This study examines the relationship between sustainability reporting, guided by Global Reporting Initiative standards, and dividend policy among Vietnamese listed firms.
Multinomial logistic regression is employed to analyse how disclosures aligned with the Sustainable Development Goals (SDGs) influence dividend policy in the 100 largest firms listed on the Hanoi and Ho Chi Minh stock exchanges between 2021 and 2023. The study investigates whether higher levels of SDG disclosure affect both the form and magnitude of dividend payouts.
The results reveal that firms with more extensive SDG disclosures are significantly more likely to pay dividends, either in cash or shares. Notably, higher disclosure levels are positively associated with dividend payouts exceeding 50% and negatively associated with the decision to omit dividends altogether.
The study contributes to signalling theory by highlighting the strategic role of SDG disclosures in communicating corporate stability and governance quality through dividend policy.
The findings underscore the relevance of sustainability disclosures in shaping corporate dividend strategies, especially during periods of financial uncertainty, offering practical guidance to managers on improving reporting practices.
Given Vietnam's vulnerability to climate-related risks, robust sustainability reporting is essential for maintaining investor trust and supporting broader economic resilience.
This is the first study to provide a comprehensive assessment of how GRI-based sustainability reporting influences dividend policy in Vietnam. It offers novel insights into how transparency in sustainability practices informs financial decision-making in emerging markets.
1. Introduction
In September 2024, Typhoon Yagi severely impacted in Northern Vietnam, causing estimated losses of VND 40 trillion (approximately US$1.63 billion). In response, the Vietnamese government lowered its 2024 GDP growth forecast by 0.15% points and prioritised initiatives to enhance economic resilience and facilitate sectoral recovery (Ministry of Industry and Trade, 2024; Vietnam Briefing, 2024). Sustainability is increasingly recognised as essential for long-term economic stability, with sustainable finance gaining prominence in international discourse and research (HSBC, 2022; Benameur et al., 2025).
Abdelbaky et al. (2024) underscore the importance of ethical financial practices and innovation in strengthening corporate ESG performance. Companies now integrate sustainability into operational and stakeholder engagement strategies to align with the SDGs (Van Marrewijk, 2003). Beyond social responsibility, sustainability initiatives serve as strategic tools for enhancing reputation (Du et al., 2010) and are empirically linked to improved customer attraction, sales performance, and market valuation (Luo and Bhattacharya, 2006).
Dividend policy, how firms allocate after-tax profits between reinvestment and shareholder payouts, remains a central issue in corporate finance (Brigham and Houston, 2019). Investors typically prefer cash dividends due to their certainty. While Miller and Modigliani's (1961) theory posits dividend irrelevance under ideal conditions, agency theory (Jensen and Meckling, 1976) argues that dividends help resolve manager-shareholder conflicts by limiting discretionary funds (Yu, 2019). Thus, dividend policies serve to balance capital needs and shareholder expectations (Barros et al., 2020, 2021, 2023).
Although research on the nexus between sustainability disclosures and dividend policy is limited, foundational studies by Naz et al. (2017), and Imamah et al. (2019), provide early insights. Anwer et al. (2021) document cash dividend preferences among MNCs shaped by governance and capital structures. Their work builds on Fama and French (2001) “disappearing dividend” hypothesis and La Porta et al.’s (2000) dividend policy framework. Non-financial disclosures enhance transparency, reduce information asymmetry, and strengthen reputation – factors influencing capital costs and dividend policies (Saeed and Zamir, 2021; Hoang and Nguyen, 2024; Nguyen and Duong, 2025a). Almulhim et al. (2024) link strong ESG practices to sustained dividend payouts, while Athari (2022) finds raise dividends to manage litigation risk, improve disclosure, and enforce accountability.
Vietnam has made considerable progress in advancing sustainable development through Decision No. 622/QD-TTg and Circular No. 96/2020/TT-BTC, which support the 2030 Agenda (Prime Minister, 2017; Ministry of Finance, 2020). However, how Vietnamese firms understand and act on sustainability, particularly regarding dividend policies, remains unclear. This study addresses a critical gap in Vietnam's developing market by empirically assessing the relationship between SDG-related disclosures and dividend policy—an underexplored intersection despite increasing academic and regulatory attention.
Vietnam offers a unique empirical setting due to its dynamic corporate governance reforms, rising investor scrutiny, and growing alignment with global sustainability frameworks. Its institutional structures, though progressing, remain less developed than in mature economies. This creates a context where firms must navigate tensions between sustainability objectives and shareholder demands. The country's integration into global capital markets and recent climate and pandemic shocks further underscore the relevance of exploring how sustainability disclosures shape financial policies in emerging markets.
From a theoretical standpoint, this relationship merits deeper analysis. As sustainability becomes central to both corporate strategy and regulation, important questions arise around how firms adjust dividend policies under competing resource demands (Dahiya et al., 2023; Hoang and Nguyen, 2024). This study is particularly relevant to developing countries, where achieving the SDGs poses unique challenges (United Nations, 2015).
2. Literature review and hypothesis development
2.1 Theoretical background
2.1.1 Dividend policy theories
Dividend policy, which determines how after-tax profits are distributed between reinvestment and shareholder payouts, remains a core issue in corporate finance. As noted by Brigham and Houston (2019), these decisions can influence firm value through their effect on share prices. Dividend policies can also serve as strategic signals to investors, particularly during periods of volatility (Al-Khasawneh et al., 2024). Miller and Modigliani (1961) proposed the dividend irrelevance theory, arguing that in a perfect market, dividend decisions do not affect firm value. In contrast, agency theory (Jensen and Meckling, 1976) highlights how dividends can mitigate conflicts of interest between managers and shareholders by limiting the discretionary use of cash. Building on this, DeAngelo et al. (2009) suggest that consistent dividend payments indicate financial health and reduce agency costs, while Frankfurter and Lane (1992) view dividend policy as a governance mechanism that promotes managerial accountability. Liu et al. (2025) further emphasize that governance features – such as ownership concentration and CEO duality – affect dividend decisions.
Recent research suggests that sustainability disclosures may also serve as complementary signals of financial strength and ethical governance. This study argues that firms with higher levels of sustainability reporting – especially when aligned with GRI standards – may enhance investor confidence through consistent dividend practices. This interplay between sustainability reporting and dividend behaviour is explored further under signalling theory.
