Agency Theory:

Free cash flows are very high, which could encourage managers to waste. Agency costs are minimised by paying dividends or repurchasing common stock, thereby preventing funds from being spent on unprofitable ventures. Disciplined payout policy, in its turn, is expected to increase the firm value. The school of thought holds that dividends can reduce moral hazard by forcing managers to finance their activities externally, thereby exposing the decision-making process to market scrutiny. Additionally, dividends may signal a firm's financial situation and outlook, particularly when information asymmetry exists, thereby affecting investor preferences and share prices (Barclay and Smith, 1988). This is because regular dividend payments signal that a firm is confident it will remain profitable and liquid over the long term, which may reassure investors and lead to an understated cost of capital ( Arnone and Leogrande, 2024; Bayón and Vega, 2018). On the other hand, the bird-in-hand theory implies that investors will give more weight to present dividends than to perceived future capital gains, which makes firms with regular dividends pay out to have higher firm valuations ( Demirgüneş, 2015). Such a choice is explained by reduced uncertainty about future cash flows, which makes dividend-paying stocks more appealing to risk-averse investors.

Signalling Theory:

The corporate policies act as signals. For example, higher dividends or voluntary ESG reporting can signal good things to come and management-led governance. Successful companies are prone to pay dividends that are way below the board to stand out. Such behaviour is consistent with the principle that alterations in dividend payments may have important implications for investors, especially for the financial health of a firm and its prospects, thereby affecting its market value ( Fanning and Brittain, 1967). This applies especially in the case of asymmetric information, i.e., when managers have better knowledge of the firm's actual performance and prospects than external investors ( El-Deeb and Allam, 2024; Nazir and Nawaz, 2012). Thus, those with high prospects resort to dividend payments as a plausible cue to communicate their strong financial condition and future growth to the market (Al-Najjar and Kilincarslan, 2019). On the other hand, a low or absent dividend may also be a bad indicator, reflecting financial volatility or pessimism, and this will result in falling share prices and investor confidence ( Yurko, 2018). What is more, regular dividends would help reduce asymmetric information between management and investors, as they would show that a company can generate sufficient cash flows to maintain such payments ( Charbti, 2020; Damayanti and Dinaseviani, 2024). The signalling effects of dividends are more important for mature companies, which, due to high accumulated profits and other sources of self-funding, are more likely to pay higher dividends to shareholders ( AlQudah et al., 2025).

Resource-Based View (RBV):

Knowledge, innovation, and digital capabilities can be characterised as intangible assets that are scarce and confer a competitive advantage. Intellectual capital is the sum of human capital, structural capital, and relational capital, one of the driving forces of performance. Companies that invest in FinTech also develop unspoken capabilities (such as data infrastructure and analytics) that can increase productivity and value ( Chen and Srinivasan, 2023; Lăzăroiu et al., 2023). This perspective argues that intellectual capital, including human, structural, and relational assets, is a valuable, hard-to-duplicate asset that underpins a firm's long-run competitive advantage and value creation ( Kadim et al., 2020). This school of thought also assumes that companies with stronger internal resources and capabilities, including those that can be developed through FinTech investments, are better positioned to achieve higher profitability and market valuation by leveraging these specific assets to innovate and differentiate in the market. This is given that even when such resources are efficiently deployed, firms can build unique competencies, increase operational efficiency, improve product or service offerings, and eventually achieve better financial performance ( Essel, 2025).

Stakeholder Theory:

The existence of firms is to serve their stakeholders (customers, employees, community, and regulators). ESG disclosure is driven by stakeholders' requirements for transparency and accountability. ESG practices build reputational capital and legitimacy, thereby promoting long-term value by taking stakeholders' concerns into account. This method combines the principles of agency theory and stakeholder theory, implying that organisations align their environmental, social, and governance investments with different stakeholder interests to increase profitability ( Wang et al., 2025). Moreover, the long-term performance and sustainability of a firm are inevitably tied to stakeholder approval and support; the firm's management must balance and meet the expectations of the various parties. A combination of these theoretical lenses, specifically the resource-based perspective and stakeholder theory, provides a background on how sustainability and innovation serve as mutually reinforcing instruments for creating competitive advantage and increasing firm value ( Widagdo and Rahmawati, 2025). The concept of ESG practices' power to enhance social capital through stakeholder interactions strengthens this synergistic relationship, as social capital is an important intangible asset that drives a firm's competitive advantage and performance.

Information Asymmetry:

It is common for managers to be more knowledgeable than investors. ESG reports (voluntary disclosures) and digital financial statements minimise this gap. The use of FinTech applications (such as blockchain and big data) also enhances transparency and data accuracy, further alleviating information asymmetry. Minimising asymmetry can reduce the cost of capital and increase firm value. Such a decrease in information asymmetry, enabled by the development of highly sophisticated FinTech solutions, results in a more efficient allocation of capital and, possibly, an increase in firm valuation because investors are now more likely to trust the information disclosed ( Toumi et al., 2023). Such increased disclosure also fosters trust among investors who are essential in attracting capital and promoting eventual financial stability ( Najaf et al., 2024). In addition, high ESG results particularly reduce information asymmetry, which facilitates resource acquisition; thus, innovation becomes more available and viable due to reduced sunk costs and financing constraints arising from the high level of risk in research and development. Combined, these views represent how ESG disclosure simultaneously serves as a response to stakeholder pressures, a source of strategic benefit, a credibility-enhancing indicator, and a tool of governance ( Metwally et al., 2025).

Technology Adoption (Diffusion):

Classical diffusion paradigms (Rogers) and the technology acceptance paradigm assume that firms adopt innovations to achieve efficiency and competitiveness. Use of FinTech is a technology decision. Companies operating in FinTech should benefit from automated business operations, innovative financial services (e.g., mobile payments), and expanded market reach. This aligns with the literature on innovation and digital transformation ( Brehmer, 2023). It is especially topical in high-tech industries that can better afford FinTech due to their large resources and research-and-development focus, enabling sustainable growth and quick market demand adjustments. Such a combination of FinTech with high-technology fields not only contributes to operational efficiencies but also to the prospect of developing new financial products to meet existing and emerging investor needs, namely, socially responsible and technology-enhanced investment opportunities ( Najaf et al., 2024). Transparency and risk management are other advantages of the FinTech and ESG intersection, which, in combination with technological components such as AI, machine learning, and blockchain, are becoming increasingly tangible assets, making organisations more resilient and improving organisational performance ( AlQudah et al., 2025). Such technological overlays, especially within the FinTech industry, are increasingly seen as indispensable solutions that enable companies to adapt to emerging regulatory demands and compete at a new level through ESG-driven innovation ( Arnone and Leogrande, 2024).

