20 © 2026 Conscientia Beam. All Rights Reserved. The effect of corporate governance in driving organizational performance to AI-driven decision making Ala’Aldin Al Rowwad1+ Mohannad Ahmad ALmomani2 1Faculty of Business, Public Administration Department, The University of Jordan. Email: a.alrowwad@ju.edu.jo 2School of Management, Universiti Sains Malaysia,11800 Penang, Malaysia. Email: moh.momani@student.usm.my (+ Corresponding author) ABSTRACT Article History Received: 16 June 2025 Revised: 23 September 2025 Accepted: 28 October 2025 Published: 13 November 2025 Keywords AI-driven decision-making Corporate governance ESG practices Organizational performance SmartPLS4. This paper explores the linkages between corporate governance, AI-sponsored decision- making, ESG (Environmental, Social, and Governance) practices, and organizational performance. In particular, it examines the mediating influence of ESG practices and the moderating effect of AI-based decision-making regarding implementing the corporate governance and performance relationship. The study employed a quantitative research design by collecting primary data through structured surveys distributed to executives, managers, and board members of various industry Jordanian firms. A stratified random sampling approach is used to select a representative sample. Structural Equation Modeling was used to test the hypotheses. The findings indicate that corporate governance positively impacts ESG practices and organizational performance. It suggests that ESG practices partially mediate between governance and performance. Moreover, AI-based decision-making moderates the governance-performance relationship, whereby governance effectiveness is augmented by improved risk assessment, compliance monitoring, and strategic planning. The findings of this study strengthen the belief that corporate governance should go beyond traditional compliance models and adopt AI-backed and ESG-influenced strategies to maintain an expertise edge. The results highlight the practical implications for corporate heads, policymakers, and investors, underscoring the need for governance systems that align artificial intelligence-based decisions with ESG sustainability standards. Enterprises must be responsible for AI adoption, with adequate human oversight and ethical safeguards to minimize risks. Contribution/Originality: This paper investigates how ESG initiatives mediate empirically and AI-based approaches moderate the corporate governance–performance nexus in Jordanian corporations. By integrating these domains into a single model, it contributes to governance literature while offering practical recommendations for aligning sustainability and technology use with organizational success. 1. INTRODUCTION Corporate governance, ESG practices, AI-driven decision-making, and organizational performance are interconnected, influencing the modern corporate landscape. Corporate governance, as a bedrock in the corporate world, is no longer a new concept, as the notion underscores an accountability mechanism and responsible business practices that run counter to the broader interests of the organization (Kim & Li, 2021). Organizations that have sound governance find that it enables them to reap the benefits of reduced financial risk, increased investor confidence, better decision-making, and greater shareholder power. Conversely, with greater regulatory pressures, (Hesamzadeh, Hosseini, & Rezaei, 2023) and increased stakeholder demands (Baba, Nestor, & Chen, 2023) and environmental risks Humanities and Social Sciences Letters 2026 Vol. 14, No. 1, pp. 20-43 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v14i1.4545 © 2026 Conscientia Beam. All Rights Reserved. mailto:a.alrowwad@ju.edu.jo mailto:moh.momani@student.usm.my https://orcid.org/0009-0009-5588-2024 https://www.doi.org/10.18488/73.v14i1.4545 Humanities and Social Sciences Letters, 2026, 14(1): 20-43 21 © 2026 Conscientia Beam. All Rights Reserved. (Bai, Satir, & Sarkis, 2023) facing organizations, they are increasingly being forced to adopt Environmental, Social, and Governance (ESG) guidelines to drive their strategies (Câmara, 2022). Corporate governance establishes the framework for decision-making processes. In light of the above, ESG activities (Kottagoda & Gamage, 2024) ensure that firms undertake activities that conform to their responsibilities to society and the environment. Simultaneously, the introduction of AI-led decision-making has brought a positive influence in terms of how effectively governance is established by leveraging data insights, transparency, predictive analytics, and automating compliance processes (Zhang, Chen, & Liu, 2022). AI-driven governance allows firms to make strategic decisions, identify fraud, and enhance corporate risk assessments (Kapoor & Agarwal, 2025), further strengthening governance impact. Yet, deploying Artificial Intelligence (AI) in governance systems raises challenges, such as algorithmic bias, regulatory issues, and ethical dilemmas (Dubey, Bryde, & Fynes, 2020). Therefore, the nature of the interaction between corporate governance and organizational performance is no longer linear. It is increasingly mediated by ESG practices and moderated by AI-supported managerial decision-making. Such interconnectedness brings about a need for greater exploration of what the governance impact on performance is, how the arithmetic disperses with the meditative effects of ESG, and whether the modifying role of AI offers more effective governance (Al‑Zaqeba, Hamid, Khairi, Mursidi, & Melhem, 2024; Qahman, Al-Zaqeba, Jarah, Al-Kharbsheh, & Assaf, 2025). Corporate governance is perceived as a potential mechanism to drive financial stability and corporate success; however, the effect of corporate governance on organizational performance is mediated through the interplay of numerous organizational factors (Saygili, Arslan, & Birkan, 2022). New governance paradigms are needed to confront contemporary dilemmas, including stakeholder activism, sustainability, and digital transformation (Kapoor & Agarwal, 2025). Traditional governance ultimately fails to cope with these challenges, requiring a transition towards ESG and AI governance. While the adoption of AI technologies in governance frameworks is rising, issues of transparency, ethical governance, and regulatory compliance remain (Dubey et al., 2020). However, AI is often considered an efficiency and decision-making accuracy tool rather than a moderator in the governance-performance relationship (Elamer & Boulhaga, 2024). On the other hand, ESG practices correlate positively with financial performance and corporate resilience; there is a need for more empirical evidence on the extent to which they mediate the governance-performance relationship (Kottagoda & Gamage, 2024). Organizations that embrace ESG in governance models demonstrate improvements in financial viability, goodwill, and adherence to regulatory processes. This is supported by the work of Câmara (2022), which highlights the discretionary adoption of ESG frameworks across organizations, shaped by external regulations and industry-specific factors. Therefore, the research problem is to understand the impact of corporate governance on organizational performance and how ESG practices and AI in decision-making influence this relationship. The first motivation of this study lies in the increasing concern about corporate governance as an institutional pillar for a firm's success and the role of AI-based decisions and ESG practices in changing the mechanism of effective governance. As organizations face increasing scrutiny from regulators, growing pressures of sustainability targets, and technological advances, there is a need to understand better how governance structures can underpin performance (Maabreh, 2024; Qahman & Abdu, 2024). AI integration into governance frameworks has proved to be a transformative factor, optimizing risk management, compliance monitoring, and strategic decision-making (Zhang et al., 2022). However, its function through the prism of governance-driven performance is relatively untapped, especially in the context of ethical AI deployment, algorithmic biases, and human oversight (Kapoor & Agarwal, 2025). Likewise, ESG practices are no longer optional for long-term corporate success, with companies prioritizing sustainability and ethical accounting practices demonstrating excellent financial durability, enhanced stakeholder trust, and decreased regulation risk (Câmara, 2022). Nevertheless, ESG does play a positive role in contributing to performance. However, whether it mediates the relationship between governance and performance is still debated and requires further empirical justification (Kottagoda & Gamage, 2024). While existing studies focus on corporate governance and AI or ESG separately, the relationships among these elements require a holistic approach Humanities and Social Sciences Letters, 2026, 14(1): 20-43 22 © 2026 Conscientia Beam. All Rights Reserved. (Al‑Zakwani, Al‑Zaqeba, Bashatweh, Al‑Refai, & Abdo, 2025; Falaha, 2024; Giachino, Cepel, Truant, & Bargoni, 2024; Lootah, 2024). This research aims to fill this gap by deriving an integrated governance-performance framework, where we will examine governance structures as drivers of performance, the mediating effect of ESG on this relationship, and the moderating effect of AI on the governance-performance relationship (Al‑Jamal, 2024; Shboul, 2024). This study fills these gaps and is essential for corporate governance literature and business strategy by providing insights on how firms can ensure efficient governance, improve sustainability practices, and integrate AI- led decision-making, leading to improved performance in a changing corporate landscape. 