2.1.2 The signalling hypothesis
Signalling theory explains why firms continue to pay dividends despite theoretical arguments against it. Ross (1977) introduced the information content hypothesis, proposing that dividend changes reflect managers' private insights about future performance. Under this theory, dividend announcements serve as signals, with adjustments conveying earnings expectations (Bhattacharya, 1979; Fama and French, 1997). Nonetheless, empirical findings remain inconclusive. Although early research (e.g. Marsh and Merton, 1987) supports this view, Benartzi et al. (1997) find no consistent link between dividend changes and future earnings.
Sustainability reporting has similarly been conceptualised as a non-financial signal of corporate quality. Firms committed to long-term value creation often employ both dividend payments and sustainability disclosures to reduce information asymmetry and build. These practices function as dual signalling mechanisms, particularly in emerging markets where investors rely on both financial and non-financial indicators.
Alternative theories offer additional insight. Litzenberger and Ramaswamy's (1979) free cash flow hypothesis suggests that dividends signal a lack of profitable reinvestment opportunities, while the maturity hypothesis posits that increasing dividends reflect a firm's transition to lower growth stages (Charitou et al., 2011). These views emphasise the role of information asymmetry in shaping how investors interpret dividend actions. Recent studies (e.g. Rouf and Siddique, 2023; Nguyen and Duong, 2025a, b) apply multiple theoretical lenses – signalling, agency, legitimacy, and contract theory – to explore corporate disclosure.
Firm-specific characteristics also affect dividend policy. Larger firms (SIZE) generally have stronger reputations and better access to capital, leading to higher dividend payouts (Smith et al., 2017; Anwer et al., 2021; El-Helaly and Al-Dah, 2022). Highly leveraged firms (LEV) often face constraints due to debt obligations, limiting dividend distribution (Imamah et al., 2019; Saeed and Zamir, 2021). Profitability (ROA, ROE) increases a firm's ability to pay dividends (Imamah et al., 2019; Anwer et al., 2021; Saeed and Zamir, 2021). Older firms (AGE) with well-established reputations tend to issue dividends more consistently (Sun and Yu, 2022), while market valuation (Tobin's Q) is positively associated with dividend policy (Anwer et al., 2021; Sun and Yu, 2022). From a signalling perspective, board gender diversity (BGD) may also reduce agency conflicts, fostering more stable dividend practices.
2.2 SDG-related disclosures
Sustainability reporting has grown significantly, evolving from a “shortage” phase in 2000 to a “saturation” phase by 2022 (Benameur et al., 2024). Companies are increasingly incorporating Big Data into accounting systems to improve sustainability performance and generate long-term stakeholder value (Azzam et al., 2024). Nevertheless, concerns about the credibility and depth of ESG reporting remain. Schwoy et al. (2025) point out that many firms focus on high-profile incidents, overlooking broader sustainability concerns. When sustainability disclosure is used more for image management than for genuine commitment, it risks undermining trust (Agnese et al., 2025).
Among the available frameworks, the Global Reporting Initiative (GRI) is the most widely adopted, especially in the Americas (KPMG, 2022). Launched in 2000 and revised into GRI Standards in 2016, the framework enhances transparency and comparability in sustainability reporting (GRI, 2016).
2.3 The link between SDG-related disclosure and dividend policy
Theoretical perspectives suggest that SDG-related disclosures can affect dividend policy through two main mechanisms: reducing financing constraints and enhancing financial performance (Zahid et al., 2023).
First, sustainability disclosures reduce information asymmetry between managers and investors, improving financing access and lowering capital costs. Transparent ESG disclosures reduce perceived risk, reassure investors, and increase firms' capacity to pay dividends (Dhaliwal et al., 2011). In contrast, vague or selective disclosures can erode confidence and reduce dividend payouts (Cheung et al., 2018). From a signalling theory standpoint, strong ESG disclosures project firms' capability to manage environmental and social risks, reinforcing stable dividend policies.
Second, SDG disclosures can support earnings growth by strengthening brand reputation, customer loyalty, and employee engagement (Cheung et al., 2018; Zahid et al., 2023). These benefits can lead to improved financial results and support more consistent dividend distributions.
Empirical research offers mixed findings. Cheung et al. (2018), studying U.S. firms from 1991 to 2010, found that firms with higher CSR ratings had higher dividend payout ratios, though CSR alone did not dictate dividend policy. Adhikari and Agrawal (2018) observed that payout decisions are influenced by industry peers, with younger firms mimicking peer practices and mature firms favouring share repurchases. Saeed and Zamir (2021) found that in some economies, CSR disclosures negatively correlate with dividends as firms prioritise long-term sustainability over short-term payouts. In contrast, Barros et al. (2023) reported a positive association between ESG transparency and dividend levels, suggesting such disclosures help maintain investor confidence. Recently, Hoang and Nguyen (2024) studied COVID-19-related disclosures and found that detailed reporting was positively associated with both cash and stock dividends, though not with mixed payout structures.
Despite this growing literature, no prior study has specifically explored how SDG disclosures influence dividend decisions in Vietnam. Given the country's unique financial landscape, this research aims to fill that gap. Drawing on signalling theory and empirical evidence, we hypothesise:
Higher levels of SDG-related disclosure are positively associated with the likelihood of dividend payments.
3. Research methodology
3.1 Sample and data description
This study examines the 100 largest Vietnamese-listed firms by market capitalisation as of 31 December 2023. These firms account for over 90% of total market capitalisation, making the sample highly representative of the broader market. Sustainability disclosure data were collected based on the GRI (2016) framework, using publicly available reports on annual, financial, ESG, and SDG disclosures for the period 2021–2023. Financial and dividend data were sourced from Vietstock (www.vietstock.vn), a widely used financial database in Vietnam. Appendix 2 presents the average SDG disclosure score by sector over the three-year period.
3.2 Research model and variable measurement
3.2.1 Research model
To assess the impact of SDG disclosures on dividend policy, the study employs multinomial logistic regression. This method is suitable due to the categorical nature of the dependent variable and the panel structure of the data (Liu, 2015). The dependent variable, dividend payment intention (DIV), is categorised by payment method, year, and payout ratio. Given these categorical attributes, multinomial logistic regression is preferred over linear regression. The regression model is structured as follows:
The probability (Pj) of the j dividend event occurring is modelled as (Anwer et al., 2021):
where t .
