal variances.
• This study also recognises the possibility of reverse causality and endogeneity. Firms with higher PBV may have more resources, investor visibility, and organizational capacity to prepare SDG disclosures, while broader SDG disclosures may also be associated with stronger investor confidence. Because the available dataset covers only two years and does not contain a clearly valid external instrument, the empirical results are interpreted as associational rather than causal. Therefore, the robustness tests strengthen the stability of the observed relationship, but they do not fully eliminate the possibility of reverse causality, omitted variables, or simultaneity.
• The explanatory power of the model was interpreted cautiously. Firm value, measured by PBV, is affected by many observable and unobservable factors, including growth expectations, market sentiment, ownership structure, liquidity, macroeconomic conditions, and investor risk preference. Therefore, a relatively low R-squared in some specifications, especially the 2022 year-specific model, does not invalidate the estimated association; rather, it indicates that SDG disclosure and the included controls explain only part of the variation in firm value. Therefore, the analysis emphasises coefficient direction, statistical significance, robustness checks, and theoretical consistency while avoiding claims that the model fully explains corporate valuation.
• To enrich the control structure within the available data, the baseline model includes firm size, profitability, and leverage, while the robustness specification adds country and industry dummy variables to the model. The country dummy variables are labelled as Malaysia, Singapore, and Thailand, with Indonesia as the reference country. The industry dummy variables are based on the Global Industry Classification Standard (GICS), with the financial sector used as the reference category. Additional controls, such as ownership structure, board characteristics, liquidity, analyst coverage, and sustainability assurance, were not included because they were not consistently available for all firms in the balanced sample; however, they are acknowledged as important variables for future studies.
• To examine the stability of the main findings, we performed several robustness tests. The purpose of these tests is to assess whether the relationship between SDG disclosure and firm value remains consistent with alternative model specifications, data treatments, and additional endogeneity checks. Specifically, the robustness analysis includes re-estimating the regression using winsorised variables to reduce the influence of extreme observations, conducting separate regressions for each year of observation, and incorporating country and industry dummy variables to control for structural differences across national and sectoral contexts. In the dummy variable specification, Indonesia and the financial sector are used as benchmark categories. In addition, this study conducts a two-stage least squares (2SLS) estimation to address the potential endogeneity between SDG disclosure and firm value. This issue may arise because firms with higher values may also have stronger resources, reporting systems, and stakeholder visibility, which may enable broader SDG disclosure. Therefore, SDG disclosure is considered a potentially endogenous variable. The 2SLS estimation uses lagged SDG disclosure and peer SDG disclosure at the country-year level as the instrumental variables. The first stage estimates SDG disclosure using instrumental and control variables, while the second stage examines the association between firm value and the instrumented value of SDG disclosure. Given the two-year observation period, the 2SLS analysis was interpreted as an endogeneity robustness test rather than definitive causal evidence.

Following the data analysis procedure described above, a baseline regression model is specified to test the association between SDG disclosure and firm value. The baseline regression model is as follows:

Y = α + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + ε

(1)

where

• α = Constant;
• β = Regression coefficient;
• Y = Firm Value;
• X1 = SDG Disclosure;
• X2 = Company Size;
• X3 = Profitability;
• X4 = Leverage;
• ε = Error.

4. Results and Discussion

4.1. Results

4.1.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variables	Mean	Std. Dev.	Min	Max
X1	0.48	0.30	0.00	1.00
X2	20.59	2.01	15.03	27.05
X3	0.04	0.08	−0.98	0.48
X4	1.91	4.74	0.02	99.61
Y	2.29	4.82	0.14	74.76

presents the descriptive statistics for all the variables used in this study.