A. The adoption of FinTech and its implications for ESG Disclosure.

The introduction of FinTech into corporate structures is gaining greater recognition for its ability to drive change in Environmental, Social, and Governance (ESG) development ( Du et al., 2022). Such a disruptive effect can be attributed to FinTech's ability to increase information openness, improve reporting procedures, and enable more effective capital allocation to sustainable projects ( Wang, 2025). In particular, FinTech can enable small- and medium-sized ventures to more closely monitor their environmental footprints and align their performance with sustainability objectives, thereby enhancing transparency ( Campanella et al., 2025). Furthermore, the use of FinTech solutions can provide substantial support for a firm's ESG by enabling real-time tracking of environmental indicators and promoting the creation of more effective governance frameworks. This integration plays a vital role in achieving a comprehensive understanding of the effects a firm has on stakeholders and the environment, which will eventually lead to improvements in overall ESG performance.

Moreover, the introduction of artificial intelligence on FinTech platforms will directly and indirectly improve ESG performance, especially in green finance practices ( Sohail et al., 2025). This is done through reduced credit risks, enhanced supervision efficiency, increased product innovation, and enhanced information sharing, all of which are critical to developing green finance. All of these technological advances will allow making more accurate measurements of investments based on sustainability and building new financial solutions that can encourage companies to act in a responsible way towards the environment ( Du et al., 2022; Sohail et al., 2025) FinTech adoption, consequently, would greatly contribute to corporate sustainability through resource optimization, reduced carbon footprint and general environment sustainability performance of the companies ( Tian et al., 2023).

Path 2: How Intellectual Capital affects ESG Disclosure.

Intellectual Capital is essential in defining a firm's ability to fully express ESG disclosure, as it involves the knowledge, skills, and organisational processes that make a firm innovative and strong ( Demiraj, 2025). In particular, intellectual capital, encompassing human, structural, and relational aspects, provides the foundation for the resources needed to develop and implement more advanced ESG strategies and reporting systems. Proper utilisation of intellectual capital will enable companies not only to identify salient ESG issues but also to develop solutions and convey them transparently to stakeholders ( Sun and Guo, 2025). The increase in intellectual capital is reputed to have been associated with more investments in employee training, innovation and knowledge systems, which are a part of ESG strategies and contribute to greater development of human and structural capital, resulting in increased financial performance ( Demiraj, 2025).

Furthermore, companies with high intellectual capital are better placed to navigate the intricacies of ESG reporting, including integrating diverse data sets and providing a statement of long-term sustainability ( Chouaibi and Chouaibi, 2020). This will allow them not only to comply with regulatory disclosure requirements but also to engage voluntarily in disclosure activities more indicative of a stronger commitment to sustainability. This active involvement can enable companies to improve their reputation, attract not only able investors but also encourage the creation of value over the long-term horizon, as this can be achieved with a highly developed knowledge base and skilled human resources. Hypothesis 2:

The higher the level of intellectual capital, the more transparent and comprehensive ESG disclosures have a positive effect.

The growing interest of stakeholders in corporate social responsibility and clear environmental stewardship has only heightened the significance of ESG disclosure, thereby affecting key corporate financial judgments, such as dividend payments ( Pham et al., 2024). Firms with strong ESG disclosures tend to be viewed as more stable and controlled, which, in turn, can be reflected in a more stable, predictable dividend policy, indicating financial soundness and sustainability to investors in the long term ( Jorgji et al., 2024). Moreover, well-established ESG functioning and reporting can ensure the attraction of a larger segment of sustainably-oriented investors who can consider the payment of dividends as evidence of the company's shaping its shareholder returns and its sustainable interests ( Duong and Nguyen, 2025).

On the other hand, companies with less robust ESG reporting can be scrutinised and doubted more by investors, which can lead to changes in dividend policies as they strive to appease stakeholders or to control the impression that a company is a risky undertaking. This interaction implies that strong ESG reporting can normalise and even increase dividend payments because these explicit forms of reporting are usually viewed by investors as indicating reduced risk and long-term corporate value ( Zahid et al., 2022). It means that comprehensive ESG disclosures may also be a tool for promoting the sustainability activities of a firm and complying with the requirements of corporate responsibility, which eventually leads to a constructive correlation with dividend policy due to the inflow of sustainability-sensitive investors ( Jorgji et al., 2024). Hypothesis 3:

ESG disclosure has a positive effect on dividend payout, that is, the ways companies use to attract attention to their shareholders' interests and meet their expectations of corporate accountability.

B. Intellectual Capital and Dividend Policy.

As greater focus is developing on the importance of intellectual capital, it is becoming evident as a crucial factor in the dividend policy of a particular firm in influencing its ability to produce sustainable returns as well as allocating them to the shareholders ( Odat and Bsoul, 2024). In particular, it has a set of elements of intellectual capital, including human capital (superiority of employees), structural capital (organisational procedures and patents), and relational capital (friends and enemies). Having a high human capital base may drive operational performance and innovation, leading to higher earnings and sustainable dividend payouts ( Duong and Nguyen, 2025). In the same vein, healthy structural capital, or structures that cover the organisation's processes and intellectual property, can elevate operational efficiency and minimise expenses, thereby liberating capital to be passed on to shareholders. Further, strong relational capital, driven by stakeholder involvement and brand recognition, can boost market and consumer trust, which will be translated into stable revenue, thereby improving a firm's capacity to support or raise dividend payments. Hypothesis 4:

Intellectual capital is positively related to a firm's tendency to make higher and more uniform dividend payouts.