Model fit is assessed using the −2 Log-Likelihood statistic (-2LL). A lower -2LL values indicates better model performance. Comparison of an intercept-only model and a full model (with independent variables) helps determine goodness of fit. The Pseudo R2 statistic, analogous to R2 used in linear regression. Pseudo R2 in linear regression, measures the explanatory power of the model.
3.2.2 Dependent variable
The dependent variable reflects firms' dividend payout trends and includes four proxies: payment method (DIVmethod), payout below 0% (DIV < 0), payout above 50% (DIV>50), and dividend payment occurrence (DIVyear). These are operationalised as:
DIV (DIVmethod) = ;
DIV (DIV0: payout <0%) = ;
DIV (DIV50: payout >50%) = ;
This approach is consistent with Barros et al. (2023).
DIV (DIVyear) =
These measures follow prior studies (e.g. Smith et al., 2017; Anwer et al., 2021; Sun and Yu, 2022; Barros et al., 2023; Fonseka and Richardson, 2023; Hoang and Nguyen, 2024).
3.2.3 Independent variable
SDG-related disclosure is measured using a dichotomous scoring approach based on a 77-item checklist aligned with the GRI (2016) standards (see Appendix 1). Each indicator is scored “1” if disclosed and “0” otherwise. To ensure validity and reliability, the checklist and scoring methodology adopt procedures consistent with those used by Nguyen (2023).
The SDG score is computed as the average number of disclosed indicators, using the following formula:
This method has been widely used in previous studies, including Saeed and Zamir (2021), Ellili (2022), Barros et al. (2023), Nguyen (2023), Alshahrani et al. (2024), Bhatia and Kaur (2024), and Hoang and Nguyen (2024).
To enhance the validity of the measurement, independent reviewers assessed 30 sustainability reports from ten companies over three years, confirming consistency with the GRI framework. Following the triangulation strategy described by Nguyen and Duong (2025b), this process supports the reliability of the dataset. The 77 items comprehensively reflect all 17 Sustainable Development Goals. Specifically, the environmental dimension comprises 30 indicators, the social dimension 34 indicators, and the economic dimension 13 indicators. These components align with key sustainability priorities and stakeholder expectations within the Vietnamese context.
Additionally, seven control variables (CONTROLi,t) are included to account for their potential influence on the model, as outlined in Table 1. These variables are theoretically grounded in the dividend policy literature and incorporated to enhance the robustness of the empirical analysis.
Control variables
| Variable | Definition and measurement | Theoretical framework | Supporting references |
|---|---|---|---|
| SIZE | Natural log of total assets at the balance date | Agency theory | |
| LEV | Total liabilities divided by total assets at the balance date | Contract theory | |
| TobinQ | Market Value of Assets/Book Value of Assets | Contract theory | |
| ROA | Return on total assets at the balance date | Signalling theory | |
| ROE | Return on total equity at the balance date | Signalling theory | |
| AGE | Years of the establishment of the enterprise | Legitimacy theory | |
| BGD | Board gender diversity, measured as the percentage of female directors across the board | Agency theory |
4. Results and comments
4.1 Descriptive statistics
Table 2 presents descriptive statistics, highlighting significant variation in SDG-related disclosures, which range from 2.6% to 57.1% across the sampled firms. Among the independent variables, Return on Assets (ROA) has a variance inflation factor (VIF) exceeding 6, indicating potential multicollinearity.
Descriptive statistics
| Panel A. Descriptive statistics for dividend policy (categorical variable) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | Obs | Mean | Std dev | Skewness | Kurtosis | Frequency | |||
| “0” | “1” | “2” | “3” | ||||||
| DivMethod | 300 | 1.063 | 0.932 | 0.647 | (0.376) | 90 | 132 | 47 | 31 |
| Div0 | 300 | 0.300 | 0.459 | 0.877 | (1.239) | 210 | 90 | ||
| Div50 | 300 | 0.047 | 0.211 | 4.320 | 16.776 | 286 | 14 | ||
| DivYear | 300 | 0.030 | 0.171 | 5.538 | 28.863 | 291 | 9 | ||
| Panel A. Descriptive statistics for dividend policy (categorical variable) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | Obs | Mean | Std dev | Skewness | Kurtosis | Frequency | |||
| “0” | “1” | “2” | “3” | ||||||
| DivMethod | 300 | 1.063 | 0.932 | 0.647 | (0.376) | 90 | 132 | 47 | 31 |
| Div0 | 300 | 0.300 | 0.459 | 0.877 | (1.239) | 210 | 90 | ||
| Div50 | 300 | 0.047 | 0.211 | 4.320 | 16.776 | 286 | 14 | ||
| DivYear | 300 | 0.030 | 0.171 | 5.538 | 28.863 | 291 | 9 | ||
| Panel B Descriptive statistics for continuous variables | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | Obs | Mean | Std dev | Min | Max | Skewness | Kurtosis | VIF | 1/VIF |
| SDG | 300 | 0.169 | 0.102 | 0.026 | 0.571 | 1.354 | 5.111 | 1.123 | 0.891 |
| SIZE | 300 | 17.673 | 1.479 | 15.304 | 21.557 | 0.639 | 2.600 | 2.276 | 0.439 |
| LEV | 300 | 0.590 | 0.255 | 0.052 | 0.998 | (0.132) | 1.895 | 4.621 | 0.216 |
| TobinQ | 300 | 0.829 | 0.940 | 0.011 | 10.840 | 4.651 | 44.772 | 1.600 | 0.625 |
| ROA | 300 | 0.063 | 0.081 | (0.224) | 0.476 | 0.945 | 6.564 | 6.072 | 0.165 |
| ROE | 300 | 0.128 | 0.117 | (0.825) | 0.557 | (1.907) | 16.190 | 3.523 | 0.284 |
| AGE | 300 | 28.600 | 16.636 | 9.000 | 133 | 2.834 | 16.426 | 1.038 | 0.963 |
| BGD | 300 | 20.627 | 18.719 | 0 | 80 | 0.588 | (0.343) | 1.116 | 0.896 |
| Panel B Descriptive statistics for continuous variables | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | Obs | Mean | Std dev | Min | Max | Skewness | Kurtosis | VIF | 1/VIF |
| SDG | 300 | 0.169 | 0.102 | 0.026 | 0.571 | 1.354 | 5.111 | 1.123 | 0.891 |
| SIZE | 300 | 17.673 | 1.479 | 15.304 | 21.557 | 0.639 | 2.600 | 2.276 | 0.439 |
| LEV | 300 | 0.590 | 0.255 | 0.052 | 0.998 | (0.132) | 1.895 | 4.621 | 0.216 |
| TobinQ | 300 | 0.829 | 0.940 | 0.011 | 10.840 | 4.651 | 44.772 | 1.600 | 0.625 |
| ROA | 300 | 0.063 | 0.081 | (0.224) | 0.476 | 0.945 | 6.564 | 6.072 | 0.165 |
| ROE | 300 | 0.128 | 0.117 | (0.825) | 0.557 | (1.907) | 16.190 | 3.523 | 0.284 |
| AGE | 300 | 28.600 | 16.636 | 9.000 | 133 | 2.834 | 16.426 | 1.038 | 0.963 |
| BGD | 300 | 20.627 | 18.719 | 0 | 80 | 0.588 | (0.343) | 1.116 | 0.896 |
Note(s): Negative numbers are presented in parentheses
Regarding the dependent variable, the data demonstrate considerable diversity in dividend payment methods. Cash dividends are paid by approximately 44% of firms, whereas cases where dividend payouts exceed 50% constitute 4.67% of the sample. Advance dividend payments during the fiscal year account for 3% of observations.