The SDG disclosure variable (X1) has a mean value of 0.48 and a standard deviation of 0.30, indicating that, on average, companies disclosed approximately 48% of the 17 SDG items evaluated. The minimum value of 0.00 and the maximum of 1.00 confirm that the disclosure level varies widely across firms, from no disclosure to full disclosure on all SDGs. This variation reflects the different levels of corporate commitment to sustainability reporting across the sample. For firm size (X2), measured as the natural logarithm of total assets, the mean is 20.59 with a standard deviation of 2.01, and the values range from 15.03 to 27.05, suggesting that the sample includes a broad range of firms, from small to large in terms of asset size. Profitability (X3), proxied by Return on Assets (ROA), shows a relatively low mean of 0.04, with a standard deviation of 0.08. The negative minimum value (−0.98) indicates the presence of firms experiencing losses during the observation period, while the highest ROA recorded is 0.48, implying strong performance by some companies. Leverage (X4), measured by the Debt-to-Equity Ratio (DER), has a mean of 1.91 and a standard deviation of 4.74, with values ranging from 0.02 to 99.61. This wide dispersion signifies substantial differences in the capital structure among firms, with some highly leveraged companies potentially indicating greater financial risk. Lastly, the dependent variable, firm value (Y), measured by the price-to-book value (PBV), shows a mean of 2.29 and a standard deviation of 4.82. The minimum and maximum values of 0.14 and 74.76, respectively, also indicate a high level of variation, suggesting that some firms are highly valued in the market, while others trade significantly below their book value. The high standard deviations in X4 and Y further justify the decision to average the values over two years to reduce the influence of outliers in the subsequent regression analysis.

Table 3. Summary of the average number of companies that disclosed specific SDG components.

SDG Component	Average Number of Companies Disclosed
	Indonesia	Malaysia	Singapore	Thailand	Overall
SDG 1	62.99%	21.96%	10.53%	37.06%	29.32%
SDG 2	46.75%	16.62%	13.82%	23.24%	21.52%
SDG 3	75.97%	57.12%	63.16%	71.47%	63.71%
SDG 4	76.62%	45.25%	33.55%	66.18%	52.95%
SDG 5	62.99%	46.74%	40.13%	55.29%	50.08%
SDG 6	54.55%	37.24%	29.61%	52.94%	42.42%
SDG 7	59.09%	42.28%	59.21%	62.94%	51.52%
SDG 8	80.52%	68.25%	75.00%	81.18%	73.79%
SDG 9	62.99%	45.99%	56.58%	68.24%	54.92%
SDG 10	55.84%	40.80%	33.55%	52.06%	44.62%
SDG 11	53.90%	36.80%	50.00%	52.35%	44.32%
SDG 12	60.39%	62.17%	61.84%	80.59%	66.67%
SDG 13	72.73%	58.90%	69.74%	82.65%	67.88%
SDG 14	29.87%	19.29%	15.13%	27.35%	22.12%
SDG 15	53.25%	25.82%	19.08%	43.82%	32.88%
SDG 16	55.19%	49.55%	49.34%	69.71%	55.38%
SDG 17	46.10%	25.67%	45.39%	44.41%	35.15%

presents a summary of the average number of companies that disclosed specific Sustainable Development Goal (SDG) components across four Southeast Asian countries—Indonesia, Malaysia, Singapore, and Thailand—as well as the overall average. The purpose of this table is to provide a detailed picture of the extent to which individual SDG items are addressed in corporate sustainability reporting, allowing for the identification of disclosure patterns and potential gaps in alignment with global sustainability priorities.

From the table, it is evident that SDG 8 (Decent Work and Economic Growth) is the most frequently disclosed component, with an overall disclosure rate of 73.79% and especially high reporting rates in Thailand (81.18%) and Indonesia (80.52%). This result is not surprising, as SDG 8 aligns closely with core business objectives, such as employment, productivity, and labour conditions—topics that are both material and mandatory in many ESG frameworks. Similarly, SDG 13 (Climate Action) and SDG 12 (Responsible Consumption and Production) show relatively high disclosure rates of 67.88% and 66.67%, respectively, indicating growing corporate awareness of environmental sustainability and resource management. In contrast, the least disclosed SDG component is SDG 14 (Life Below Water), with an overall disclosure rate of only 22.12%. This trend is consistent across countries, reflecting a general lack of corporate focus on ocean-related sustainability issues, which are often perceived as sector-specific and less directly connected to core business activities, particularly for firms outside the maritime, fisheries, or coastal infrastructure sectors. SDG 2 (Zero Hunger) also ranks low in disclosure at 21.52%, likely because of its strong alignment with public policy and social programs, which are traditionally viewed as governmental responsibilities rather than business priorities.