2. LITERATURE REVIEW The importance of effective governance structures in ensuring ethical decision-making and responsible finance has gained attention in organizations of all sectors and sizes, particularly from the perspective of an organization’s long-term sustainability (Kim & Li, 2021). However, governance alone cannot explain, as other factors, such as involvement in ESG and technological development, are also important (Saygili et al., 2022). Recent research highlights the mediating role of ESG activities in governance performance; better ESG policies are followed by increased investor trust, greater risk awareness, and, ultimately, superior financial performance (Câmara, 2022; Kottagoda & Gamage, 2024). Besides these trends, the impact of AI-based decisions on governance effectiveness has led to improvements in compliance monitoring, strategic agility, and fraud detection (Zhang et al., 2022). The emergent behavior of AI with firm governance serves as a moderator that intensifies the influence of the governance mechanism on organizational performance by enhancing the quality of choices, eliminating unnecessary routines, and gathering on-the-spot feedback (Kapoor & Agarwal, 2025). Nevertheless, problems such as algorithm bias, ethical concerns, and the absence of regulation were still raised, inviting reflection on a proper trade-off to be made between using AI and manual government at the corporate level (Dubey et al., 2020; Elamer & Boulhaga, 2024; Zaqeeba, 2024). The originality of this paper lies in its merger of three distinct domains: governance, ESG, and AI. It constructs a conceptual framework that describes why governance affects performance, how ESG modulates this link, and how AI is reshaping the efficacy of governance as a business necessity for more complex organizations. 2.1. Organizational Performance Organisational performance is a broad construct that includes success in achieving financial, operational, and strategic objectives. You can even go broad and say this is a wicked problem, with drivers ranging from corporate governance and ESG to bad AI decision-making, rooted in the GIGO philosophy (Garbage In, Garbage Out), and all of these factors either cause or affect the problem. Scholars have studied this in more than enough detail, you know, showcasing the trade-offs of embedding them inside business models. Corporate governance is widely recognized as a crucial determinant of organizational performance and has an impact on the channels of decision-making, risk management, and relationships with stakeholders. Good governance practices have been found to have a positive influence on financial stability, investor confidence, and operational efficiency (Kim & Li, 2021). Saygili et al. (2022) examined the impact of corporate governance on firm performance in the Borsa Istanbul. It concluded that separate boards, robust audit committees, and clear disclosures lead to better financial results. These findings are consistent with agency theory, Jensen and Meckling (1976), which posits that governance structures help mitigate agency problems between shareholders and managers, thereby enhancing efficiency and profitability. Sotonye, Lateef, and Ene (2024) also examines the impact of board size, audit committee independence, and the profitability of manufacturing firms. The second study demonstrates a strong positive relationship between CGQ and ROCE and NPAT, highlighting the relevance of governance mechanisms in financial sustainability. However, the implementation of governance models might differ among regulatory and market environments, indicating that a standardized governance model cannot be generally applicable across different economies. However, favorable outcomes notwithstanding, Bergh, Connelly, and Ketchen (2019) caution against overregulation, stating Humanities and Social Sciences Letters, 2026, 14(1): 20-43 23 © 2026 Conscientia Beam. All Rights Reserved. that overzealous regulation hampers decision-making speed and innovation. It also shows that there needs to be a careful balance between controlling how they spend the money in governance terms and still being able to be strategic in the longer term for their sustainability. In competitive markets, organizations may have become so overregulated by enforced governance that they are losing agility. In addition, Qahman et al. (2025) demonstrated that robust corporate governance systems are often associated with improved financial performance, reduced risk levels, and increased investor confidence. Kim and Li (2021) find that firms with strong ESG performance exhibit better economic performance, as indicated by figures such as Return on Equity (ROE) and Return on Capital Employed (ROCE). These results suggest that the increased adoption of ESG in the region may serve as a strategic lever for financial success when combined with effective governance. Through the incorporation of environmental sustainability, social responsibility, and transparent governance, firms can reduce the risks of regulation, attract long-term investors, and enhance brand image (Al‑Zaqeba et al., 2024). However, the way ESG practices impact organizational performance varies by industry. Dubey et al. (2020) propose that ESG integration can be effective in resource-intensive sectors such as energy and finance. However, its effectiveness relies on the rules and consumer sustainability preferences in service-dependent industries. This diversity makes it challenging to generalize financial returns about any ESG issues. It implies that ESG policy interventions in (e.g.) the finance sector need to be sector-specific rather than industry-wide. A critical obstacle to relating ESG practices to performance is the lack of standard ESG performance metrics. Elamer and Boulhaga (2024) for different ESG rating systems and governance constructs, leading to performance assessment discrepancies and consequently, shortages in comparison across industries. They call for standardized global corporate sustainability indexes to provide comparability, transparency, and reliability of claims made in ESG reporting. AI has transformed the way businesses make decisions, from strategic planning to operational efficiency and financial forecasting. By utilizing AI methods to inform decision-making, companies can leverage big data, identify potential risks, and allocate resources more efficiently, ultimately leading to improved financial performance and market longevity (Zhang et al., 2022). AI-facilitated predictive analytics drive corporate decision-making that results in increased sales and operational efficiency (2016). The AI-powered tools help firms analyze financial markets, consumer trends, and supply chain performance in real-time, mitigating risks and increasing opportunities. As a result, businesses can make swift and effective strategic decisions by gaining insights from data. Conversely, Kapoor and Agarwal (2025) suggest that AI-derived insights can be beneficial for superior financial forecasting and risk management. Meanwhile, companies that had adopted AI for investment decisions experienced little financial turbulence and provided investors with more trust. It is also consistent with the notion that AI contributes to risk resiliency, a key factor in driving organizational performance over time. But the impact of AI on performance varies depending on the industry and the organizational model of the firm. Dubey et al. (2020) argue that both AI governance and data-driven ESG strategies are particularly beneficial for capital-intensive industries. In contrast, their application in service-oriented firms depends on consumer demand and regulatory constraints. Therefore, AI-powered decision-making must focus on solving business-defining problems to drive the most value. The ethical liabilities of algorithmic bias and data are considerable barriers to AI-based decisions. Elamer and Boulhaga (2024) warned that over-reliance on AI models can lead to biased decision-making, as seen in recruitment, credit scoring, and risk prediction, highlighting the need for accountability and human oversight over corporate decisions. Strong governance and ESG integration create a solid basis for financial stability, risk management, and sustainable performance over time. AI will continue to make those strategic decisions in the future, helping firms react to market changes. Nonetheless, the extent to which these factors matter differs across sectors, regulatory regimes, and governance structures. Humanities and Social Sciences Letters, 2026, 14(1): 20-43 24 © 2026 Conscientia Beam. All Rights Reserved. 