Table 3 shows generally weak correlations among the independent variables; however, three pairs exhibit notable correlations: LEV and SIZE (r = 0.7), TobinQ and LEV (r = −0.564), and ROA and LEV (r = −0.631). Additionally, the SDG disclosure variable significantly correlates with DIV0 (r = −0.209) and DIV50 (r = 0.128). Although the VIF of ROA is relatively high, multicollinearity does not pose a substantive problem as it is used solely as a control variable, while the main variable of interest (SDG) has a low VIF (1.123). Moreover, the elevated VIF for ROA arises naturally from its calculation as a function of other financial ratios (ROE, LEV, and Equity/Liabilities). This type of induced multicollinearity does not bias the p-values or coefficient estimates significantly, and hence, it does not compromise the validity of the empirical findings.
Pearson correlation matrix
| Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) DivMethod | 1 | |||||||||||
| (2) DIV0 | (0.748)** | 1 | ||||||||||
| (3) DIV50 | 0.189** | (0.145)* | 1 | |||||||||
| (4) DIVYear | 0.072 | (0.115)* | (0.039) | 1 | ||||||||
| (5) SDG | 0.111 | (0.209)** | 0.128* | (0.007) | 1 | |||||||
| (6) SIZE | 0.136* | 0.139* | (0.065) | (0.089) | 0.121* | 1 | ||||||
| (7) LEV | 0.076 | 0.227** | (0.150)** | 0.091 | (0.110) | 0.700** | 1 | |||||
| (8) TobinQ | 0.028 | (0.087) | 0.130* | 0.135* | 0.149** | (0.388)** | (0.564)** | 1 | ||||
| (9) ROA | 0.167** | (0.337)** | 0.229** | 0.176** | 0.183** | (0.300)** | (0.631)** | 0.493** | 1 | |||
| (10) ROE | 0.325** | (0.320)** | 0.154** | 0.146* | 0.137* | 0.093 | (0.118)* | 0.168** | 0.721** | 1 | ||
| (11) AGE | 0.158** | (0.242)** | 0.029 | (0.066) | 0.112 | 0.043 | (0.037) | 0.036 | 0.072 | 0.065 | 1 | |
| (12) BGD | (0.063) | 0.151** | (0.100) | 0.045 | (0.033) | 0.084 | 0.178** | 0.042 | (0.010) | 0.029 | (0.134)* | 1 |
| Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) DivMethod | 1 | |||||||||||
| (2) DIV0 | (0.748)** | 1 | ||||||||||
| (3) DIV50 | 0.189** | (0.145)* | 1 | |||||||||
| (4) DIVYear | 0.072 | (0.115)* | (0.039) | 1 | ||||||||
| (5) SDG | 0.111 | (0.209)** | 0.128* | (0.007) | 1 | |||||||
| (6) SIZE | 0.136* | 0.139* | (0.065) | (0.089) | 0.121* | 1 | ||||||
| (7) LEV | 0.076 | 0.227** | (0.150)** | 0.091 | (0.110) | 0.700** | 1 | |||||
| (8) TobinQ | 0.028 | (0.087) | 0.130* | 0.135* | 0.149** | (0.388)** | (0.564)** | 1 | ||||
| (9) ROA | 0.167** | (0.337)** | 0.229** | 0.176** | 0.183** | (0.300)** | (0.631)** | 0.493** | 1 | |||
| (10) ROE | 0.325** | (0.320)** | 0.154** | 0.146* | 0.137* | 0.093 | (0.118)* | 0.168** | 0.721** | 1 | ||
| (11) AGE | 0.158** | (0.242)** | 0.029 | (0.066) | 0.112 | 0.043 | (0.037) | 0.036 | 0.072 | 0.065 | 1 | |
| (12) BGD | (0.063) | 0.151** | (0.100) | 0.045 | (0.033) | 0.084 | 0.178** | 0.042 | (0.010) | 0.029 | (0.134)* | 1 |
Note(s): ** and * show significance at the 0.01 and 0.05 levels, respectively (two-tailed). Negative numbers are presented in parentheses
4.2 Impact of SDG-related disclosures on dividend policy
The results in Table 4 indicate that SDG-related disclosures positively correlate with the likelihood of both share and cash dividend payments, with regression coefficients of β = 5.208 and β = 5.479, respectively. However, the relationship with combined payment methods is statistically insignificant. The findings also suggest that cash dividends are more likely than share dividends, aligning with Smith et al. (2017) and Hoang and Nguyen (2024).