When viewed by country, Thailand and Indonesia demonstrate relatively higher average disclosure levels across most SDG components, likely due to a stronger regulatory emphasis on sustainability reporting and broader ESG integration in public disclosures. Malaysia and Singapore, while actively engaged in sustainability practices, show more moderate disclosure levels, possibly due to differences in mandatory disclosure regimes, reporting standards, or the sectoral compositions of the listed firms. These findings illustrate both country- and issue-specific variations in corporate alignment with the SDGs, highlighting the need for targeted policy frameworks and corporate initiatives to close the remaining gaps.

4.1.2. ANOVA

Table 4. ANOVA test results.

Variable/Explanation	2-Years Average Score for X1
	Mean	Std. Dev
Per Country
Indonesia	0.594	0.299
Malaysia	0.412	0.301
Singapore	0.427	0.283
Thailand	0.571	0.274
ANOVA test-score	16.177
Sig	0.000 ***
Per Industry
Financials	0.507	0.307
Industrials	0.402	0.291
Real Estate	0.487	0.266
Communication Services	0.534	0.266
Consumer Staples	0.499	0.302
Information Technology	0.331	0.310
Consumer Discretionary	0.405	0.343
Utilities	0.639	0.252
Materials	0.516	0.311
Energy	0.583	0.241
Health Care	0.548	0.295
ANOVA test-score	4.085
Sig	0.000 ***

Notes: ***: significant at α = 1%.

presents the results of the ANOVA analysis for the two-year average SDG disclosure scores (X1), compared across both countries and industry sectors.

The country-level analysis reveals notable disparities in SDG disclosure, with Indonesia (mean = 0.594) and Thailand (mean = 0.571) exhibiting the highest average disclosure scores. These results suggest that companies in these two countries tend to be more proactive in aligning their corporate reports with the Sustainable Development Goals. This may be influenced by stronger regulatory encouragement, public pressure, or increasing investor awareness in these jurisdictions. In contrast, Malaysia (mean = 0.412) and Singapore (mean = 0.427) show relatively lower average disclosure scores, which may reflect either a more voluntary approach to sustainability reporting or less pressure to disclose SDG-related information. The ANOVA test score of 16.177, with a significance level of 0.000, indicates that the differences between countries are statistically significant, confirming that the national context plays an important role in shaping corporate sustainability disclosure behaviours.

At the industry level, the analysis indicates that SDG disclosures vary significantly across sectors. The Utilities sector stands out with the highest average score of 0.639, followed by the Energy (0.583) and Healthcare (0.548) sectors. These industries often operate under high environmental and social scrutiny and are more directly linked to sustainability-related risks and opportunities, which likely compels them to disclose information more comprehensively. On the other hand, the Information Technology sector shows the lowest average score of 0.331, followed by Industrials (0.402) and Consumer Discretionary (0.405). The relatively low disclosure in the IT and consumer-oriented sectors may stem from the perception that their operations are less visibly connected to the SDGs or due to a more limited regulatory focus on sustainability in these fields. The ANOVA test score of 4.085 and a significance level of 0.000 further confirm that the differences in disclosure across industries are significant.

4.1.3. Multiple Regression Analysis

Table 5. Classical assumption test.

Classical Assumption Test	Score	Criteria for Good Fit	Test Result
Normality test (Kolmogorov–Smirnov test)	0.000	sig > 0.05	Not passed
Multicollinearity test (Variance Inflation Factor)	1.014–1.280	VIF < 10	Passed
Heteroscedasticity test (White test)	0.000	sig > 0.05	Not passed

summarises the results of the classical assumption tests conducted prior to the multiple regression analysis. These tests include the normality test (Kolmogorov–Smirnov), multicollinearity test (Variance Inflation Factor), and heteroscedasticity test (White test), which are essential to ensure the reliability and robustness of the regression model.

The normality test, using the Kolmogorov–Smirnov method, yielded a significance value of 0.000, which is below the threshold of 0.05, indicating that the residuals are not normally distributed. Given the relatively large sample size (n = 660), this result is not unusual in firm-level financial data, where PBV, profitability, and leverage often exhibit skewness and extreme observations. The multicollinearity test, measured by the Variance Inflation Factor (VIF), showed a range of values between 1.014 and 1.280, which is well below the threshold of 10. This result confirms that multicollinearity is not a serious concern in this model. The heteroscedasticity test, based on the White test, also reported a significance value of 0.000, indicating that the variance of the residuals was not constant across observations. Consequently, robust standard errors were applied to improve the reliability of the significance testing. This choice is appropriate because robust estimation adjusts the standard errors in the presence of heteroscedasticity, while the coefficient estimates remain directly comparable to the baseline regression model.