2.2. Corporate Governance Corporate governance is considered one of the touchstones of enterprise performance, delivering transparency, strategic oversight, risk management, and stakeholder stewardship. The skeletal system shapes how decisions are made, promotes transparency, and guides ethical conduct throughout the organization. Good corporate governance improves financial stability, investor confidence, and operational efficiency, among other positive outcomes (Kim & Li, 2021). Effective governance structures lead to increased transparency, decreased information asymmetry, and better strategic oversight (Câmara, 2022). Academic evidence establishes a direct correlation between governance quality and financial performance. Saygili et al. (2022) examined the impact of CG practices on the economic performance of firms listed on Borsa Istanbul. They provided evidence that independent boards, solid audit committees, and transparency in reporting lead to better performance in firms, specifically in developing nations. The results follow the study of agency theory, Jensen and Meckling (1976), which observed that good governance can reduce conflicts among managers and shareholders. Through better governance, company strategies can be more suitable for investor expectations. Similarly, (Kuzey, Al-Shaer, Karaman, & Uyar, 2023), the micro-level functioning of corporate governance depends on macro-level public governance apparatuses that support or hinder its effectiveness, which is especially significant in countries where formal institutions are well established. Bergh et al. (2019) even issued a caution that tight governance can block decision-making agility and stifle innovation. Governance models must strike a balance between oversight and the ability to pivot, when necessary, to compete or innovate. Saygili et al. (2022) found that companies with non-executive boards and active audit committees have a better Return on Equity (ROE) and greater shareholder confidence. This is consistent with agency theory, which posits that board independence can mitigate managerial opportunism and improve corporate governance. Supporting this line of argument, Sotonye et al. (2024) conducted a study on Nigerian manufacturing companies, demonstrating that board size and audit committee independence are positively linked to Return on Capital Employed (ROCE) and Net Profit After Tax (NPAT). Nevertheless, the study also cautioned that a large board size was detrimental to the efficiency of the decision-making process, suggesting that board structure is critical to attaining effective governance (Bergh et al., 2019). The CEO as also board chair is a very contested matter in governance literature. Câmara (2022) contends that duality may increase strategic alignment, bringing about a reduction in accountability. While some companies prosper under centralized control, other such firms suffer from conflicts of interest and lack of independent oversight. In contrast, Paganou, Antoniadis, Zournatzidou, and Sklavos (2024) investigated CEO duality in the family firm context; they found that CEO duality leads to governance failures and the risk of corruption in family firms where public regulation is ineffective. This indicates that the influence of CEO duality is contingent on the industry structure, ownership composition, and governance enforcement. ESG principles are increasingly integrated into corporate governance in response to growing stakeholder and regulatory demands. Robust governance frameworks drive ESG accountability, from ethical supply chains to sustainable practices and clear reporting on ESG risks and opportunities. Câmara (2022) proposes the notion of the “ESG cascade effect,” which describes how well-governed firms pressure their investors, suppliers, and competitors to adopt ESG policies. Richis, thanks to this systemic interaction, we can see that corporate governance influences industry-wide sustainability trends beyond internal compliance. Examined corporate governance as a mediator in the relationship between sustainability disclosure and financial performance in Swedish companies, Kottagoda and Gamage (2024) showed that firms with firm governance structures have more investor confidence, resulting in higher stock market valuations due to higher transparency regarding ESG. This helps stakeholder theory, which states that businesses must balance financial goals (profit motive) with social and environmental responsibilities to achieve long- term sustainability. Nonetheless, governance challenges remain in implementing ESG. Kumar, King, and Ranta (2024) highlight that many companies face challenges complying with this ESG legislation due to difficulties with evolving regulations and high implementation costs. Elamer and Boulhaga (2024) also discuss ESG controversies, Humanities and Social Sciences Letters, 2026, 14(1): 20-43 25 © 2026 Conscientia Beam. All Rights Reserved. including greenwashing, whereby firms present distorted views of relevant sustainability claims to enhance their public image without taking actual significant action towards promising to act in a cohesive, transparent, and sustainable manner for the people and the planet. This highlights the need for robust governance oversight to ensure ESG authenticity. AI is revolutionizing how governance structures handle risk, compliance, and strategic decisions. Artificial Intelligence improves governance efficiency by automating audits, reducing fraud, and analyzing financial trends. Zhang et al. (2022) found that firms utilizing AI in decision-making lead to better accuracy, decreased compliance costs, and improved fraud detection. Such an assertion aligns with Kapoor and Agarwal (2025), who also suggests that using AI-powered governance minimizes the probability of fluctuations arising from human biases and, therefore, enhances the effectiveness of operations. Yet, the prospect of increasing AI reliance in governance remains a concern. Dubey et al. (2020) and Dubey, Gunasekaran, Bryde, Dwivedi, and Papadopoulos (2020) warn that heavy dependence on AI could harm managerial responsibility and present algorithmic prejudices. In the same vein, Elamer and Boulhaga (2024) caution that obtuse algorithms complicate governance transparency, which creates ethical AI integration as an undisputed challenge before corporate boards. Challenges aside, AI has great potential for improving governance, especially in regulatory compliance, risk assessment, and ESG performance monitoring (Giachino et al., 2024). Risk management is a key part of corporate governance through which firms identify, evaluate, and minimize the impact of financial, operational, and reputational risks. Research indicates that companies with sound governance structures are better positioned to navigate through economic crises and regulatory ambiguities. Sotonye et al. (2024) focused on board oversight and audit committees and how they prevent out-of-control financial risk, and concluded that businesses with active governance frameworks have less financial volatility and credit risk. Kim and Li (2021) also proposed that high-quality CG can affect a firm’s ability to survive economic crises by pursuing sound financial policies and effective internal control. Furthermore, Câmara (2022) asserts that new risks, including cybersecurity threats, geopolitical instability, and ESG non- compliance failures, necessitate a transformation in governance mechanisms. To fit effectively with today’s reality, governance approaches must deploy modern software-based risk assessment tools for real-time monitoring. For instance, Elamer and Boulhaga (2024) analyzed how corporate governance affects firm resilience during the COVID- 19 pandemic. The findings indicated that well-governed firms could better account for the impact on their supply chains and those of their regulators. However, they also found that some management models were too inflexible, which hindered swift decision-making in fast-moving markets. This, in turn, suggests that governance frameworks should be developed to manage strategic capabilities and be flexible enough to accommodate new strategic conditions. AI is being integrated into governance models for new risk management strategies, compliance monitoring procedures, and informed strategic decision-making. The governance efficiency was enhanced through the automation of AI in audits, fraud detection, and financial trend analysis. Zhang et al. (2022) found that AI-generated decisions can improve corporate governance, making decisions more accurate, and reduce compliance costs, while also enhancing fraud detection for firms. This aligns with Kapoor and Agarwal (2025), who argue that AI-enabled governance is bias-free, as opposed to human decision-making, and also makes the operation more efficient. However, there are lingering concerns about the use of AI in governance. Dubey et al. (2020) caution that an over-reliance on AI can decrease managerial responsibility and inject biases from the algorithms. Similarly, Elamer and Boulhaga (2024) advise that non-transparent AI operating systems may undermine the transparency of governance, a core challenge for corporate boards to safeguard the ethical integration of AI. Yet, notwithstanding these limitations, AI remains a powerful tool that can enhance governance, especially in areas such as regulatory compliance, risk analysis, and ESG performance monitoring (Giachino et al., 2024). These serve as evidence that corporate governance is a dynamic field of study. Why governance is key. Good governance is crucial for financial stability, risk optimization, and ESG compliance, while AI-driven decision-making can enhance governance efficiency. Opportunities