Results of multinomial logit regression
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | 5.479*** | 5.208** | 0.203 | (4.708)*** | 4.283* | (0.425) |
| 6.911 | 5.917 | 0.005 | 6.998 | 3.169 | 0.012 | |
| SIZE | (0.839)*** | (0.034) | (0.431)* | 0.450*** | 0.058 | (0.308) |
| 17.534 | 0.026 | 2.912 | 8.146 | 0.035 | 0.636 | |
| LEV | 0.018 | 0.208 | 3.737 | (0.043) | (1.735) | (0.151) |
| 1x10−4 | 0.011 | 2.020 | 0.001 | 0.366 | 0.002 | |
| TobinQ | (0.425)* | (0.333) | (0.086) | 0.293 | 0.141 | 0.150 |
| 3.088 | 0.499 | 0.116 | 2.120 | 0.351 | 0.484 | |
| ROA | 19.593*** | (2.409) | 16.336* | (8.658) | 1.411 | 1.994 |
| 6.639 | 0.120 | 3.029 | 2.228 | 0.017 | 0.027 | |
| ROE | 1.702 | 7.330** | 12.384** | (6.771)** | 4.859 | 7.153 |
| 0.194 | 4.629 | 6.004 | 5.095 | 0.368 | 0.725 | |
| AGE | 0.078*** | 0.042** | 0.079*** | (0.070)*** | (0.006) | (0.046) |
| 16.884 | 3.864 | 13.319 | 15.706 | 0.099 | 1.923 | |
| BGD | (0.026)*** | 0.009 | (0.032)** | 0.014** | (0.037)* | 0.014 |
| 6.994 | 0.670 | 4.876 | 3.014 | 3.039 | 0.543 | |
| –2Log-likelihood (Intercept) | 748.423 | 366.519 | 113.149 | 80.845 | ||
| –2Log-likelihood (Final) | 522.696*** | 269.753*** | 94.425** | 67.414** | ||
| Pseudo R2 | 0.302 | 0.264 | 0.165 | 0.166 | ||
| Observations | 300 | 300 | 300 | 300 | ||
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | 5.479*** | 5.208** | 0.203 | (4.708)*** | 4.283* | (0.425) |
| 6.911 | 5.917 | 0.005 | 6.998 | 3.169 | 0.012 | |
| SIZE | (0.839)*** | (0.034) | (0.431)* | 0.450*** | 0.058 | (0.308) |
| 17.534 | 0.026 | 2.912 | 8.146 | 0.035 | 0.636 | |
| LEV | 0.018 | 0.208 | 3.737 | (0.043) | (1.735) | (0.151) |
| 1x10−4 | 0.011 | 2.020 | 0.001 | 0.366 | 0.002 | |
| TobinQ | (0.425)* | (0.333) | (0.086) | 0.293 | 0.141 | 0.150 |
| 3.088 | 0.499 | 0.116 | 2.120 | 0.351 | 0.484 | |
| ROA | 19.593*** | (2.409) | 16.336* | (8.658) | 1.411 | 1.994 |
| 6.639 | 0.120 | 3.029 | 2.228 | 0.017 | 0.027 | |
| ROE | 1.702 | 7.330** | 12.384** | (6.771)** | 4.859 | 7.153 |
| 0.194 | 4.629 | 6.004 | 5.095 | 0.368 | 0.725 | |
| AGE | 0.078*** | 0.042** | 0.079*** | (0.070)*** | (0.006) | (0.046) |
| 16.884 | 3.864 | 13.319 | 15.706 | 0.099 | 1.923 | |
| BGD | (0.026)*** | 0.009 | (0.032)** | 0.014** | (0.037)* | 0.014 |
| 6.994 | 0.670 | 4.876 | 3.014 | 3.039 | 0.543 | |
| –2Log-likelihood (Intercept) | 748.423 | 366.519 | 113.149 | 80.845 | ||
| –2Log-likelihood (Final) | 522.696*** | 269.753*** | 94.425** | 67.414** | ||
| Pseudo R2 | 0.302 | 0.264 | 0.165 | 0.166 | ||
| Observations | 300 | 300 | 300 | 300 | ||
Note(s): Significant at *p < 0.10; **p < 0.05; ***p < 0.01; Negative numbers are presented in parentheses; the italicised value below is the Wald value
Additionally, the negative coefficient for SDG and Div0 (β = −4.708) indicates that greater SDG disclosure reduces the probability of no dividend payments, thereby increasing the likelihood of dividends being issued, consistent with Saeed and Zamir (2021).
The coefficient between SDG and DIV50 (β = 4.283, p < 0.10) suggests that higher SDG disclosure increases the probability of dividend payments exceeding 50%. Interestingly, this finding contrasts with Barros et al. (2023), who reported a different trend regarding ESG disclosure and higher dividend payouts. While Barros et al. (2023) found that greater ESG disclosure increases the likelihood of dividend payments within the fiscal year, this study observes no significant relationship between SDG disclosure and DivYear.
Figure 1 illustrates the predicted probabilities of cash dividend payments and overall dividend issuance. The results indicate that the strongest positive associations are observed for firms with DIVMethod = Cash and DIV0, suggesting that higher levels of SDG-related disclosure significantly increase the likelihood of dividend payouts, with a pronounced preference for cash distributions.
The figure shows the four graphs arranged in a 2 by 2 grid. All the graphs are labeled “Adjusted Predictions with 95 percent C I s” on top. The top left graph is labeled “Cash.” The vertical axis is labeled “P r(Divmethod double equals 1)” and is marked from bottom to top as follows: “negative 1.00 e−09,” “negative 5.00 e−10,” “0,” “5.00 e−10,” “1.00 e−09.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with three error bars at 30, 40, and 60 is shown across the axis range at y equals 0. The top right graph is labeled “Stock.” The vertical axis is labeled “P r(Divmethod double equals 2)” and is marked from bottom to top as follows: “0.9999,” “0.99995,” “1,” “1.00005,” “1.0001.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with an error bar at 30 is shown across the axis range close to y equals 1. The bottom left graph is labeled “Div 0.” The vertical axis is labeled “P r(Div0 double equals 1)” and is marked from bottom to top as follows: “negative 0.4,” “negative 0.2,” “0,” “0.2,” “0.4.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with three error bars at 50, 60, and 70 is shown across the axis range at y equals 0. The bottom right graph is labeled “Div 50.” The vertical axis is labeled “P r(Div50 double equals 1)” and is marked from bottom to top as follows: “0,” and “2.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points without visible error bars is shown across the axis range at y equals 1.Predicted probability of event occurrence. Authors' own work
The figure shows the four graphs arranged in a 2 by 2 grid. All the graphs are labeled “Adjusted Predictions with 95 percent C I s” on top. The top left graph is labeled “Cash.” The vertical axis is labeled “P r(Divmethod double equals 1)” and is marked from bottom to top as follows: “negative 1.00 e−09,” “negative 5.00 e−10,” “0,” “5.00 e−10,” “1.00 e−09.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with three error bars at 30, 40, and 60 is shown across the axis range at y equals 0. The top right graph is labeled “Stock.” The vertical axis is labeled “P r(Divmethod double equals 2)” and is marked from bottom to top as follows: “0.9999,” “0.99995,” “1,” “1.00005,” “1.0001.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with an error bar at 30 is shown across the axis range close to y equals 1. The bottom left graph is labeled “Div 0.” The vertical axis is labeled “P r(Div0 double equals 1)” and is marked from bottom to top as follows: “negative 0.4,” “negative 0.2,” “0,” “0.2,” “0.4.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points with three error bars at 50, 60, and 70 is shown across the axis range at y equals 0. The bottom right graph is labeled “Div 50.” The vertical axis is labeled “P r(Div50 double equals 1)” and is marked from bottom to top as follows: “0,” and “2.” The horizontal axis is labeled “S D G” and ranges from 30 to 70 in increments of 10 units. A horizontal line of data points without visible error bars is shown across the axis range at y equals 1.Predicted probability of event occurrence. Authors' own work
4.3 Robust test
According to Hassanein and Elmaghrabi (2025) reporting of sustainability activities negatively correlates with the intensity of market competition. Consequently, this section examines whether varying levels of SDG disclosures reflect strategic financial decisions among Vietnamese firms. For robustness checks, the sample was divided into two groups based on the median SDG disclosure score (0.143). Logistic regression analyses were then performed separately for firms with high and low SDG disclosures, with the results presented in Tables 5 and 6.