The results of the multiple regression analysis are presented in

Table 6. Multiple regression analysis.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	10.621	2.114	5.023	0.000 ***
X1	1.344	0.620	2.169	0.031 **
X2	−0.522	0.115	−4.549	0.000 ***
X3	16.692	5.213	3.202	0.001 ***
X4	0.571	0.130	4.394	0.000 ***
Adjusted R-squared				0.388759
F-statistic				105.1478
Prob (F-statistic)				0.000 ***

Notes: ***: significant at α = 1%. **: significant at α = 5%.

.

The model exhibits moderate explanatory power, with an adjusted R-squared of 0.3888, indicating that approximately 39% of the variation in firm value is explained by the independent variables included in the model. Furthermore, the F-statistic value of 105.15 with a significance level of 0.000 confirms that the regression model is statistically significant as a whole, meaning that the independent variables are jointly associated with firm value.

Looking at the individual variables, X1 (SDG Disclosures) has a positive and significant coefficient of 1.344 with a p-value of 0.031, indicating that greater disclosure of SDG-related information is associated with higher firm value. This finding is consistent with the research hypothesis, suggesting that firms with broader SDG-related disclosures tend to have higher PBV, possibly because investors associate sustainability transparency with legitimacy, risk awareness, and long-term orientation.

X2 (Company Size), represented by the natural logarithm of total assets, shows a negative and highly significant coefficient of −0.522 (p = 0.000). Although much of the mainstream literature often associates larger firm size with stronger stability and market recognition, the negative coefficient in this study suggests that larger firms in the sample may be traded at lower PBV multiples. This may reflect slower growth expectations for mature or asset-heavy firms, especially in emerging markets, where investors may assign higher valuation multiples to smaller firms with stronger perceived growth prospects. This interpretation is consistent with a possible small-cap or growth-opportunity effect rather than evidence that size is inherently value-reducing (Ishak & Selamat, 2024). X3 (profitability) has a strong positive effect on firm value, with a coefficient of 16.692 and a p-value of 0.001, suggesting that more profitable firms are valued more highly by the market. X4 (Leverage) is also positively associated with firm value, with a coefficient of 0.571 and a p-value of 0.000. This may indicate that, for some firms, debt is perceived as supporting expansion or tax shield benefits (Odhiambo et al., 2025). However, this result must be interpreted cautiously in emerging markets, where high leverage can signal financial fragility, refinancing risk, and vulnerability to interest-rate or currency shocks (Kalash, 2021). Therefore, the positive leverage coefficient should not be read as a general recommendation for higher debt but as an empirical pattern within the specific sample and period examined (Appiah et al., 2020).

The regression results support the hypothesis of a positive association between SDG disclosure (X1) and firm value. The control variables (X2, X3, and X4) are also statistically significant and provide meaningful insights into the financial and structural characteristics associated with corporate valuation in Southeast Asia. Nevertheless, the results should be interpreted as evidence of market associations rather than definitive causal effects.

4.1.4. Robustness Test

This section presents the robustness tests conducted to evaluate the consistency of the main regression results.

Table 7. Winsorised regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	5.508	0.705	7.814	0.000 ***
X1_W	0.662	0.186	3.564	0.000 ***
X2_W	−0.249	0.037	−6.764	0.000 ***
X3_W	15.124	1.317	11.488	0.000 ***
X4_W	0.274	0.040	6.813	0.000 ***
Adjusted R-squared				0.270626
F-statistic				61.75671
Prob (F-statistic)				0.000 ***

Notes: ***: significant at α = 1%.

presents the results of the winsorised regression, in which all continuous variables were winsorised at the 5th and 95th percentiles to reduce the influence of outliers and assess whether the main findings remain robust after limiting the effect of extreme observations.