C. Financial performance as a moderator.

The averting effect arises from the fact that high financial performance gives firms the space and capacity to invest in ESG activities and maintain dividend disbursement levels, even in the face of economic uncertainty ( Wang, 2025). In this way, financially sound companies can more easily absorb the costs of extensive ESG disclosures and even adopt sustainable practices without undermining their loyalty to shareholders by paying them dividends ( Almulhim et al., 2024). In turn, companies with weaker financial positions can have difficulty allocating resources to comprehensive ESG reporting and stable dividend distributions, making it a trade-off between these two strategic goals. This implies that financial strength can be seen as an important facilitator that enables businesses to address the perceived dilemma between investing in sustainability and giving dividends to shareholders ( Jain and Malhotra, 2025). Hypothesis 5:

Financial performance is a positive moderator of the relationship between ESG disclosure and dividend policy, which enables financially capable companies to focus on sustainability and shareholder returns.

D. Dividend Policy and Firm Value.

As a key financial choice, the dividend policy has a direct impact on the valuation of a firm, on the one hand, by its signalling effect on future profitability and risk, and, on the other hand, on the preferences of investors and capital structure of the firm ( Malik and Kashiramka, 2024). In particular, a properly designed dividend policy may signal financial stability and good corporate governance, which, in turn, would boost shareholder value ( Zhou and Bu, 2025). Dividend infections can be used to attract and maintain long-term investors who often consider regular payments as a sign of good financial status and willingness of the company to pay its shareholders their capital back ( Almulhim et al., 2024; Jain and Malhotra, 2025). Additionally, dividend policy may create information asymmetry between management and investors, thereby minimising the cost of equity and increasing the firm's total market value ( Joshi and Joshi, 2024). In its turn, an unpredictable or missing dividend policy can be viewed as a negative indicator of financial insecurity or mismanagement, which may incidentally result in a loss of investor trust and a reduction in the company's value ( Kusumawati and Hadiyanto, 2024). It aligns with the idea that regular, large dividend payments are commonly viewed as signs of firm stability and administrative effectiveness and thus have a positive impact on the firm's value ( El-Deeb and Allam, 2024). (Hypothesis 6) The influence of dividend policy, specifically, high dividend payout ratios, is assumed to have a positive effect on the market value of a company as it is a positive indicator of financial performance and may attract a group of investors who need additional returns in the long term ( Moolkham, 2024).

E. The Mediation of ESG Disclosure.

Such a mediation effect means that ESG disclosure will convert intangible assets and operational decisions into tangible signals that will shape internal policy-making and perceptions among external stakeholders ( Jain and Malhotra, 2025). In particular, ESG disclosure mediates the relationship between proactive tax planning and firm value, and between intellectual capital and dividend policy as transparency and accountability improve ( Adthajak et al., 2025). This disclosure, in its turn, will aid in eliminating the problems of information asymmetry as it will lead to investor trust, which may become a value generator for the firm's value. In addition, sustainability reporting, especially in active tax planning, has been shown to enhance ESG performance, which, in turn, drives overall business value, particularly for companies with strong financial capacity. This increased transparency, stemming from full ESG disclosure, provides another monitoring mechanism for stakeholders, demonstrating the quality of the company's operations and strengthening its image ( Matuszewska-Pierzynka et al., 2023).

F. The Mediation of Dividend Policy.

Huge dividend payout, as an example, may indicate high cash flows and high profitability, hence, positively affecting the investor perceptions and thus, the firm value ( Yuniningsih et al., 2022). It is also supported by the fact that the dividend policy can be viewed as an indicator of a corporation's future, which positively affects a firm's value and helps attract investment ( Handayani and Ibrani, 2023). This signalling capability is especially applicable to sustainable capitalism: the value of firms is no longer defined by financial performance, but by strategic responses to environmental challenges and the incorporation of green innovation capacity ( Widagdo and Rahmawati, 2025). Additionally, healthy ESG performance is further reinforced by media coverage, which enhances transparency and strengthens a company's image, thereby attracting investment and helping firms stand out in the competitive market ( Xue et al., 2024). This interrelatedness highlights the problem of how clear disclosure of ESG activities, as well as a steady dividend policy, are used in the long-term sustainability and value of a firm in the market through trust and indicators of a healthy operational and financial activity ( Metwally et al., 2025; Tamasiga et al., 2024).

In this research, an integrated conceptual framework is developed to examine the relationships among FinTech adoption, ESG disclosure, intellectual capital, and dividend policy in explaining firm value. The framework positions firm value as an outcome variable, measured by a market-based indicator such as Tobin's Q or market-to-book value. In particular, the use of FinTech is a strategic technological competence of the company. It improves data processing, minimises information asymmetry, and increases financial transparency ( Alkhawaldeh et al., 2023). FinTech enhances the transparency and visibility of ESG practices through the following mechanisms. Companies that embrace digital financial technology are better positioned to gather, assess, and report on sustainability-related information. To that end, ( Arnone and Leogrande, 2024), it is projected that FinTech adoption will have a direct impact on firm value and an indirect impact on ESG disclosure.

Based on this premise, ESG disclosure serves as a key mediating construct. It indicates the firm's seriousness regarding environmental, social, and governance practices and investor perception. Clear ESG reporting minimises uncertainty, enhances stakeholder trust, and can affect payout decisions. Nevertheless, the relevance of ESG values is determined by the firm's economic strength (Yu et al., 2018). Thus, the association between ESG disclosure and firm value is moderated by financial performance. ESG programs deliver value when backed by consistent profitability and permanent cash flows. In addition to these factors is the intellectual capital, which encompasses the firm's knowledge resources, such as human capital, structural capital, and relational capital. It facilitates innovation, operational efficiency, and the strategic alignment of ESG practices. In addition, intellectual capital enhances the firm's ability to implement sustainability strategies and increases the likelihood that ESG disclosure will translate into market valuation benefits ( Manzari, 2024; Pham et al., 2024). The framework, therefore, reifies both a direct and an artificial route between intellectual capital and firm value based on ESG disclosure. Also, the dividend policy serves as a signalling mechanism. Dividend payments reflect the management's financial security and confidence. ESG-oriented companies can adjust their dividend policies to balance sustainability investments with shareholder returns. It follows that dividend policy serves as a transmission mechanism between ESG performance and firm value ( Jain and Malhotra, 2025; Wang, 2025). This interaction implies that companies that incorporate green innovation and digital ESG models are better positioned to increase corporate financial value ( Widagdo and Rahmawati, 2025). Concisely, the conceptual model brings all these relationships into a unified pattern. The strategic enablers are FinTech adoption and intellectual capital. ESG disclosure serves as a mediating mechanism. The dividend policy is a signalling channel. The ESG-firm value relationship is moderated by financial performance. These elements, in combination, describe the interaction among digital capability, sustainability reporting, knowledge assets, and payout strategy that influences corporate market valuation (see Figure 1).