Multinomial logit regression results for high-level SDG disclosure
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | 7.471** | 10.400*** | (3.040) | (6.752)** | 0.966 | (22.418)* |
| 4.063 | 6.885 | 0.281 | 4.250 | 0.084 | 3.481 | |
| SIZE | (0.618)** | 0.164 | (0.115) | 0.261 | 0.223 | (0.636) |
| 4.106 | 0.216 | 0.076 | 1.008 | 0.352 | 1.437 | |
| LEV | (0.105) | (4.250) | 9.518* | 1.854 | (0.849) | 5.961 |
| 0.002 | 1.700 | 2.913 | 0.617 | 0.057 | 1.046 | |
| TobinQ | (0.051) | 0.723 | 0.836 | (0.360) | 0.891* | 1.627** |
| 0.008 | 1.430 | 1.026 | 0.524 | 3.068 | 4.638 | |
| ROA | 24.972* | (27.977)* | 36.281* | (2.578) | 3.222 | 2.620 |
| 2.908 | 3.216 | 3.337 | 0.048 | 0.052 | 0.021 | |
| ROE | 0.442 | 17.337** | 6.405 | (11.006)* | 3.898 | 7.403 |
| 0.004 | 6.237 | 0.383 | 3.722 | 0.125 | 0.366 | |
| AGE | 0.072*** | 0.051 | 0.064** | (0.068)** | 0.003 | (0.054) |
| 6.796 | 2.675 | 4.037 | 6.529 | 0.030 | 1.357 | |
| BGD | (0.010) | 0.045** | (0.026) | (0.003) | (0.070)** | 0.010 |
| 0.425 | 5.340 | 1.229 | 0.065 | 5.111 | 0.136 | |
| −2Log-likelihood (Intercept) | 396.348 | 174.612 | 85.706 | 57.765 | ||
| −2Log-likelihood (Final) | 245.046*** | 121.858*** | 66.923** | 40.266** | ||
| Pseudo R2 | 0.382 | 0.302 | 0.219 | 0.303 | ||
| Observations | 163 | 163 | 163 | 163 | ||
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | 7.471** | 10.400*** | (3.040) | (6.752)** | 0.966 | (22.418)* |
| 4.063 | 6.885 | 0.281 | 4.250 | 0.084 | 3.481 | |
| SIZE | (0.618)** | 0.164 | (0.115) | 0.261 | 0.223 | (0.636) |
| 4.106 | 0.216 | 0.076 | 1.008 | 0.352 | 1.437 | |
| LEV | (0.105) | (4.250) | 9.518* | 1.854 | (0.849) | 5.961 |
| 0.002 | 1.700 | 2.913 | 0.617 | 0.057 | 1.046 | |
| TobinQ | (0.051) | 0.723 | 0.836 | (0.360) | 0.891* | 1.627** |
| 0.008 | 1.430 | 1.026 | 0.524 | 3.068 | 4.638 | |
| ROA | 24.972* | (27.977)* | 36.281* | (2.578) | 3.222 | 2.620 |
| 2.908 | 3.216 | 3.337 | 0.048 | 0.052 | 0.021 | |
| ROE | 0.442 | 17.337** | 6.405 | (11.006)* | 3.898 | 7.403 |
| 0.004 | 6.237 | 0.383 | 3.722 | 0.125 | 0.366 | |
| AGE | 0.072*** | 0.051 | 0.064** | (0.068)** | 0.003 | (0.054) |
| 6.796 | 2.675 | 4.037 | 6.529 | 0.030 | 1.357 | |
| BGD | (0.010) | 0.045** | (0.026) | (0.003) | (0.070)** | 0.010 |
| 0.425 | 5.340 | 1.229 | 0.065 | 5.111 | 0.136 | |
| −2Log-likelihood (Intercept) | 396.348 | 174.612 | 85.706 | 57.765 | ||
| −2Log-likelihood (Final) | 245.046*** | 121.858*** | 66.923** | 40.266** | ||
| Pseudo R2 | 0.382 | 0.302 | 0.219 | 0.303 | ||
| Observations | 163 | 163 | 163 | 163 | ||
Note(s): Significant at *p < 0.10; **p < 0.05; ***p < 0.01; Negative numbers are presented in parentheses; the italicised value below is the Wald value
Multinomial logit regression results for low-level SDG disclosure
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | (30.403)*** | (8.518) | (16.804) | 19.393** | (4.754) | 86.524 |
| 8.607 | 0.685 | 1.882 | 6.585 | 0.021 | 2.239 | |
| SIZE | (1.260)*** | (0.609) | (0.743)** | 0.632*** | (1.611) | (0.942) |
| 14.690 | 2.476 | 4.223 | 8.148 | 1.224 | 0.577 | |
| LEV | (0.016) | 5.199 | 1.947 | (1.711) | (5.506) | (8.655) |
| 5x10−5 | 1.656 | 0.345 | 0.825 | 0.376 | 0.626 | |
| TobinQ | (0.440) | (2.997)* | (0.192) | 0.405 | (1.272) | (0.402) |
| 1.268 | 3.257 | 0.301 | 1.242 | 0.502 | 0.326 | |
| ROA | 15.312* | 16.195 | 11.201 | (13.108)* | (1.472) | (17.936) |
| 2.910 | 1.659 | 1.068 | 3.296 | 0.003 | 0.437 | |
| ROE | 2.263 | 3.424 | 12.040** | (4.123) | 11.110 | 25.282 |
| 0.265 | 0.455 | 3.939 | 1.431 | 0.333 | 1.550 | |
| AGE | 0.081*** | 0.039 | 0.076** | (0.065)*** | (0.033) | (0.141) |
| 9.336 | 1.061 | 6.087 | 7.800 | 0.388 | 2.426 | |
| BGD | (0.045)*** | (0.034)* | (0.035)* | 0.035*** | (0.015) | 0.003 |
| 8.191 | 3.171 | 2.994 | 8.432 | 0.148 | 0.005 | |
| −2Log-likelihood (Intercept) | 378.208 | 204.477 | 29.452 | 29.293 | ||