SDG disclosure (X1_W) continues to show a positive and statistically significant coefficient (coefficient = 0.662, p = 0.000), indicating that the positive relationship between SDG disclosure and firm value is not driven by the outliers. This suggests that the main finding remains stable even after limiting the effect of extreme values in the dataset. The control variables also show results consistent with the main regression. Company size (X2_W) remains negatively and significantly associated with firm value, whereas profitability (X3_W) and leverage (X4_W) continue to exhibit positive and statistically significant effects. The consistency in both the direction and significance of these coefficients indicates that the model is robust to alternative treatments of the data. Although the adjusted R-squared of the winsorised model is lower than that of the baseline model, the regression remains statistically significant overall, as reflected by the F-statistic and its p-value.

Table 8. Separate regression analysis for 2022 and 2023.

Variable	2022				2023
	Coefficient	Std. Error	t-Statistic	Prob.	Coefficient	Std. Error	t-Statistic	Prob.
C	6.97	2.081	3.35	0.001 ***	11.866	1.995	5.947	0.000 ***
X1	1.408	0.844	1.668	0.096 *	1.555	0.475	3.275	0.001 ***
X2	−0.26	0.116	−2.233	0.026 **	−0.578	0.098	−5.896	0.000 ***
X3	0.261	0.429	0.609	0.543	8.787	4.26	2.063	0.040 **
X4	0.035	0.031	1.102	0.271	0.578	0.096	6.052	0.000 ***
Adjusted R-squared	0.008742				0.597643
F-statistic	2.452882				245.7126
Prob (F-statistic)	0.044789 **				0.000 ***

Notes: ***: significant at α = 1%. **: significant at α = 5%. *: significant at α = 10%.

presents the results of separate regression analyses for 2022 and 2023, conducted as an additional robustness test to examine whether the main findings remain consistent across each year of observation and are not driven solely by the use of average data.

The results provide further support for the main findings, particularly with respect to the direction of relationships. In both years, SDG disclosure (X1) remained positively associated with firm value, company size (X2) remained negatively associated, and profitability (X3) and leverage (X4) retained positive coefficients. This consistency in the coefficient direction suggests that the baseline results are not solely driven by the use of average data. Simultaneously, the strength of the relationships differed substantially across the two years. In 2022, SDG disclosure is positive and only weakly significant at the 10% level, whereas profitability and leverage are not statistically significant. The explanatory power of the 2022 model is also relatively low, as reflected in the adjusted R-squared of 0.008742, although the model remains marginally significant. In contrast, the 2023 regression shows much stronger results. SDG disclosure is positive and significant at the 1% level, and all control variables are statistically significant in the expected directions. The adjusted R-squared in 2023 is substantially higher at 0.597643, indicating a much stronger explanatory power. However, this difference should be interpreted cautiously. The variable definitions and data sources are consistent across both years, and the same Refinitiv-based operationalisation is used. Therefore, this sharp difference is unlikely to reflect a change in the measurement definition. More plausibly, it may reflect differences in market conditions, valuation sensitivity, reporting maturity, or the distribution of PBV and financial variables between the two periods. Because this study does not directly measure investor sentiment, post-pandemic recovery, or data quality changes, it avoids making a definitive causal claim about the reason for the difference and instead treats the year-by-year results as evidence that the disclosure-value relationship may vary over time.

Table 9. Regression with country and industry dummy variables.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	9.819	3.319	2.959	0.003 ***
X1	1.195	0.635	1.882	0.060 *
X2	−0.477	0.149	−3.207	0.001 ***
X3	15.539	4.777	3.253	0.001 ***
X4	0.573	0.129	4.44	0.000 ***
Malaysia (country dummy)	−1.134	0.708	−1.603	0.109
Singapore (country dummy)	−0.807	0.607	−1.328	0.185
Thailand (country dummy)	−1.059	0.612	−1.732	0.084 *
D_IND2	0.768	0.885	0.868	0.386
D_IND3	0.579	0.737	0.786	0.432
D_IND4	1.437	1.172	1.226	0.221
D_IND5	2.107	0.943	2.234	0.026 **
D_IND6	1.76	1.052	1.674	0.095 *
D_IND7	1.198	0.902	1.328	0.185
D_IND8	0.612	0.677	0.904	0.366
D_IND9	0.451	0.801	0.563	0.574
D_IND10	0.419	0.843	0.497	0.619
D_IND11	1.315	0.893	1.473	0.141
Adjusted R-squared	0.397029
F-statistic	26.36988
Prob (F-statistic)	0.000 ***

Notes: ***: significant at α = 1%. **: significant at α = 5%. *: significant at α = 10%.

presents the regression results after including country and industry dummy variables for the robustness test. The country dummy variables are explicitly labelled as Malaysia, Singapore, and Thailand, with Indonesia as the reference category. The industry dummy variables are coded according to the GICS sectors, with the Financials sector used as the reference category. This specification examines whether the main findings remain consistent after controlling for structural differences across countries and industries.