Define the following hypotheses: •

H1: FinTech adoption increases firm value.

To test H1–H5, you specify a fixed-effects panel model: FV it = α i + δ t + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + γ ′ X it + u it , where FV it is firm value (Tobin’s Q, market-to-book ratio, or ROA); α i are firm effects; δ t are year effects; X it includes size, leverage, growth, profitability, and sector/country dummies; and u it is the error term. Study tests the hypotheses by examining the signs and significance of β 1 through β 4 . A positive β 1 supports H1, and so on. If the study finds that FinTech adoption, ESG disclosure, intellectual capital, and dividend policy all have positive and significant coefficients, then accept H5.

Testing moderation (H6)

To test whether ESG disclosure moderates the link between dividend policy and firm value, include an interaction term: FV it = α i + δ t + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + β 5 ( ESG it × DIV it ) + γ ′ X it + u it .

A positive and significant β 5 indicates that ESG disclosure strengthens the dividend policy effect, thus supporting H6.

Dynamic specification and endogeneity correction

Firm value often exhibits persistence. To account for dynamics and address potential endogeneity, we extend the model: FV it = α i + δ t + ϕ FV i , t − 1 + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + β 5 ( ESG it × DIV it ) + γ ′ X it + ε it .

Lagging firm value introduces a correlation between FV i , t − 1 . Moreover, firm fixed effects creates bias. You apply the system GMM estimator to resolve this. The estimator uses differenced equations to remove firm effects and instruments lagged dependent variables with further lags. The moment conditions are E [ FV i , t − s ε it ] = 0 for s ≥ 2 . Study test instrument validity using Hansen’s J-statistic and examine first- and second-order serial correlation using the Arellano–Bond tests. Study accepts H1–H6 if the estimated parameters remain consistent in the GMM framework.

The sample comprises all bank-based financial institutions listed on the DSE. A panel data set spanning nine fiscal years, from 2015 to 2023, is assembled. The main sources of data are the annual reports and sustainability disclosures. These reports will give audited financial statements, dividends, managerial discussions, and ESG reports. The reports also include textual components used to build a firm-level index of FinTech adoption. Since the research covers the timeframe of the high-paced digital change and increased focus on corporate sustainability, the time wisdom will guarantee adequate variation in the activity of FinTech and its ESG practices ( Qamruzzaman, 2026).

Dependent variable. The three complementary measures used to determine firm value include the Tobin Q, the market-to-book ratio, and return on assets (ROA). Tobin’s Q is computed as Tobin Q = market capitalisation/book debt/ total assets. The market-to-book ratio is a measure of the relationship between market equity and book equity. ROA- Net income/total assets. These indicators show market perceptions, opportunities for growth and performance of the operations.

Key predictors. Text analytics coverage of the FinTech usage is registered in a custom index. The index is calculated by applying a bag-of-words model to annual reports, summing the frequencies of FinTech-related words (e.g., digital banking, mobile payments, blockchain, etc.), and dividing by the total number of words. The larger the values, the higher the focus on FinTech innovations. ESG disclosure is quantified using standardised ESG performance scores from Bloomberg, MSCI, or Refinitiv, with higher scores indicating greater disclosure power. When standardised scores are unavailable, an ESG index is created by scoring narratives in sustainability reports across environmental, social, and governance dimensions. Intellectual capital is given a Value Added Intellectual Coefficient (VAIC) calculated as the product of capital employed efficiency, human capital efficiency and structural capital efficiency. Components of VAIC are obtained using income statement data: sales less input costs other than labour are value added; the whole book value of equity plus debt is capital employed; employee costs are reported in the notes to financial statements. Dividend policy is formulated into the dividend payout ratio: dividends paid/net income.

Control variables. The size is the natural logarithm of total assets. Leverage is the ratio of total debt to total assets. Growth is analytically calculated as the percentage change in annual sales. The return on equity (ROE) captures profitability. Sector and country dummies capture unobservable heterogeneity within industries and across regulatory jurisdictions. The year-fixed effects control for macroeconomic shocks and regulatory changes. A detailed description of variables and measurements is displayed in Table 1.

The econometric specification translates the study’s hypotheses into testable equations and defines the estimation strategy. This section details the model structure, variables, assumptions, and statistical tests used to evaluate how FinTech adoption, ESG disclosure, intellectual capital and dividend policy influence firm value across Bangladeshi financial institutions. It builds on theoretical evidence that FinTech adoption helps firms meet ESG objectives, that ESG disclosure and intellectual capital affect firm value, and that dividend policy signals financial health.

1. Baseline fixed-effects model

To test the core hypotheses (H1 through H5), the study employs a linear panel regression with firm and year fixed effects: FV it = α i + δ t + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + γ ′ X it + ε it , where i indexes firms and t indexes years. FV it is firm value (Tobin’s Q, market-to-book ratio, or ROA). α i captures unobserved firm-specific effects; δ t captures year-specific shocks; FINTECH it , ESG it , IC it and DIV it represent FinTech adoption, ESG disclosure, intellectual capital and dividend policy; X it contains controls (size, leverage, growth, profitability, sector and country dummies); and ε it is the idiosyncratic error. The fixed-effects estimator removes time-invariant heterogeneity. The hypotheses are tested by the signs and statistical significance of β 1 through β 4 . A positive β 1 suggests FinTech adoption increases firm value (H1); a positive β 2 suggests ESG disclosure increases firm value (H2); a positive β 3 suggests that intellectual capital increases firm value (H3), and a positive β 4 supports the dividend policy effect (H4). Joint significance of these coefficients supports H5.

2. Moderation tests

To test whether ESG disclosure moderates the effect of dividend policy on firm value (H6), an interaction term is added: FV it = α i + δ t + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + β 5 ( ESG it × DIV it ) + γ ′ X it + ε it .