include Humanities and Social Sciences Letters, 2026, 14(1): 20-43 26 © 2026 Conscientia Beam. All Rights Reserved. integrated solutions, improved governance across the lifecycle, addressing regulatory overreach, enhancing ESG credibility, and mitigating AI bias. 2.3. ESG Practices ESG practices are becoming a strategic necessity for corporate sustainability, ensuring financial performance, managing risks, and creating long-term value. ESG sector frameworks help organizations manage their environmental impact, social responsibility, and governance structure. This makes ESG practices vital to corporate resilience, reputation, and stakeholder trust. Financial performance and risk mitigation are generally better in companies with good ESG policies (Kim & Li, 2021). In exploring the relationships between corporate governance determinants and ESG adoption perceptions, Kottagoda and Gamage (2024) found that firms with independent boards, gender diversity, and shareholder-driven governance structures are more likely to practice transparent ESG disclosures. Their study proves that robust corporate governance cultivates a culture of sustainability and corporate social responsibility consistent with stakeholder theory. Câmara (2022) introduced the ESG cascade effect, whereby organizations that adopt ESG frameworks drive their supply chain, investors, and competitors to adopt sustainable initiatives. The systemic impact of ESG demonstrates that it is not simply a corporate function but a wide-reaching and company-contextual strategy that touches on market prices. However, difficulties in implementing ESG persist strongly in terms of sustainability in the market. Kumar et al. (2024) suggest that governance frameworks must be flexible to adapt to and incorporate new ESG regulations to avoid compliance becoming burdensome through bureaucracy. Moreover, Elamer and Boulhaga (2024) caution that the increased exposure of firm-level ESG controversies like greenwashing can erode corporate credibility if firm governance mechanisms fail to guarantee ESG authenticity. The interplay between corporate governance, ESG participation, and organizational performance has become a prominent area of exploration. Recent research also suggests that ESG activities moderate the relationship between governance mechanisms and financial performance, as well as sustainability performance. Lee and Hooy (2024) investigate the interrelationships between CEO power, ESG performance, and managerial risk-taking. It suggests that ESG performance, as a complex formation, can be influenced by corporate risk-taking policies associated with the CEO's power, and risk-taking serves as an intermediate variable. This study also suggests the need to examine internal governance mechanisms to assess the extent to which ESG practices are implemented. Similarly, Giachino et al. (2024) research the interrelationship between ESG activities and financial performance at the corporate level, with a focus on green innovation in UK and German companies. They find that green innovation has a full mediation effect on the relationship between ESG and financial performance, further supporting the notion that sustainability integrity leads to competitive advantage and economic benefits. E3S Web Conf. (2023) also argues that technological innovation moderates the association between ESG practices and environmental performance. They demonstrate that companies that apply ESG in their technological advancement practices in Morocco cause significant environmental effects, emphasizing the role of tech-driven sustainability. Corporate governance is material because it impacts the adoption and effectiveness of ESG. In further analyzing governance influence, Kumar et al. (2024) compared existing literature about the nature of ESG practices and corporate governance characteristics and how they affect one another. They found that board diversity, CEO traits, and ownership structure were primary drivers of engagement with ESG factors. Such trends agree with the stakeholder theory, which serves as the theoretical rationale for this study. The study findings indicate that companies with diverse boards and independent directors are likelier to internalize corporate decision-making according to ESG principles. However, there are barriers to ESG adoption at both the regulatory and ethical levels. A study by Elamer and Boulhaga (2024) warns against greenwashing, which is one of those terms that makes firms make their sustainability efforts look better to the public through misleading Humanities and Social Sciences Letters, 2026, 14(1): 20-43 27 © 2026 Conscientia Beam. All Rights Reserved. communication and not have real ESG investing. These are clear implications regarding governance since stricter oversight is needed to drive more transparency in the ESG reporting space. The relationship between ESG incorporation and Corporate Financial Performance (CFP) is highly debated. There is evidence of a positive relationship in some studies, although others focus on the income trade-offs between implementing sustainability-related projects and short-term profits. A recent meta-analysis of ESG-CFP literature was conducted (Kim & Li, 2021). The findings suggest that firms with higher ESG scores are expected to perform better financially, as indicated by higher ROA and ROE. The benefits are derived from risk management, investor confidence, and brand reputation, according to the study. Similarly, Saygili et al. (2022) reported that companies with better ESG practices had a greater stock market valuation and lower financial volatility. Companies with higher ESG performance showed lower exposure to legal risks and regulatory fines, achieving long-term economic stability. Perdana, Sudiro, Ratnawati, and Rofiaty (2023) examined the effect of sustainable finance and ESG disclosure on the firm value of banks in ASEAN. Assuming that ESG switching will lead to an improvement in credit ratings and lower borrowing risk, their results suggest that value creation occurs for firms when ESG switching takes place. These types of investment are consistent with the resource-based view (RBV) (Barney, 1991) and can result in innovation, competitive advantage, and improved risk resilience. ESG compliance: ESG, and particularly ESG reporting, is increasingly driven (and in some cases mandated) by regulation and it turns out that AI systems can monitor sustainability index assurance and track the carbon footprint across the supply chain by ensuring it does not exceed an allowed threshold, run the ethical supply chain, and much more (Giachino et al., 2024). Zhang et al. (2022) explored the applicability of AI in ESG governance, concluding that companies implementing AI in sustainability initiatives were more likely to comply, with a lower frequency of reporting errors and a better ability to track environmental impact. Their findings align with Kapoor and Agarwal (2025), who suggests that AI-generated ESG strategies contribute to a firm's resilience and corporate reputation. However, there are concerns about relying on AI. Dubey et al. (2020) caution that the growing reliance on AI in ESG reporting could lead to algorithmic biases and diminish managerial accountability. Additionally, as Elamer and Boulhaga (2024) note, opaque, non-transparent AI algorithms pose a challenge to governance transparency, raising ethical concerns in the context of automation decision-making for sustainability. However, notwithstanding these challenges, AI is a powerful tool for advancing ESG governance, especially in real-time measurement of carbon footprint, predictive risk analysis, and automated sustainability audits (Giachino et al., 2024). ESG, therefore, matters for corporate sustainability, financial strength, and risk mitigation. Strong governance structures support meaningful ESG integration, helping to protect against cases of greenwashing and non-compliance. As AI overhauls ESG reporting and monitoring, concerns remain about transparency and ethics. 