The results provide additional support for these main findings. SDG disclosure (X1) remains positively associated with firm value, with a coefficient of 1.195 and remains statistically significant at the 10% level. Although the level of significance becomes weaker compared to the baseline model, the positive sign is maintained, indicating that the relationship between SDG disclosure and firm value persists even after controlling for structural differences between countries and industries. This specification is also useful for a sensitivity analysis of the unequal country composition of the sample, particularly the larger number of Malaysian firms. By including country and industry dummy variables, the model reduces the risk that the main result is solely driven by one country or sector. Nevertheless, the weaker significance level indicates that country and industry contexts matter and that cross-country comparability should be interpreted carefully. The control variables remained consistent with the baseline regression. Company size (X2) continues to show a negative and significant effect on firm value, whereas profitability (X3) and leverage (X4) remain positive and statistically significant.

Table 10. The 2SLS endogeneity robustness test.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	14.143	2.125	6.657	0.000 ***
X1	1.914	0.646	2.961	0.003 ***
X2	−0.663	0.100	−6.631	0.000 ***
X3	8.713	1.481	5.882	0.000 ***
X4	0.575	0.019	29.850	0.000 ***
Industry	0.054	0.054	1.013	0.311
Malaysia (country dummy)	−1.200	0.531	−2.260	0.024 **
Singapore (country dummy)	−0.428	0.664	−0.645	0.519
Thailand (country dummy)	−1.181	0.550	−2.147	0.032 **
Adjusted R-squared	0.599424
F-statistic	124.7715
Prob (F-statistic)	0.000 ***
First-stage Wald F-statistic	974.4855
Prob (Wald F-statistic)	0.000 ***
Prob (J-statistic)	0.137704

Notes: ***: significant at α = 1%. **: significant at α = 5%.

presents the results of the two-stage least squares (2SLS) estimation conducted as an additional robustness test to address the potential endogeneity between SDG disclosure and firm value. Endogeneity may arise because the relationship between SDG disclosure and firm value may be bidirectional. While SDG disclosure may influence investors’ assessments of firm value, firms with higher firm values may also have stronger resources, better reporting systems, and greater stakeholder visibility, which may enable broader SDG disclosure. Therefore, SDG disclosure (X1) is treated as a potentially endogenous variable in this study. The 2SLS estimation uses lagged SDG disclosure and peer SDG disclosure at the country-year level as the instrumental variables. Lagged SDG disclosure is used because disclosure practices tend to be persistent over time, while peer SDG disclosure captures reporting pressure and disclosure norms among firms operating in the same country-year context. Because lagged SDG disclosure is only available for the 2023 observations, the 2SLS estimation is conducted using the 2023 subsample only.

The results provide additional support for the robustness of the main findings after considering the potential endogeneity. In the second-stage regression, SDG disclosure (X1) remains positively and statistically significantly associated with firm value, with a coefficient of 1.914 and a p-value of 0.003. This indicates that the positive relationship between SDG disclosure and firm value remains evident after X1 is instrumented using lagged SDG and peer SDG disclosures at the country-year level. The first-stage Wald F-statistic of 974.4855 indicates that the instruments are jointly strong predictors of X1, suggesting that the instrument relevance condition is satisfied. In addition, the probability of the J-statistic is 0.137704, indicating that the over-identifying restrictions are not rejected. This provides no statistical evidence of the validity of the instruments. The control variables also remained broadly consistent with previous robustness tests. Company size (X2) is negatively and significantly associated with firm value, while profitability (X3) and leverage (X4) are positively and significantly associated with firm value. The Malaysia and Thailand country dummies are negative and significant, suggesting that firms in these countries have lower firm values relative to the reference country in this specification, while the Singapore dummy is not statistically significant. Overall, the 2SLS result strengthens the conclusion that SDG disclosure is positively associated with firm value, while still requiring cautious interpretation due to the short observation period and the use of a 2023-only instrumental variable specification.