A positive and significant β 5 indicates that firms with higher ESG disclosure benefit more from dividend payouts, aligning with evidence that ESG practices enhance stakeholder trust. For the role of financial performance in the ESG–value relationship (H7), a second interaction between ESG disclosure and ROE or ROA is included: FV it = α i + δ t + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + β 5 ( ESG it × DIV it ) + β 6 ( ESG it × ROE it ) + γ ′ X it + ε it .

A significant β 6 indicates that financial performance moderates the ESG–value link, consistent with findings that ESG effectiveness depends on economic strength.

3. Dynamic panel model and endogeneity correction

Firm value may exhibit persistence, and reverse causality could bias estimates. To address these issues, a dynamic specification includes the lagged dependent variable: FV it = α i + δ t + ϕ FV i , t − 1 + β 1 FINTECH it + β 2 ESG it + β 3 IC it + β 4 DIV it + β 5 ( ESG it × DIV it ) + γ ′ X it + ε it .

Here, ϕ measures persistence. The presence of FV i , t − 1 and potentially endogenous regressors (e.g., dividend policy might respond to firm performance) calls for the system Generalised Method of Moments (GMM). In system GMM, first differences remove fixed effects, and lagged levels and differences of endogenous variables serve as instruments. The validity of instruments is tested using Hansen’s J-statistic, and the absence of second-order serial correlation is verified using Arellano–Bond tests.

4. Cross-sectional dependence, slope heterogeneity and CS-ARDL

Because banks operate in a common economic environment, cross-sectional dependence may arise. Pesaran’s cross-sectional dependence (CD) test evaluates the null of independence: CD = 2 N ( N − 1 ) ∑ i < j ρ ̂ ij , where ρ ̂ ij is the pairwise residual correlation. Significant CD suggests common shocks. The Swamy test tests for slope homogeneity across firms; rejecting the null indicates heteroscedasticity. When cross-sectional dependence is present, the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model is used to capture long-run relationships while accounting for common factors. Its error-correction form is: Δ FV it = ψ i ( FV i , t − 1 − λ i 0 − λ i 1 FINTECH i , t − 1 − λ i 2 ESG i , t − 1 − λ i 3 IC i , t − 1 − λ i 4 DIV i , t − 1 − λ i 5 ′ X i , t − 1 ) + ∑ p = 0 P − 1 θ ip Δ FV i , t − 1 − p + ∑ q = 0 Q − 1 ϕ iq Δ Z i , t − 1 − q + δ 0 FV ¯ t − 1 + δ 1 ′ Z ¯ t − 1 + e it , where Z it = ( FINTECH it , ESG it , IC it , DIV it , X it ) , · ¯ denotes cross-section averages, ψ i is the speed of adjustment, and λ ij are long-run coefficients. A significant negative ψ i confirms cointegration.

5. Quantile regression and machine-learning models

Quantile regression explores heterogeneity across the distribution of firm value. For quantile τ, estimate: Q τ ( FV it | Z it ) = α τ + β τ 1 FINTECH it + β τ 2 ESG it + β τ 3 IC it + β τ 4 DIV it + β τ 5 ( ESG it × DIV it ) + γ τ ′ X it .

Estimating this model at, say, the 10th, 25th, 50th, 75th, and 90th percentiles reveals whether the effects differ between firms with low and high market valuations.

Finally, to capture nonlinearities and complex interactions, a deep neural network (DNN) is trained. Input features include normalised values for FinTech adoption, ESG disclosure, intellectual capital, dividend policy, and control variables. The network comprises multiple fully connected layers with rectified linear units (ReLUs) and dropout regularisation. Hyperparameters are tuned through cross-validation. Performance is measured using mean squared error (MSE) and mean absolute error (MAE). Shapley additive explanations (SHAP) quantify each feature’s contribution to predicted firm value.

6. Estimation procedure and diagnostics

Before estimation, multicollinearity is checked using variance inflation factors. Stationarity and long-run relationships are examined via unit-root tests (e.g., cross-sectionally augmented IPS) and cointegration tests (Westerlund). The baseline fixed-effects model is estimated first, followed by Hausman tests to determine whether to use fixed or random effects. The dynamic system GMM model addresses endogeneity. CS-ARDL models handle cross-sectional dependence and cointegration. Quantile regressions and DNN models provide distributional insights and capture nonlinear patterns. Robustness checks include alternative proxy definitions (e.g., dividend yield vs payout ratio), alternative FinTech index constructions, and subsample analyses (pre-2019 vs post-2019). Together, these econometric and machine-learning techniques rigorously test the study’s hypotheses and offer comprehensive evidence on how digital finance and sustainability practices shape corporate valuation.

Descriptive statistical analysis (see Table 2) shows that the key variables for DSE-listed financial institutions are moderately dispersed over the 2015-2023 period. According to market-based measures, banks trade at slightly above book value on average, implying investor confidence in their sustainability. The Q and the Tobin Market-to-Book ratios are skewed to the right, indicating a group of institutions with significantly higher valuation premiums. The Return on Assets (ROA) shows low dispersion, consistent with stability in the financial regulations governing the banking industry. The indices of the FinTech report show rather small mean scores, but significant variation is evident due to differences in the level of digital adoption across institutions. The scores for Environmental, Social, and Governance (ESG) exhibit moderate standard deviation and a weak skewness, indicating. The variability in intellectual-capital efficiency is evident in the Value Added Intangible Capital (VAIC) values, and the central tendency in the dividend payout ratio is moderate and stable, indicating the payout has occasionally been adjusted drastically.

Table 3 shows that there are positive relationships between the proxy for firm value and the constructs of FinTech adoption, ESG disclosure, and intellectual capital. The highest pair-wise correlation is between VAIC and Market-to-Book, where the two are very closely aligned in knowledge-based efficiency and equity value. The dividend payout ratios also show a moderate positive relationship with firm value, consistent with signalling theory. Notably, the correlations between independent variables do not exceed the threshold for traditional multicollinearity; the Variance Inflation Factor ranges from 1.42 to 2.76, which is considerably lower than the critical value of 10. All these results indicate the absence of severe multicollinearity, thus confirming the legitimacy of all the explanatory variables in the regression formulas. Both predictors are shown to provide unique information about the change in firm value.