2.4. AI-Driven Decision Making AI is the next era in corporate governance, strategic management, and ESG, enabling decision accuracy, bias reduction, and real-time adaptation. Governance tools utilizing Acanld to automate compliance monitoring, optimize resource allocation, and enhance the accuracy of financial projections, thereby providing companies with a competitive edge in turbulent business conditions (Kapoor & Agarwal, 2025). Recent research has also found that AI can serve as a moderator between corporate governance and firm performance, thereby strengthening corporate risk management, operational efficiency, and long-term success. At this point, some key studies on AIs as decision-makers, considering the methodologies used, results, and theoretical issues, have been examined. AI could potentially revolutionize corporate governance models by introducing greater transparency, enhancing monitoring of compliance, and improving accountability. AI-enabled systems will enable the tracking of financial transactions in real-time, and predictive analytics can be leveraged to analyze risk, facilitating automated regulatory compliance that mitigates governance failures and financial misbehavior (Zhang et al., 2022). Humanities and Social Sciences Letters, 2026, 14(1): 20-43 28 © 2026 Conscientia Beam. All Rights Reserved. A longitudinal study of the moderating effect of AI on the relationship between corporate governance mechanisms and firm performance (Zhang et al., 2022). Zhang et al. (2022) also illustrate that firms deploying AI- driven governance tools enjoy superior regulatory compliance, fewer frauds, and enhanced solvency. AI can detect cases of non-compliance with legislation or regulations more quickly than traditional forms of oversight, and has a beneficial impact on governance, enabling more real-time and preventive decision-making. In this vein, (Kapoor & Agarwal, 2025) investigated the use of AI-based risk managers by financial institutions. They concluded that AI- based governance leads to more effective board discussions, improved audit processes, and reduced human bias in regulatory compliance. However, they caution against relying too heavily on AI in governance, which could introduce new challenges, such as ethical quandaries over AI-based regulatory enforcement and the erosion of human judgment in corporate decisions. Despite these benefits, the application of AI to corporate governance presents challenges. Dubey et al. (2020) highlight the risk of algorithmic bias in AI governance frameworks by warning that decision- making systems supported by AI can further bias in access to employment, allocation of investment, granting of credit, etc. They argue that this can be achieved by promoting human oversight of AI, suggesting that humans should make moral and strategic judgments, with AI augmenting their capabilities. With the advent of AI and big data analytics, the current era of ESG strategies can collect a larger volume of sustainability data, aiming to move towards a one-stop shop for optimizing resource usage and forecasting environmental risks. AI-powered ESG data services enable companies to automatically measure their impact, detect malpractices in ESG reporting, and automate compliance with regulations, thereby optimizing the sustainability of their performance and earning trust from stakeholders (Giachino et al., 2024). Giachino et al. (2024) demonstrated the impact of AI-powered analytics on ESG performance, indicating that machine learning models could enhance ESG reporting accuracy by detecting inconsistencies in sustainability disclosure. AI enables ESG compliance tracking with NLP and big data analytics, cutting out the legwork (manual audits and sustainability assessments). Similarly, Mukhtar, Khan, Mubarik, and Ali (2024) studied the role of AI as a driver of green innovation. They found that AI- driven ESG disclosures build investor confidence, reduce the risk of greenwashing, and ensure compliance with the mandates of global sustainability. Their results are also congruent with stakeholder theory (Freeman, 1984), which suggests that firms that eschew transparent ESG practices may garner longer runs of stakeholder trust and other paths to financial health. But questions linger around the ethics of AI-driven ESG reporting. Elamer and Boulhaga (2024) caution that firms use sharp practices to manipulate AI-based ESG scores in order to meet thresholds and regulatory requirements, thereby undermining the credibility of ESG.. Using artificial intelligence (AI), firms can predict financial crashes, identify instances of fraud, and build cyber risk management strategies at a rate faster than ever experienced. By processing real-time financial data, predictive models driven by AI allow companies to take a proactive approach to fluctuating market conditions and increasing risks (Dubey et al., 2020). Dubey et al. (2020) proposed a combination of AI algorithms and market risk analysis, reporting the existence of early warning signals for defaults that reduced bankruptcy risk. The research also found that AI helps companies make more accurate market predictions, enabling them to make investment decisions based on data rather than market volatility. Kapoor and Agarwal (2025) also explored the following domain: artificial intelligence and fraud detection in banks and financial companies, and claimed that AI-powered anomaly detection systems proved to be better at detecting fraudulent transactions compared to conventional auditing methods performed by people. The findings demonstrate the fraud prevention potential of AI-driven audit by increasing corporate accountability, reducing financial crime, and reinforcing investor confidence. However, just as in previous scenarios, the extensive use of AI in risk management opens new threats. Zhang et al. (2022) demonstrated that AI- based risk models can be manipulated through biased data, resulting in misleading financial predictions. In addition, cyberattacks targeting AI-based financial systems pose a significant risk, requiring more robust regulatory protection for the use of AI in financial risk control. Humanities and Social Sciences Letters, 2026, 14(1): 20-43 29 © 2026 Conscientia Beam. All Rights Reserved. AI is providing the next wave of strategic planning for businesses with a powerful, streamlined approach for resource optimization and decision-making that is more intelligent. This capacity to make hyper-precise decisions with AI to help manage the workforce, scale operations, and foster long-term business resilience is part of the company’s competitive differentiators as well as financial resilience (Marchegiani, 2023). Marchegiani (2023) examined the process of strategic planning optimization and real-time decision-making use in MNCs through AI and discovered that the use of AI-based real-time decision-making is associated with increased productivity, profitability, and market adaptability in AI-using companies. Organizations can use AI to track trends in the global economy, streamline supply chains, and engage their workforce more effectively. Similarly, Saci, Chien, and Chien (2024) investigated the ability of AI to assist companies in their resilience against a macroeconomic slowdown. They discovered that AI-supported decision-makers enhance a firm's ability to adapt and address financial shocks and industry disruptions. Such findings support the dynamic capabilities theory (Teece, Pisano, & Shuen, 1997), which posits that a firm creates a sustainable competitive advantage through the processes by which it quickly responds to market changes. There are concerns about overdependence on AI in decision-making, but Kapoor and Agarwal (2025) caution that overreliance on AI diminishes human creativity, strategic intuition, and ethical decision-making. Furthermore, Dubey et al. (2020) emphasize the importance of frequently updating AI-driven decision-making models, as relying on outdated algorithms can lead managers to take suboptimal strategic actions. Artificial intelligence has emerged as a critical mediator of corporations and ESG programs and firm performance (P) factors. AI fosters effective government structures, sustainability targets, and good financial results by enhancing data accuracy, regulatory compliance, and future strategic planning (Câmara, 2022). Zhang et al. (2022) emphasized the importance of sound governance systems in the integration of AI, which results in increased decision-making efficiency, regulatory compliance, and operational efficiency. The findings also show that the performance gains on the KPI are greater when AI's capabilities are larger in relation to the governance system, which reinforces the need to adopt more advanced AI-based governance frameworks. Additionally, AI can serve as a tool for monitoring ESG performance. Giachino et al. (2024) stated that AI-derived insights help firms better identify sustainability risks and opportunities, leading to stronger ESG results. Big Data and machine learning algorithms can sift through and process extensive quantities relevant to environmental impact, social responsibility, and Governance, resulting in more accurate and transparent reporting. However, there are still ways to go when it comes to AI-enabled governance and ESG compliance. Dubey et al. (2020) argue that in rapidly changing markets where the technologies of AI are evolving quickly, it becomes difficult to regulate ethical AI and guarantee the transparency of algorithms. Although AI contributes to improved governance and performance of ESG, it has also raised concerns, particularly in data privacy, AI bias, and regulatory monitoring (Elamer & Boulhaga, 2024). Analytical suggestions being weighted with representative AI insights result in a process-driven approach to workplace strategies translated to better overall corporate governance, ESG (Environment, Social, and Governance), and financial performance. Artificial intelligence facilitates real-time monitoring, predictive risk assessment, and strategic decision-making to enhance organizational resilience and regulatory compliance. To leverage AI to its full potential, however, organizations must build governance models that balance AI efficiency with human oversight, ethical AI frameworks, and regulatory precautions that protect organizations so that AI can be responsibly adopted and make appropriate business sense. 