Overall, the robustness tests indicate that the main findings remain stable under alternative specifications and data treatment. The positive association between SDG disclosure and firm value remains consistent after controlling for outliers, examining each year separately, accounting for country and industry heterogeneity, and conducting an additional endogeneity check using the 2SLS method. Although the level of significance varies across the models, the direction of the relationship remains unchanged. This suggests that the baseline conclusion is sufficiently robust, while still requiring cautious interpretation as evidence of association rather than definitive causality.

4.2. Discussion

The findings of this study provide empirical support for the positive relationship between Sustainable Development Goal (SDG) disclosure and firm value. This result supports the central hypothesis that increased transparency and integration of SDG-related information are associated with firms’ perceptions in capital markets. The strategic relevance of sustainability disclosures is increasingly recognised, particularly as investors seek long-term value and resilience in the post-pandemic economic environment (Janik & Ryszko, 2023). This positive association suggests that firms that communicate their commitment to global sustainability goals may enhance stakeholder trust, improve their market reputation, and obtain higher valuations (Ahmad et al., 2023). However, the results should be interpreted carefully: the study demonstrates an association rather than definitive causation, and the market value effect may depend on disclosure credibility, investor awareness, the regulatory environment, and firm-specific fundamentals.

Theoretically, these findings strengthen the relevance of legitimacy, signalling, and institutional perspectives in explaining SDG disclosures. From a legitimacy perspective, firms may disclose SDG information to demonstrate that their activities align with societal expectations. From a signalling perspective, SDG disclosure may reduce information asymmetry by communicating sustainability orientation and risk awareness to investors and stakeholders. From an institutional perspective, cross-country differences indicate that disclosures are shaped by regulatory norms, investor expectations, and peer practices. The contribution of this study lies in showing that these theoretical mechanisms are observable in a Southeast Asian setting, where sustainability reporting practices are developing unevenly across markets and sectors in Malaysia.

However, the analysis also reveals that SDG disclosures remain limited and selective. Most companies tend to focus on a narrow subset of goals—especially SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action)—while reporting on others, such as SDG 14 (Life Below Water) and SDG 2 (Zero Hunger), remains minimal. This pattern suggests that disclosure is often guided by perceived materiality, sector relevance, or reputational value rather than a comprehensive sustainability commitment (Diaz-Sarachaga, 2021). This raises important questions regarding the authenticity and depth of corporate engagement with the full SDG agenda. Companies may prioritise reporting goals that align closely with their core business operations, are easier to quantify, or are more visible to investors and regulators (Izzo et al., 2020).

ANOVA further highlights the statistically significant differences in SDG disclosure across both countries and industry sectors. Firms in Indonesia and Thailand demonstrate relatively higher disclosure levels than their counterparts in Malaysia and Singapore. This variation may be driven by differing regulatory landscapes, public expectations, or ESG maturity in each jurisdiction (Sadiq et al., 2022; Amornkitvikai & Pholphirul, 2023). At the industry level, the Utilities, Energy, and Health Care sectors appear to be more proactive in disclosing SDG-related information, possibly because of their higher exposure to environmental and social risks (Thammaraksa et al., 2024). In contrast, sectors such as Information Technology and Consumer Discretionary exhibit lower levels of disclosure, which may reflect a disconnect between operational models and direct alignment with SDG themes (Hamad et al., 2022).

These findings have important implications for regulators, investors, and managers. From a policy perspective, the uneven distribution and selectivity of disclosures underscore the need for stronger regulatory guidance and possibly more mandatory or harmonized frameworks to ensure balanced and comparable SDG reporting across firms and industries (Arena et al., 2022). In Indonesia and Thailand, where the average SDG disclosure is relatively higher, regulators may focus on improving disclosure quality, assurance, and consistency across all 17 goals. For Malaysia and Singapore, where the sample shows relatively lower SDG disclosure breadth, policy attention should focus on encouraging more explicit SDG mapping and improving the comparability between sustainability reports and global SDG targets. For investors, the observed trends may serve as a signal of corporate integrity and long-term strategic alignment with sustainability objectives (Su et al., 2024). However, investors should not rely solely on the presence of SDG disclosures; they should also evaluate the depth, credibility, target specificity, and performance evidence behind the disclosures.