According to the CD test conducted by Pesaran (2004), the dependence across the panel sample across the cross-section is statistically significant, implying that common shocks that arise are common among the banks and may be a result of regulatory policies, monetary conditions, or other macro conditions in Bangladesh, see output in Table 4. Such dependence can lead to incorrect standard errors and estimates of coefficients that are ignored. The Swamy slope homogeneity test rejects the null hypothesis of homogeneous slopes, indicating heterogeneity in the effects of the explanatory variable on bank valuation. As a result, these results support the use of second-generation panel methods, i.e., CS-ARDL and CCEMG, which can account for cross-sectional correlation and heterogeneous dynamics. The findings highlight the importance of sophisticated panel techniques rather than simple, pooled estimators.

Table 5 presents the baseline fixed-effects estimates for three firm-value proxies. These are the firm-fixed effects and year-fixed effects models. The specification captures unobserved time-varying differences across banks, including their governance cultures, ownership structures, and risk appetites. It is also calculated to eliminate the impact of typical annual shocks, e.g., macroeconomic conditions, regulatory adjustments and industry-wide liquidity events. The findings directly support H1-H5 across both market-based and accounting-based values.

H1 estimates that the adoption of FinTech augments firm value. All three models have a positive, statistically significant coefficient for FINTECH. For Tobin's Q, the estimated value is 0.183, with a significant p-value. It suggests a relationship between the higher level of FinTech engagement, as measured by the text-based index, and a higher market valuation relative to assets ( AlQudah et al., 2025; Najaf et al., 2023). The findings of the analysed materials assume that the adoption of FinTech will notably improve the financial stability of a bank, its financial performance, and the efficiency of its operations, which will help increase the overall valuation of the firm and promote its sustainable growth ( Handayani and Ibrani, 2023; Kayed et al., 2024). This impact is also in line with the resource-based view, which states that increased operational efficiency, better customer service, and the novelisation of offered products may result from a strategic investment in advanced IT resources, including those related to FinTech ( Tarawneh et al., 2024).

The effect size of the Market-to-Book model is even greater, 0.271, with extremely high significance. Such a trend suggests that equity investors reward banks with a stronger digital orientation, presumably because digital capabilities enhance service access, reduce transaction costs, and improve information processing ( Bueno et al., 2024; Chen and Srinivasan, 2023). Research results in support of the position that at the stage of FinTech development, the profitability of banks is boosted many times over and the level of risk-taking is negatively influenced, which means that there is an excellent and statistically significant effect on financial activity and the level of financial stability ( Kayed et al., 2024). Moreover, ESG-related issues, especially in combination with FinTech innovation, can further improve performance, as excessive quantification of the role of sustainable strategies in the financial sector ( AlQudah et al., 2025). These improvements are eventually followed by increased financial stability and sustainability, as evidenced by sustained positive changes across different model forms and financial stability indicators ( AlHares et al., 2022). A positive FINTECH effect, with a coefficient of 0.0082, which is significant at traditional levels, is also indicated in the ROA model. This inconsistency between proxies and differences in scale, but consistency across proxies, supports H1. The most fundamental assumption is that FinTech adoption is linked to market expectations and operating performance. Based on research findings it can be assumed that FinTech innovations help to add more automated processes and achieve better customer experience due to the use of such tools as chatbots and mobile apps and ensure better fraud detection opportunities in the FinTech industry with the help of machine learning, which in turn lead to a higher profitability and efficiency of a bank ( Alsmadi et al., 2023).

H2 hypothesis a positive relationship between the ESG disclosure and the firm value. In all three models, ESG has a positive value and is statistically significant. In the Tobin Q model, the ESG coefficient is 0.0041, and in the Market-to-Book model, it is 0.0068. These estimates suggest that the wider the ESG disclosure, the higher the market value. The coefficient of ROA, in turn, indicates a smaller yet significant relationship between ESG reporting and improved profitability; this relationship is evident in the coefficient of 0.00018, though the channels through which it operates likely involve investor confidence, funding conditions, and stakeholder relationships. The fact that the stronger of the market-based measures indicates a greater influence of ESG disclosure suggests that it will have a primary impact on valuation through perceptions, risk reassessment, and the anticipated predictability of cash flows. This qualifies H2 and aligns with the idea that transparency minimises information asymmetry and reputational risk. Our results are consistent with the current literature, including studies suggesting that the positivity of ESG performance, which FinTech frequently supports, has a positive effect on banks' performance and overall financial stability ( Hamdouni, 2025; Mokhtar and Alam, 2023; Yuan, 2025). Also, the collaboration between fintech and green finance can lead to a more sustainable future for the global banking industry by incorporating environmental performance into the financial practices of banking organisations and making them more goal-oriented in terms of sustainability ( Kassetty et al., 2024).

H3 is that firm value increases due to intellectual capital. The VAIC is both good and meaningful across all specifications. The Q coefficient of the Tobin is 0.057, and the Market-to-Book is 0.091. The two are emphatically important. The coefficient of ROA is also significant at 0.0019. This trend signifies that more efficient banks in terms of using human resources, structure, and capital invested resources have better market value and high profitability ( Alkababji and Mushtaha, 2023). The findings indicate that intellectual capital is a value-creation resource in the banking industry, where the quality of services, risk analytics, product design, and process discipline depend on knowledge resources. According to the market-derived coefficients, which were found to be relatively higher than accounting-derived coefficients, this implies that investors valued intangible capability more than current profitability. This proposition is aligned with the literature of Lev and Radhakrishnan (2005); William et al. (2019).

In the context of H4, it is assumed that dividend policy increases a firm's value. The dividend payout ratio shows positive, significant coefficients in all three models. This is less in the Tobin Q, with a coefficient of 0.112 significant at the five per cent level, but more in the Market-to-book, with a coefficient of 0.186 significant at the five per cent level. The DIV in the ROA model is also positive and considerable, with a coefficient of 0.0065. This evidence suggests that dividend payout is a valuation indicator for listed financial institutions. Thin and non-tinier dividend distributions may be interpreted as a sign of the quality of earnings, the strength of the liquidity situation, and a disciplined managerial team ( Kılınçarslan, 2018; Nkn, 2018). Hypothesis-4 (H4) is supported by the positive correlation that is repeated in the Q, Market-to-Book and ROA of Tobin. It also indicates that the dividend decisions are associated with both the market confidence and operating returns.