3. HYPOTHESIS DEVELOPMENT Existing studies indicate that effective corporate governance can promote ESG adoption (Kottagoda & Gamage, 2024) by enhancing ethical business practices, transparency about business decisions' environmental and social impact, and sustainability-oriented actions (Câmara, 2022). The ESG practices have, in turn, been associated with positive organizational performance through better risk management, more substantial investor confidence, and improved regulatory compliance (Kim & Li, 2021; Saygili et al., 2022). Moreover, the cross-sectional usability AI can Humanities and Social Sciences Letters, 2026, 14(1): 20-43 30 © 2026 Conscientia Beam. All Rights Reserved. drive is forecasted to offer a transformational factor to governance frameworks, providing solutions that have driven data-based implications and are capable of data automation and predictive analytics that market governance effectively (Zhang et al., 2022). Based on these relationships, this study proposes hypotheses that examine the direct impact of corporate governance on both ESG and performance, the mediation of ESG, and the role of AI-driven decision- making as a moderating factor in the corporate governance outcome. It aims to establish a holistic framework that aligns governance, sustainability, and AI-embedded innovation to help organizations chart courses that lead to their success. 3.1. Corporate Governance and ESG Practices Extensive literature exists about the nexus of corporate governance and ESG practices. Corporate governance offers the framework through which ESG principles can be well-implemented and monitored. Implementing robust governance mechanisms, including independent boards, transparent reporting, and active audit committees, fosters ESG adoption by promoting accountability and adherence to standards while ensuring stakeholder trust (Marchegiani, 2023; Pucheta-Martínez & Gallego-Álvarez, 2020). This is aided by stakeholder theory, which highlights that firms must help and manage to provide the best higher growth towards performance where both social and environmental supports can be provided (Freeman, 1984). The ESG cascade effect proposed by (Câmara, 2022) describes how companies with robust governance systems shape their supply chains and competitive ecosystems to establish sustainable behaviors that enhance the impact of ESG initiatives. Nonetheless, issues like greenwashing and the impact of distinct governance features (e.g., board tenure, CEO influence) on the incorporation of ESG underline the requirement for dynamic governance frameworks (Elamer & Boulhaga, 2024; Kumar et al., 2024). For example, Kottagoda and Gamage (2024) and Saygili et al. (2022) empirically demonstrate that firms with strong governance tend to pursue transparent ESG reporting and long-term goals, supporting the governance-ESG positive relationship. Literature thus robustly supports the hypothesis that corporate governance positively impacts ESG practices. Thus, the proposed hypothesis is as follows: H1: Corporate Governance Positively Affects ESG Practices. 3.2. ESG Practices and Organizational Performance Environmental, Social, and Governance (ESG) practices have become vital indicators of organizational success, shaping financial performance, risk mitigation strategies, and corporate reputation. Companies that take ESG initiatives seriously also tend to have higher profitability, market valuation, and long-term resilience (Ioannou & Serafeim, 2015; Kim & Li, 2021). This stakeholder theory has its roots in Freeman (1984), which suggests that ESG practices improve trust and ethical practices in business, all of which positively impact organizational performance. Other empirical studies, including Kim and Li (2021) and (Saygili et al., 2022) show that firms with strong ESG frameworks outperform their peers in financial metrics, such as ROA, ROE, and stock market valuation. However, issues like regulatory complexities, high compliance costs, and the risks of greenwashing highlight the necessity of effective governance for substantiating the sincerity of ESG engagement (Elamer & Boulhaga, 2024; Kumar et al., 2024). The resource-based view (RBV) additionally emphasizes that investments in environmental, social, and governance (ESG) domains result in a competitive edge in terms of innovation and risk aversion (Barney, 1991). Consequently, there is a strong empirical basis for the statement that ESG practices positively impact the organization's overall performance. Therefore, the following is the hypothesis: H2: ESG practices positively affect organizational performance. 3.3. Corporate Governance and Organizational Performance Corporate governance is an essential factor driving the organization's health, effective decision-making, risk management, and corporate stability. A well-developed governance framework improves financial performance, Humanities and Social Sciences Letters, 2026, 14(1): 20-43 31 © 2026 Conscientia Beam. All Rights Reserved. strengthens investor confidence, and enhances competitive advantage (Kim & Li, 2021; Saygili et al., 2022). In particular, agency theory posits that governance mechanisms align managerial interests with those of shareholders, alleviating conflicts and enhancing efficiency (Jensen & Meckling, 1976). On the other hand, governance committees can be too far removed from the business and become bureaucratic, so a balanced approach is needed (Bergh et al., 2019). Empirical works like Kim and Li (2021) and Saygili et al. (2022) propose that companies with independent boards, an efficient audit committee, and transparent reporting processes achieve better financial performance. However, governance is determined by much more than ownership factors, including the board's composition, regulatory environments, and strategic flexibility. Therefore, there is considerable empirical evidence supporting the hypothesis that corporate governance has a positive impact on organizational performance. Consequently, the hypothesis is as follows: H3: Corporate governance positively affects organizational performance. 3.4. ESG Practices as Mediator The mediating role of ESG practices in the relationship between corporate governance and organizational performance is increasingly recognized in the literature. Strong governance frameworks establish the foundation for ESG integration, enhancing financial and operational outcomes (Câmara, 2022; Pucheta-Martínez & Gallego-Álvarez, 2020). The ESG cascade effect illustrates how governance-driven ESG initiatives influence industry-wide sustainability practices, reinforcing corporate resilience and long-term performance (Câmara, 2022). Empirical studies, such as those by Kottagoda and Gamage (2024) and Saygili et al. (2022), demonstrate that firms with strong governance and ESG practices experience higher stock valuations, reduced financial volatility, and improved corporate reputation. However, the credibility of ESG as a mediator depends on governance integrity and regulatory oversight. Challenges such as greenwashing and algorithmic biases in AI-driven ESG monitoring highlight the need for transparent and ethical sustainability reporting (Dubey et al., 2020; Elamer & Boulhaga, 2024). Thus, the hypothesis that ESG practices mediate the effect of corporate governance on organizational performance is supported by substantial empirical evidence. Therefore, the proposed hypothesis is as follows. H4: ESG practices mediate the effect of corporate governance on organizational performance. 3.5. AI-Driven Decision Making as Moderator With the advent of a generative AI society (Ibrahima, Alsuraihib, & Al‑zaqebac, 2025; Kapoor & Agarwal, 2025; Zhang et al., 2022), AI-driven decision-making has rapidly become a game-changing component of corporate governance by increasing decision-making precision, improving risk assessment, and bolstering regulatory compliance. First, AI moderates governance and organizational performance by decreasing information asymmetry, preventing fraud, and facilitating strategic decisions (Giachino et al., 2024). More generally, there are empirical studies with, for example, Zhang et al. (2022) and Kapoor and Agarwal (2025) Observed that firms employing AI in governance positively influence financial performance as well as compliance risk reduction. Yet, problems like algorithmic biases and ethical dilemmas underscore the necessity for responsible AI integration into governance structures (Dubey et al., 2020; Elamer & Boulhaga, 2024). Therefore, the literature supports the argument that AI- based decision-making mediates the relationship between corporate governance and organizational performance. This observation leads to the hypothesis the following: H5: AI-Driven Decision-Making Moderates the Effect of Corporate Governance on Organizational Performance. 4. RESEARCH MODEL Based on existing literature, ESG practices and corporate governance have a synergistic relationship that may mutually reinforce each other. Figure 1depicts the conceptual framework outlining this relationship as hypothesized in prior sections. Empirical research shows that corporate governance is a key factor that leads to adopting ESG Humanities and Social Sciences Letters, 2026, 14(1): 20-43 32 © 2026 Conscientia Beam. All Rights Reserved. practices and, ultimately, organizational success (Kim & Li, 2021; Saygili et al., 2022). This paper goes further; ESG practices mediate any sturdy association between corporate governance and organizational performance as they improve risk aversion, sustainability, and stakeholder engagement (Câmara, 2022; Kottagoda & Gamage, 2024). Additionally, AI-driven decision-making moderates the governance-performance link, as it sharpens strategic planning, ensures compliance with regulations, and enhances operational efficiency (Kapoor & Agarwal, 2025; Zhang et al., 2022). AI improves governance effectiveness, facilitates fraud reduction, automates compliance monitoring, and provides real-time data insights, increasing decision-making agility (Dubey et al., 2020; Elamer & Boulhaga, 2024; Giachino et al., 2024). However, this research perspective highlights the synergistic relationship among governance, sustainability, and AI-supported decision-making, serving as a valuable framework to understand how these elements facilitate both enhanced organizational performance and sustained competitive advantage. Figure 1. Research model. 