For managers, the results imply that SDG disclosure should not be treated merely as a compliance exercise or a symbolic communication tool. Companies can strengthen the market relevance of SDG reporting by linking SDG narratives to measurable targets, governance responsibilities, resource allocation, and performance outcomes (Rezaee et al., 2023). This is especially important because the binary disclosure measure used in this study captures whether a goal is disclosed but not whether the disclosure is detailed, credible, or strategically embedded. Therefore, managers should move beyond mentioning SDGs and provide clearer explanations of how SDG priorities are connected to business strategy, risk management, investment decisions, and long-term value creation.

5. Conclusions

This study investigates the association between Sustainable Development Goal (SDG) disclosure and firm value among publicly listed companies in Southeast Asia, focusing on Indonesia, Malaysia, Singapore, and Thailand. Using structured SDG data from the Refinitiv Eikon database and employing a two-year average for 2022–2023, the findings generally support a positive association between SDG disclosure and firm value. The evidence suggests that firms that communicate their contributions to the SDGs more visibly tend to be valued more favourably by the market. However, the strength of this relationship varies across model specifications and years, indicating that the evidence is directional and preliminary, rather than conclusive. Accordingly, this study interprets SDG disclosure as a potentially value-relevant signal of sustainability transparency, not as definitive proof that disclosure alone causes higher firm value.

The study also reveals that SDG disclosures remain uneven and selective, with companies concentrating primarily on specific goals, such as SDG 8 (Decent Work and Economic Growth) and SDG 13 (Climate Action), while others, such as SDG 2 (Zero Hunger) and SDG 14 (Life Below Water), are seldom reported. Significant disparities in disclosure levels across countries and industries suggest that both regulatory pressure and industry characteristics shape sustainability reporting behaviour. These findings underscore the need for harmonised sustainability standards, broader awareness of all 17 goals, and stronger alignment between national policies and corporate reporting practices. At the managerial level, the findings suggest that companies should improve not only the breadth of SDG disclosure but also the quality, specificity, and credibility of the reported information.

However, this study had some limitations. First, the measurement of SDG disclosure is based on a binary count system, scoring whether an SDG is disclosed, without assessing the quality, depth, credibility, or strategic relevance of the disclosures. As a result, firms that provide only minimal or symbolic disclosures may be treated equally to those that disclose more comprehensively. Second, the analysis relies on secondary data from the Refinitiv Eikon database, which, while standardised and comparable, may not capture all voluntary disclosure practices outside the standardised reporting frameworks. Third, the sample is unevenly distributed across countries, with Malaysia contributing more observations than Indonesia, Singapore, and Thailand do. Although country and industry dummy variable robustness tests help address this concern, the imbalance may still affect cross-country comparability. Fourth, by averaging data over two years, this study mitigates short-term outlier effects but may also obscure year-to-year dynamics and changes in disclosure behaviour. Fifth, the study is limited by its short two-year observation period (2022–2023), which may not capture long-term relationships. The large difference in explanatory power between 2022 and 2023 further suggests that the relationship between SDG disclosure and firm value may vary according to market conditions. Finally, while the study establishes an association, it does not fully address causal mechanisms, reverse causality, or endogeneity between disclosure and firm value.

Given these limitations, future research should incorporate content analysis, disclosure scoring rubrics, or assurance-based indicators that evaluate the depth, credibility, and strategic relevance of SDG disclosures. Future studies should also use longer time periods and more advanced econometric tools, such as panel data models, generalised least squares, instrumental variable approaches, or dynamic panel estimators, to better address causality and endogeneity. Future research should integrate variables such as corporate governance, ownership structure, investor type, market regulation, and sustainability assurance to provide more nuanced insights into the drivers of SDG disclosure quality and its impact on firm performance. Comparative studies across a broader set of emerging and developed markets would further enrich the global discourse on sustainable reporting practices and help determine whether the positive disclosure–value relationship observed in this study is stable across institutional contexts.