| −2Log-likelihood (Final) | 254.179*** | 145.702*** | 19.315 | 15.521* | ||
| Pseudo R2 | 0.328 | 0.287 | 0.344 | 0.473 | ||
| Observations | 151 | 151 | 151 | 151 | ||
| Model | DivMethod | Div0 | Div50 | DivYear | ||
|---|---|---|---|---|---|---|
| Variable | Cash | Stock | Cash–stock | |||
| SDG | (30.403)*** | (8.518) | (16.804) | 19.393** | (4.754) | 86.524 |
| 8.607 | 0.685 | 1.882 | 6.585 | 0.021 | 2.239 | |
| SIZE | (1.260)*** | (0.609) | (0.743)** | 0.632*** | (1.611) | (0.942) |
| 14.690 | 2.476 | 4.223 | 8.148 | 1.224 | 0.577 | |
| LEV | (0.016) | 5.199 | 1.947 | (1.711) | (5.506) | (8.655) |
| 5x10−5 | 1.656 | 0.345 | 0.825 | 0.376 | 0.626 | |
| TobinQ | (0.440) | (2.997)* | (0.192) | 0.405 | (1.272) | (0.402) |
| 1.268 | 3.257 | 0.301 | 1.242 | 0.502 | 0.326 | |
| ROA | 15.312* | 16.195 | 11.201 | (13.108)* | (1.472) | (17.936) |
| 2.910 | 1.659 | 1.068 | 3.296 | 0.003 | 0.437 | |
| ROE | 2.263 | 3.424 | 12.040** | (4.123) | 11.110 | 25.282 |
| 0.265 | 0.455 | 3.939 | 1.431 | 0.333 | 1.550 | |
| AGE | 0.081*** | 0.039 | 0.076** | (0.065)*** | (0.033) | (0.141) |
| 9.336 | 1.061 | 6.087 | 7.800 | 0.388 | 2.426 | |
| BGD | (0.045)*** | (0.034)* | (0.035)* | 0.035*** | (0.015) | 0.003 |
| 8.191 | 3.171 | 2.994 | 8.432 | 0.148 | 0.005 | |
| −2Log-likelihood (Intercept) | 378.208 | 204.477 | 29.452 | 29.293 | ||
| −2Log-likelihood (Final) | 254.179*** | 145.702*** | 19.315 | 15.521* | ||
| Pseudo R2 | 0.328 | 0.287 | 0.344 | 0.473 | ||
| Observations | 151 | 151 | 151 | 151 | ||
Note(s): Significant at *p < 0.10; **p < 0.05; ***p < 0.01; Negative numbers are presented in parentheses; the italicised value below is the Wald value
Table 5 indicates that among firms with higher SDG disclosure levels, the likelihood of paying cash dividends (β = 7.471) and share-based dividends (β = 10.400) significantly increases, with share dividends being particularly favoured, aligning with findings from Hoang and Nguyen (2024). Moreover, the negative association between SDG disclosure and Div0 (β = −6.752) implies that higher SDG transparency enhances the probability of dividend payments. Additionally, a negative coefficient for SDG disclosure regarding within-year dividend payments (DivYear, β = −22.418, p < 0.01) suggests that greater transparency reduces such dividend practices, contrasting with Barros et al. (2023).
Conversely, Table 6 shows that among firms with lower SDG disclosures, greater transparency reduces the probability of issuing cash dividends (β = −30.403). However, a positive association with Div0 (β = 19.393) indicates that these firms are generally less inclined to issue dividends. No significant relationships emerged between SDG disclosure and Div50 or DivYear, suggesting that for these firms, SDG transparency does not significantly influence dividend size or timing.
To address potential endogeneity, the two-stage least squares (2SLS) method was employed using the year-on-year difference in SDG disclosure as an instrumental variable. Durbin and Wu–Hausman tests confirmed the validity of this approach. The logic for using SDG disclosure differences as the instrument lies in its relevance – past sustainability disclosures significantly influence current-year disclosure – but remains unrelated to the error term. Untabulated results revealed no significant endogeneity (p-value > 0.10) across all equations tested, confirming the exogeneity of SDG disclosures within the current model. The short duration of the study period (three years) may partly explain the absence of detectable endogeneity, suggesting that future research should investigate longer timeframes to explore potential long-term effects and reverse causality.
Finally, the study calculates marginal effects at mean values of independent variables to estimate the probability of each dividend event occurring when independent variables are at their mean values. These results are detailed in Table 7.