5. RESEARCH METHODS This study applies a quantitative research methodology to empirically justify the conceptual model and the proposed hypotheses on the relationship between corporate governance, AI-based decisions, ESG practices, and overall organizational performance. This paper investigates four critical components, including corporate governance (independent variable), organizational performance (dependent variable), AI-driven decision-making (moderating variable), and ESG practices (mediating variable). For example, corporate governance is defined by specific, well- established metrics of board structure, leadership transparency, shareholder rights, risk governance, and regulatory compliance. To evaluate organizational performance, the evaluation focuses on the economic figures, precisely average return on assets (ROA)/non-worthwhile entities, and operational metrics, particularly market competitiveness and cost efficiency. AI is evaluated in decision-making, which measures the degree of AI incorporation of firms into areas like strategic planning, data analytics, risk management, and automation. Sustainability policies, Corporate Social Responsibility (CSR) initiatives, and governance ethics frameworks are used to measure ESG practices. The questionnaire was meticulously constructed to capture the primary constructs of the study, including corporate governance practices, the level of AI integration in decision-making processes, ESG adoption, and organizational performance. These perceptions and attitudes have been measured on a 5-point Likert scale, thus providing an effective tool to ascertain the extent to which certain governance, AI, and ESG actions are implemented in their companies. The Likert scale remains a well-established method for measuring attitudinal and perceptual data in research within the social sciences. Additionally, participating firms were categorized by industry sector, company Humanities and Social Sciences Letters, 2026, 14(1): 20-43 33 © 2026 Conscientia Beam. All Rights Reserved. size, and the degree of AI adoption. This categorization helped control for variations in structure and technology among organizations. To achieve a high level of sample representativeness and diversity in relevant organizational characteristics, we employed a stratified random sampling method. Stratification was based on critical organizational characteristics, namely, industry sector (e.g., financial services, manufacturing, technology, energy, among others) and company size (small, medium, and large). This detailed segmentation aligns with the authors’ aim to explore the research problem across organizational and sectoral differences, thereby enhancing the analysis and the generalizability of the results. Top-tier decision-makers in Jordanian firms (e.g., CEOs, top managers, and board members) formed the target population, as they play a significant role in strategically shaping governance procedures, sustainability programs, and introducing AI technologies into decision-making. A total of 205 questionnaires were disseminated, and 114 valid responses were collected; thus, the response rate was 55.6%, which is an acceptable level for organizational-level survey research, particularly among senior leadership that is time-bound. The response rate also indicates that the topic of study is relevant in the Jordanian business setting. The study utilizes Structural Equation Modeling (SEM), a powerful statistical method capable of assessing multiple latent constructs and complex associations simultaneously, for data analysis. Partial Least Squares SEM is employed for this purpose as it can operate on small sample sizes while appropriately dealing with direct, indirect, and moderating effects. Measurement models are validated, and path coefficients are estimated within the conceptual framework using SmartPLS 4.4 software, which assesses construct reliability and validity. Analyses were performed in two key steps: the first involves performing a Confirmatory Factor Analysis (CFA) of measurement items to test their validity, and the second involves estimating the relationships in the structural model to test hypotheses. To investigate whether and how AI adaptation will strengthen or diminish the impact of corporate governance on performance, interaction analysis under the SEM framework is performed to explore the moderating effect of AI- driven decision-making. Likewise, mediation analysis examines whether ESG practices are an explanatory mechanism in the relationship between corporate governance and performance. To ensure the reliability of the measurement models, they are subjected to the most common reliability and validity tests, including Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE). 6. RESULTS Key demographic findings of the study sample provide information about the diversity of participants in the study and whether the data represents the views of individuals engaged in corporate governance, AI-driven decision- making, and ESG practices in the context of Jordanian firms. The sample consists of 114 valid responses from executives, senior managers, and board members, so the data comes from those who determine strategies and make decisions in organizations. The sample distribution of roles emphasizes including primary agents driving the decision- making process. 38% of respondents are executives, 42% are senior managers, and 20% are board members. The distribution is meaningful since while executives and senior managers are concerned with executing corporate governance policies and AI-led decision-making strategies, they have board members who are essential players in establishing and overseeing governance frameworks and regulatory compliance. The sample also represents respondents from diverse sectors, indicating the growing interest in governance, AI, and ESG integration across industries. By industry, the top segments via their largest share are financial services (30%), as would be expected given the highly regulated nature of financial institutions and their greater focus on governance compliance and AI- led financial analytics. It is followed closely by the manufacturing sector (25%), underlining the increasing application of AI technologies in industrial automation, supply chain optimization, and quality control. The technology sector (20%) is also well represented, reflecting the growing importance of AI in data-driven decision-making and innovation management. Energy & utilities (15%) and other industries (10%) are also represented, which gives an overall picture of governance, AI, and ESG adoption across various sectors. Humanities and Social Sciences Letters, 2026, 14(1): 20-43 34 © 2026 Conscientia Beam. All Rights Reserved. This sampling also represents organizations of different sizes, providing a well-rounded view of corporate governance practices across small, medium, and large enterprises. Medium-sized firms account for 40%, large firms for 38%, and small firms for 22%, comprising the most significant portion of surveyed firms. This distribution indicates that AI-powered decision-making and governance practices are not solely the domain of large corporations but are increasingly common among medium-sized enterprises, which are often at the forefront of digital transformation and innovation in governance. Another aspect to consider is the years of AI experience in decision- making among these firms. Although 40% of respondents reported incorporating AI into their decision-making processes 1-3 years ago, a substantial portion (30%) indicated they have experienced 4-6 years of AI integration, demonstrating a maturing AI landscape within Jordanian organizations. However, only 15% of companies have used AI for over six years, suggesting that AI adoption remains in the early stages for many organizations. Additionally, 15% of firms are relatively new to AI, having integrated it less than a year ago. This trend aligns with a growing recognition among companies that AI-enabled decision-making will be essential for maintaining a competitive advantage. The study's structural model results are presented in Figure 2, which shows corporate governance, AI- driven decision-making, ESG practices, and how they relate to organizational performance. The proposed model was tested with Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the significance and strength of the hypothesized paths among these constructs. The figure's path coefficients, indicator loadings, and R² values also clarify the relationships of corporate governance to organizational performance, both directly and through the