This study investigates the influence of Environmental, Social, and Governance (ESG) disclosure on the profitability of Saudi-listed non-financial firms in the context of Saudi Vision 2030. The study uses a sample of 100 non-financial organizations from 2019 to 2023 (500 firm-year observations). This study uses panel data analysis and a random-effects regression model to examine the relationship between ESG disclosure and firm profitability as assessed by return on assets (ROA). To assess ESG disclosure, this study developed a comprehensive ESG disclosure index based on worldwide ESG guidelines and Saudi-related regulations. The regression results show a significantly positive relationship between ESG disclosure and firm profitability, emphasizing the financial benefits of corporate transparency and sustainability. This finding is consistent with the stakeholder theory, implying that firms with strong ESG commitments boost investor trust, improve risk management, and increase operational efficiency. Thus, this study adds to the ESG literature by presenting empirical evidence from Saudi Arabia, a growing country that is undergoing regulatory transition. Additionally, this study’s notable contribution is the development of a comprehensive ESG disclosure index tailored for the Saudi corporate landscape, integrating global reporting standards with local regulatory requirements. This index enhances the assessment of ESG transparency and offers a thorough tool for examining business sustainability strategies. The results offer substantial insights for policymakers, investors, and corporate leaders, emphasizing the significance of ESG in sustainable financial performance.

This study examined the influence of Environmental, Social, and Governance factors on the financial performance of companies listed on the Saudi Stock Exchange (Tadawul). Employing a panel data approach, the analysis covers 450 firm observations collected annually during the period 2018–2023. Financial performance is measured using Return on Assets (ROA) and Return on Equity (ROE), while ESG disclosure scores are disaggregated into their three constituent pillars. Firm size, revenue per share, and leverage are incorporated as control variables. The fixed effects regression results reveal that social factors demonstrate statistically significant positive relationships with both ROA and ROE, supporting the stakeholder theory-based perspective that strong social practices enhance operational efficiency and investor confidence. Conversely, environmental and governance factors exhibit no significant association with either financial performance metric within the study period. Leverage shows a significant negative relationship with ROA but not with ROE, while revenue per share consistently demonstrates strong positive associations with both performance measures. These findings contribute to the limited literature on ESG–performance linkages in Gulf Cooperation Council markets and offer important implications for corporate managers, investors, and policymakers seeking to advance sustainability objectives within the framework of Saudi Vision 2030.

In light of Saudi Arabia's economic diversification, this research investigate how Foreign Direct Investment (FDI) and technology spillovers can affect firm level productivity. It relies on panel data from 73 non-financial firms listed on the Tadawul Stock Exchange for the period of 2015-2024. Using the GGM method of moments, this study is conducted using a dynamic panel approach. Unit root and cointegration studies demonstrate that there is a consistent long-term connection between productivity, FDI, and various spillover effects. The research indicates that FDI, vertical spillovers, investment in R&D, stability of exchange rates, firm characteristics such as age and size, export intensity or leverage, all have a positive impact on Total Factor Productivity (TFP). The research highlights the distinction between TFP determinants and labor productivity, concluding that horizontal spillovers may enhance technological efficiency, but not overall output per worker. The combination of GMM estimation and cointegration analysis is a reliable method for resource-dependent economies, although alternative methods may be less effective. From a political perspective, the findings emphasize that expanding vertical relationships, building domestic R&D capabilities, and encouraging innovation to foster better utilization of FDI inflows are crucial.

This study examines the relationship between board gender diversity and green innovation, focusing on the moderating role of board-level environmental committees in Saudi-listed firms. Drawing on upper echelon and agency theories, this study argues that gender-diverse boards enhance environmental innovation by improving strategic oversight, stakeholder responsiveness and long-term decision-making. Using panel data from 107 non-financial firms listed on the Saudi Stock Exchange (Tadawul) during 2018–2024, this study applies fixed effects and dynamic generalized method of moments (GMM) estimations to address endogeneity and unobserved heterogeneity. The findings show that board gender diversity positively influences green innovation, whereas environmental committees strengthen this relationship by institutionalizing environmental oversight and sustainability governance. This study contributes to the corporate governance and sustainability literature by demonstrating that the effectiveness of board diversity depends on the complementary governance structures. These findings have practical implications for firms and policymakers seeking to strengthen sustainable innovation and environmental governance in emerging economies such as India.

Using three machine knowledge models that utilise Neutrosophic Logic (NL)—Linear Regression, Random Forest, and Gradient Increasing—this study studies the possibilities of refining financial result forecast. The cognitive behind this is that NL recovers the prediction power of these models across dissimilar organisations by accounting for the inherent uncertainty, unpredictability, and lack of sureness in financial numbers. In this study, the models' presentation is evaluated using a variety of financial factors, including interest rates and stock prices. F1 score, recall, correctness, and exactness are some of the metrics used by this drive. When likened to other models, NL with Gradient Cumulative consistently outperforms them in terms of correctness and robustness. You might think of Abu Dhabi Islamic Bank and the National Bank of Bahrain as two such examples. Companies like Emirates Islamic Bank reap some benefits from Chance Forest's combination of cheap computation with precision, but only to a lower degree. Complex datasets used by businesses like Al Rajhi Bank are beyond the capabilities of Linear Reversion, even when combined with NL. By proving that cooperative techniques combined with NL positively reduce financial data volatility, our results lay the groundwork for improved financial forecasting and decision-making. The exercise has demonstrated that NL has great potential to enhance financial prediction models, which could have future applications in investment planning and risk organization.