Marginal effects at the mean values of the independent variables
| Variable | Expression Pr(DivMethod = 0) | Expression Pr(Div0 = 1) | Expression Pr(Div50 = 1) | Expression Pr(DivYear = 0) |
|---|---|---|---|---|
| SDG | 0.169 (mean) | |||
| SIZE | 17.673 (mean) | |||
| LEV | 0.590 (mean) | |||
| TobinQ | 0.829 (mean) | |||
| ROA | 0.063 (mean) | |||
| ROE | 0.128 (mean) | |||
| AGE | 28.6 (mean) | |||
| BGD | 20.627 (mean) | |||
| Delta-method | ||||
| Margin | 0.248 | 0.198 | 0.024 | 0.986 |
| Std. err | 0.040 | 0.032 | 0.011 | 0.008 |
| z | 6.230 | 6.26 | 2.25 | 125.06 |
| P > z | 0.000 | 0.000 | 0.024 | 0.000 |
| Number of observations | 300 | 300 | 300 | 300 |
| Variable | Expression | Expression | Expression | Expression |
|---|---|---|---|---|
| SDG | 0.169 (mean) | |||
| SIZE | 17.673 (mean) | |||
| LEV | 0.590 (mean) | |||
| TobinQ | 0.829 (mean) | |||
| ROA | 0.063 (mean) | |||
| ROE | 0.128 (mean) | |||
| AGE | 28.6 (mean) | |||
| BGD | 20.627 (mean) | |||
| Delta-method | ||||
| Margin | 0.248 | 0.198 | 0.024 | 0.986 |
| Std. err | 0.040 | 0.032 | 0.011 | 0.008 |
| z | 6.230 | 6.26 | 2.25 | 125.06 |
| P > z | 0.000 | 0.000 | 0.024 | 0.000 |
| Number of observations | 300 | 300 | 300 | 300 |
5. Conclusion and implications
This study provides critical insights into the relationship between SDG-related disclosures and dividend policy, highlighting their role in mitigating information asymmetry and agency conflicts. The findings indicate that firms with higher levels of SDG disclosure are significantly more likely to issue dividends – particularly in cash, although share-based distributions are also observed. These results reinforce the increasing relevance of sustainability disclosures in emerging markets such as Vietnam, where transparency and sound corporate governance are vital to fostering investor trust and financial stability.
Managers can leverage non-financial disclosures to bolster investor confidence and reduce uncertainty, while investors may consider SDG reporting as a key input in their investment decisions. Policymakers and regulators may find value in mandating more comprehensive SDG disclosures, particularly during periods of market volatility, as improved transparency can contribute to stabilising firms and supporting investor sentiment in times of uncertainty. In the context of Vietnam's emerging market, where retail investors dominate and institutional investor activism remains nascent, sustainability disclosures are increasingly interpreted as proxies for corporate integrity and long-term viability. Vietnamese investors may view robust SDG reporting as a positive signal of governance maturity – particularly when coupled with consistent dividend policies – thereby influencing perceptions of financial discipline and responsible capital allocation. These findings contribute meaningfully to the corporate disclosure literature and offer important implications for corporate leaders and regulatory agencies, especially in formulating strategies for disclosure during financial crises. Additionally, the findings provide practical guidance for firms aiming to assess and improve their sustainability reporting practices and associated financial behaviours.
From a regulatory standpoint, authorities may consider variations in the disclosure of specific SDG indicators when designing policies to improve the relevance and utility of information communicated to stakeholders. Enhanced regulatory frameworks mandating consistent, comprehensive sustainability reporting – such as those based on GRI or the Earth Dividend System – can significantly strengthen financial policy transparency. These frameworks provide investors with standardised and reliable information, allowing more accurate assessments of corporate risk and long-term strategy. By reducing information asymmetry, disclosure standards help align dividend and other financial policies with broader sustainability objectives, thereby increasing the predictability, discipline, and accountability of corporate financial decisions.
In this regard, both firms and regulators may consider the Earth Dividend System – a modern impact measurement tool aligned with the United Nations Sustainable Development Goals (United Nations, 2015; Earthcapital, 2024). This system evaluates investments across five ESG thematic categories and 30 sustainability indicators, offering a structured framework to assess sustainability impacts and net benefits. Integrating Earth Dividend metrics into corporate due diligence (see Figure 2) can enhance organisational resilience, unlock long-term value, and align strategic priorities with sustainable development goals. Although GRI (2016) remains widely used, the Earth Dividend System supports a more holistic approach that encompasses all 17 SDGs – broader in scope than GRI's 77 indicators. In future applications, particularly with wider adoption of the Earth Dividend approach, aligning disclosures with explicit SDG targets may improve reporting quality and strategic alignment.
The diagram shows an oval at the center labeled “Earth Dividend.” Five arrows extend outward from the oval, each pointing to a text box. Starting from the top and moving clockwise, the text boxes are labeled as follows: “Natural resources Goals: 6-7-9-12-13-14-15.” “Ecosystem Services Goals: 2-3-6-7-13-14-15.” “Pollution Goals: 3-6-7-11-12-13-14.” “Social and economic distribution Goals: 1-3-8-10-16.” “Society and Governance Goals: 3-11-16-17.”Earth dividend. Authors' own work
The diagram shows an oval at the center labeled “Earth Dividend.” Five arrows extend outward from the oval, each pointing to a text box. Starting from the top and moving clockwise, the text boxes are labeled as follows: “Natural resources Goals: 6-7-9-12-13-14-15.” “Ecosystem Services Goals: 2-3-6-7-13-14-15.” “Pollution Goals: 3-6-7-11-12-13-14.” “Social and economic distribution Goals: 1-3-8-10-16.” “Society and Governance Goals: 3-11-16-17.”Earth dividend. Authors' own work
Despite its contributions, this study has several limitations. The sample focuses on the 100 largest firms by market capitalisation, which may restrict the generalisability of the findings to smaller firms or those in different sectors. Future research should expand to more diverse samples across firm sizes and industries to assess the robustness of observed patterns under varying market conditions. Further investigation could also examine the influence of SDG disclosures on other financial dimensions, such as profitability, capital structure, or investment efficiency. Importantly, future research might also explore how sustainability disclosures influence broader capital allocation decisions – beyond dividend policy – to include investment and financing choices, thereby offering a more holistic view of financial strategy under sustainability pressures.
Given the findings, SDG disclosures appear instrumental in reducing agency problems and enhancing transparency – factors that increasingly shape dividend policy. As sustainability becomes embedded in both corporate and regulatory priorities, such disclosures are likely to become central to strategic decision-making. Structured frameworks like GRI help ensure consistency in reporting and evaluating sustainability performance across firms and markets.
Enterprises are encouraged to adopt sustainability-led strategies, enhance internal capacity, and embrace digital transformation in their reporting systems. Policymakers, in turn, should support small and medium-sized enterprises through capacity-building programmes, access to green finance, and targeted support mechanisms. Establishing sustainability committees and environmental management teams may further enhance firms' sustainability performance and support effective implementation of SDG-aligned strategies (Hassanein et al., 2024; Tahat and Hassanein, 2024; Elmaghrabi et al., 2025).
The supplementary material for this article can be found online.