H5 implies that the joint influence of FinTech adoption, ESG disclosure, intellectual capital, and dividend policy explains firm value when the impact of common firm-specific factors is mitigated. Table 4 supports H5 in two ways. Once, none of the four focal variables is found to have negative signs or statistical significance when controlling for and including fixed effects. Second, the measures of model fit suggest significant within-firm explanatory power. In the range of R ² between 0.389 and 0.436, which is large with the fixed-effects design and the banking environment in which most determinants are time-invariant, there is a value. The model F-statistics are highly significant across all specifications (see Figure 2), indicating that a combination of regressors is significant in explaining the time-varying value of firms in the banking industry. This combined importance aligns with the structure that underpins digital capability, sustainability reporting, intangible efficiency, and payout strategy working in unison.

Switching to controls, leverage is negative and significant in all proxies. This is consistent with the fact that increased leverage increases risk and valuation, while also squeezing profitability. The market-based models hurt firm size, which may indicate the effect of maturity and reduced growth in large institutions. The growth in sales is positive and substantial, indicating that expansion is advancing in value and profit. ROE, one of the controls used to determine profitability, is positive and significant, and it acts as anticipated. Among the focal variables, FinTech adoption and VAIC have the strongest and most consistent drivers in market-based valuation models, whereas ROA shows lesser but notable impacts. This distinction is educative. Market valuations are expectations of future efficiency and growth, whereby they respond more intensively to FinTech and intellectual capital. Accounting profitability reflects the current returns and possibly slower changes. The general evidence in Table 4 confirms H1 5 and provides a weighted foundation for the subsequent models that address endogeneity, long-term dynamics, and nonlinear effects using system GMM, CS ARDL, and deep learning.

Interaction terms are presented in Table 6 to test the moderating roles spelt out in H6 and H7. Adding ESG x Dividend and ESG x Financial Performance to the explanatory power greatly enhances the value, as shown by the higher within-R2 values across all three firm value proxies compared to the baseline model.

The relationship between ESG disclosure and dividend payout is positive and significant for the Q, Market-to-Book, and Tobin's Q. This finding shows that dividend policy is also value-relevant in the cases when ESG disclosure is more effective in that investors turn dividend payments more reliable and sustainable when firms are represented by clear evidence of environmental, social, and governance practices at the same time. The observed higher magnitude in the Market-to-Book model indicates that equity markets are more sensitive to the combined signalling effect of sustainability disclosure and payout commitment. In the case of ROA, the correlation is positive but weaker, and operating effectiveness is improved through ESG engagement in dividend decisions. Based on the existing literature, high ESG performance has the potential to improve a firm's profitability and, in turn, its ability to pay future dividends. Also, empirical evidence shows that strong ESG practices can greatly increase shareholder value, and dividend policy is a credible signal of financial sustainability and proper governance.

The correlation between ESG disclosures and financial performance is also strong and highly statistically significant across all three specifications. This result shows that the market greatly compensates for ESG disclosure when companies have strong financial fundamentals, as greater disclosure of ESG strengthens the positive link between ESG and financial performance. Arguably, this indicates that ESG undertakings can only result in increased valuation based on profitability and earnings stability ( Demirgüneş, 2015). The moderation effect is not only statistically but also economically significant in market-based proxies (Q and Market-to-Book) and in the accounting-based model (ROA). Moreover, this symbiotic interaction highlights that whereas the importance of ESG engagement is essential, the financial effect is greater when combined with the company's financial health, which will help investors more readily identify the real value of sustainable value creation and distinguish greenwashing from genuine sustainability ( Malhotra, 2025). These results support existing studies that show that ESG disclosure has a positive impact on firm value across most performance indicators, such as Q and Return on Assets ( Hamdouni, 2025).

Using the two-step system GMM estimator and a dynamic panel specification (see Table 7), we rigorously assess the robustness of the baseline relationships and explicitly address both firm value persistence and the risk of endogeneity bias, which is one of the major threats to causal inference in panel data. There is significant inertia in the firm's value due to past performance, investors' expectations, and past strategic investments; the lagged dependent variable captures autocorrelation well. It is interesting to note that its coefficients are not only positive but also highly significant (p> m v 0.01) across all proxies, indicating strong persistence in valuation and profitability of DSE-listed financial institutions. Importantly, the FinTech adoption variable maintains its positive and substantial coefficient (p<0.05), providing robust confirmation that the occurrence of digital transformation positively influences firm value beyond the cause-and-effect relationship. ESG disclosure also has a positive and substantial impact (p<0.05), highlighting sustainability transparency as an uncompromised weight in valuation, regardless of endogeneity controls. The intellectual capital shows a high level of relevance (p<0.01), confirming its pivotal role in the structural value-generating process. The association between dividend payout and investor confidence remains strong (p<0.05), indicating a strong signal. Model diagnostics strongly support the GMM framework: AR rejects the null at first-differencing; AR does not reject; no second-order serial correlation in the data; Hansen J-statistics are insignificant (p>0.10) across specifications; instrument counts are prudently low relative to cross-sectional units. This diagnostic validation is comprehensive, which testifies to the reliability of the GMM estimates, as the results of research reveal that the observed relationships are not due only to the correlation between the variables but also the strong causal relationships between them even under the influence of quite complex econometric challenges ( Alshouha et al., 2025; Bagh et al., 2025; Zhao et al., 2024).

To crucially test whether long-run equilibrium relations and to investigate short-run dynamics, it is estimated in error-correction form using the CS-ARDL model, see Table 8. The error-correction measure is large and significant across all specifications (p<0.01), which is strong evidence of cointegrated movement and a consistent mechanism for eliminating such deviations (replenishing the shocks to the firm value) in the long-run: errors are fixed at a slow pace (of not more than 20-30% per period), making the long-run equilibrium persistent auto-correlation in the banking market plausible. Overall, the use of FinTech has a strong, positive, statistically significant impact in the long run, with an average beta of 0.15-0.25 and a p-value of less than 0.01, which proves that long-term digital transformation not only boosts but also has a long-lasting strengthening impact on market value due to the increase in operational performance and competitiveness. ESG disclosure also delivers a positive, significant long-run coefficient ($\beta = 0.10-0.20, p=0.05), confirming that sustained sustainability reporting enhances firm credibility, reduces perceptions of risk, and yields premiums in investor portfolios. The potency of intellectual capital lies in its long-run effects, as underscored by its role as a key structural driver of long-term competitiveness through knowledge advantages and innovation rents. Dividend policy also has high importance, as it reflects stable financial health and a strong long-run valuation. Smaller in size yet positive are the short-run dynamics of FinTech and ESG variables, which exhibit initial adaptation frictions that yield strategic dividends over time. The CS-ARDL model that incorporates cross-sectional averages is a powerful means to eliminate the prevalent macroeconomic shocks and enhance the reliability of the estimates, as well as causality. Twelve solidly supported by these findings are assertions of stable cointegrated relationships, which empirically confirm theoretical hypotheses that digital prowess, sustainability transparency, and intellectual assets are sterling foundations for long-run value creation in financial institutions. In particular, the adoption of AI-based FinTech-associated technologies has been proven to have a beneficial effect on the financial performance, sustainability, and stability of the banking sector ( Hamdouni, 2025).

The quantile regression findings (see Table 9) clearly indicate a high level of heterogeneity in the firm value distribution, i.e., the coefficients increase in magnitude as the quantile is shifted toward the lower or upper ends, with the largest being for market-based measures. This validates a nonlinearity, skewed and upslope influence in which the most effective performers enjoy disproportionate rewards.

In the case of Tobin Q, the FinTech coefficient shot up from 0.094 to 0.257 across the 10th to the 85th-90th percentile, or a 173 per cent increase, which is a solid indicator that digital transformation delivers considerably better value creation for well-performing banks. The fact that this incremental value is 0.163 throughout the distribution is a fabricated testament to the market leaders having a disproportionate share in integrating FinTech. Similarly, VAIC shows a monotonic increase from 0.031 at the lower quantiles to 0.083 at the higher quantiles, indicating that intellectual capital exhibits increasing marginal returns, most pronounced when banks are valued at a premium. The results show that innovation in FinTech is a highly significant contributor to Total Factor Productivity, and the quantile differences are statistically significant at the 10th percentile. This indicates that the effectiveness of FinTech use in improving bank performance is not evenly distributed, and the strongest performers have a greater capacity to convert technological expenditures into clear financial performance ( Hassan et al., 2025; Li et al., 2024).

The ESG disclosure also increases, ranging from 0.0021 to 0.0061, in the Q model of the Tobin, definitively suggesting that sustainability transparency is an effective enhancer of the valuation of strong market firms. This tendency is reflected in dividend policy, which increased its values from 0.052 to 0.169, indicating the greater power of payout signalling in promoting high-value entities. According to the research results, the noted positive correlation between FinTech and the performance of the bank is especially high in the higher quantiles, which proves that banks with more favourable initial performance metrics are in a position to use the FinTech investments to improve their financial results ( Hassan et al., 2025). This shows that FinTech leads to greater performance improvements in banks with lower performance, as they can access financial services previously unavailable ( Hassan et al., 2025).

The Market-to-Book version further widens this heterogeneity: FinTech impacts increase fourfold, to 0.364, highlighting the extreme sensitivity of equity markets to the adoption of digital technology in the elite enterprise. VAIC soars up by 0.046 up to 0.136, as the key position of knowledge efficiency is sealed. ESG coefficients almost tripled, justifying the predisposed investor reward for stability in well-valued companies. Even within the ROA model, where all coefficients are moderately scaled, the trend is persuasive. FinTech increases consistently by 0.0031 to 0.0118, VAIC increases similarly by 0.0009 to 0.0033, and dividends increase accordingly. This is a clear indication of how operational efficiencies from digitalisation, sustainability, and intellectual capital are focused within the ranks of the best banks. The results show an intent to conclude that, whereas FinTech can improve financial inclusion by expanding services to underserved populations, the advantages are more practically achieved by better-performing organisations, which can obtain economies of scale and strategically integrate technologies.

The deep neural network models (see Table 10) show high predictive ability across all three firm value proxies. In the case of Q, the testing R2 of 0.711 indicates that about 71 per cent of the change in a firm's valuation can be explained by nonlinearity effects among FinTech adoption, ESG disclosure, intellectual capital, dividend policy, and control variables. The limited difference between the training and testing R2 indicates a few cases of overfitting and high ability to generalise. The Market-to-Book model is the most predictive, with a test R2 of 0.734. It means that equity market valuation is highly responsive to complex nonlinear relationships among digital transformation, sustainability disclosure, and knowledge assets. The extra margin between the test and training MSEs is within a reasonable range, indicating the model's stability. In the case of ROA, predictive accuracy is not high but significant, with a test R2 of 0.673. This suggests that operational profitability has nonlinear interactions, whereas accounting-based performance is less sensitive to this measure than market-based performance. Across all models, the DNN provides a better account of explanatory power than the linear specifications, suggesting that the interaction and structural relationship between FinTech adoption, ESG practices, intellectual capital, and firm value exist in the context of DSE-listed financial institutions, rather than as linear relationships.

Although this research provides many in-depth insights, it also has several limitations that open the door to future studies. Firstly, it is limited to DSE-listed financial institutions in Bangladesh, and thus its results cannot be generalised to other industries, developing economies, or developed markets. This model can be replicated in future research to determine whether these relations hold in other settings. Second, strong proxies still do not eliminate the complexities associated with the imprecision in measuring intangible assets, such as intellectual capital, and the novelty of FinTech valuation methods [Current Document: Introduction]. Future research would consider more specific, and even proprietary, metrics of FinTech uptake and intellectual capital to reflect the details better. Thirdly, we develop meaningful relationships; nevertheless, the underlying causality and interdependence, particularly the complex dynamic interaction between various categories of FinTech innovations and various facets of ESG, warrant further research. These complex pathways could be further unraveled using longitudinal studies and qualitative approaches. Lastly, the research time frame (2015-2023) captures a particular stage of digital transformation and ESG development. This time period, or specific intervals of high regulatory or technological change, may provide further insight into the resilience and transformation of these relationships, particularly regarding the optimal balance and synergistic combination of disparate knowledge resources with corporate social responsibility.