mediation effects of AI-facilitated decisions and ESG efforts. Figure 2. Validity results. The PLS-SEM results above are shown in Figure 2, where corporate governance is the IV (independent variable), organizational performance is the DV (dependent variable), AI-driven decision-making is the moderating variable, and ESG practices are the mediating variable. Through the model, corporate governance demonstrates a strong direct impact on ESG practices (β = 0.713) and a moderate impact on AI-based decision-making (β = 0.425). This further means that artificial intelligence helps determine ESG practices (β = 0.508), indicating that AI integration brings about sustainability in organizations. The results also suggest that ESG practices strongly affect organizational performance (β = 0.655), while R² = 0.841 indicates that the overall model explains 84.1% of the variance of organizational performance, showing high predictive power. The measurement items' outer model loadings are at or above the acceptable threshold (≥ 0.70), suggesting strong indicator reliability. Research shows that corporate governance is key to improving ESG practices and AI adoption and influencing any organization's success. Table 1 Humanities and Social Sciences Letters, 2026, 14(1): 20-43 35 © 2026 Conscientia Beam. All Rights Reserved. shows the reliability testing and assesses internal consistency, composite reliability, and convergent validity of the primary constructs: AI-driven decision-making, corporate governance, ESG practices, and organizational performance. The table presents these measures, namely Cronbach’s alpha, composite reliability (rho_a and rho_c), and average variance extracted (AVE), which are key indicators of the measurement model's reliability and validity. Table 1. Reliability testing. Variables Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) AI-driven decision making 0.869 0.877 0.905 0.655 Corporate governance 0.858 0.868 0.894 0.584 ESG practices 0.905 0.909 0.927 0.679 Organizational performance 0.884 0.887 0.912 0.634 As presented in Table 1, Cronbach’s alpha values for all constructs range from 0.858 to 0.905, exceeding the recommended threshold of 0.70, which indicates a high level of internal consistency among the measurement items. All constructs maintain a composite reliability (rho_c) above 0.89, demonstrating excellent construct reliability. Furthermore, all Average Variance Extracted (AVE) values are well above the minimum requirement of 0.50, signifying adequate convergent validity and that the measurement items effectively explain the underlying constructs. Specifically, AI-Driven Decision-Making has an AVE of 0.655, indicating that 65.5% of the variance is captured by the indicators. Corporate Governance shows an AVE of 0.584, suggesting moderate construct validity. The AVE results confirm that ESG-related indicators provide a strong explanation of the construct, with the highest AVE of 0.679 observed in ESG Practices. Organizational Performance has an AVE of 0.634, indicating fair construct validity. Additionally, R-square (R²) and adjusted R-square (R² adjusted) values reflect the explanatory power of the independent variables on the dependent variables within the structural model, as shown in Table 2. These numbers reveal the variation in DV (ESG Practices and Organizational Performance) explained by the independent variables (Corporate Governance and AI-Driven Decision-Making). Table 2. R² results. Dependent variables R-square R-square adjusted ESG practices 0.508 0.506 Organizational performance 0.841 0.838 The analysis provides insights into the relationships and significance between corporate governance, AI-driven decision-making, ESG practices, and organizational performance. Corporate governance and AI-driven decision- making explain 50.8% of the variance in ESG practices (R² = 0.508). The adjusted R² value, worth 0.506, also confirms the model, which does not have prediction power, even considering the number of predictors. This indicates that corporate governance and AI-based decision-making play a significant role in shaping internal ESG initiatives in organizations. The above findings can give insight into what is required to promote sustainable practices; it is vital to have strong governance structures and the ability to enable technological tools. The evidence is even more dramatic in the case of organizational performance. The R² value of 0.841 implies that ESG practices, corporate governance, and AI-driven decision-making account for 84.1% of the variance in organizational performance. A very high R² indicates how well the model predicts outcomes based on the variables on which it was built, demonstrating that ESG, governance quality, and AI are strong predictors of company success. Similarly, the model is highly stable and reliable, given its complexity (adjusted R² = 0.838). These results highlight the interconnectedness of these constructs and their aggregate effect on desirable organizational outcomes. Moreover, Figure 3 presents the results of hypothesis testing based on PLS-SEM, as well as the relationships among the Humanities and Social Sciences Letters, 2026, 14(1): 20-43 36 © 2026 Conscientia Beam. All Rights Reserved. variables. The path coefficients, significance level (p-value), and coefficient of determination (R²) are known requirements for testing the hypotheses of the study. These results shed light on the implications of corporate governance and AI-based decisions for ESG practices and firm performance. The results emphasize the importance for organizations to adopt sustainable practices, leverage technological capabilities, and have effective governance for improved organizational effectiveness and sustainable growth. Figure 3. Hypothesis-results. The structural model developed by us, as shown in the figure, displays a complex nexus between corporate governance, AI decision-making, ESG practices, and organizational performance, with direct, indirect, and moderating links. One significant correlation is a strong positive relationship between corporate governance and AI decision-making (β = 0.650, p = 0.019), indicating that companies with strong governance are more likely to adopt AI technology. This effect is statistically significant at the 5% level, indicating that well-governed firms adopt AI to improve risk management, strategic planning, and operational efficiency. In addition, corporate governance has a significant positive impact on ESG (β = 0.584, p < 0.001), highlighting the importance of transparency, ethics, and compliance in driving environmental and social responsibility. Moreover, AI technology-enabled decision-making improves ESG (β = 0.679, p = 0.000), which suggests that companies using AI technologies are better suited to assess ESG risks and optimize sustainability efforts, enabling informed ESG decision-making. Also, ESG has a significant and statistically meaningful effect on organizational performance (β = 0.633, p = 0.003), consistent with existing evidence that companies inclined toward permanence bring organizations with aligned financial performance, environmental operations, and operational effectiveness, giving investors reason to trust the firms, regulatory advantages, and increasing their competitiveness in the market. Interestingly, however, the underlying link between AI-driven decision-making and performance is not statistically significant (β = 0.007, p = 0.841), revealing that while organizations adopt AI, it does not directly translate into better performance unless there is a directional push through governance and ESG practices. Table 3 validates these findings through dimensionality tests and hypothesis- based structural test results with PLS-SEM. The results present the direct, mediation, and indirect effects among variables, path coefficients, standard deviations, t-statistics, and p-values. Together, these metrics provide a holistic view of the strength and relevance of the proposed relationships, emphasizing the integrative nature of corporate governance, AI-augmented decision-making, and ESG initiatives in configuring firm performance. In summary, the Humanities and Social Sciences Letters, 2026, 14(1): 20-43 37 © 2026 Conscientia Beam. All Rights Reserved. model highlights that governance, technology, and sustainability initiatives need to work together seamlessly to achieve the best outcomes for an organization. Table 3. Hypothesis testing. Path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Direct effect AI-driven decision making -> Organizational performance 0.716 0.713 0.067 10.658 0.000 Corporate governance -> ESG practices 0.713 0.714 0.034 20.816 0.000 Corporate governance -> Organizational performance 0.102 0.104 0.038 2.682 0.007 ESG practices -> Organizational performance 0.157 0.159 0.053 2.947 0.003 AI-driven decision-making x corporate governance -> Organizational performance 0.041 0.039 0.017 2.356 0.019 Mediation effect Corporate governance -> Organizational performance 0.112 0.113 0.037 3.000 0.003 Indirect effect AI-driven decision making -> Organizational performance 0.716 0.713 0.067 10.658 0.000 Corporate governance -> ESG practices 0.713 0.714 0.034 20.816 0.000 Corporate governance -> Organizational performance 0.215 0.217 0.061 3.526 0.000 ESG practices -> Organizational performance 0.157 0.159 0.053 2.947 0.003 AI-driven decision-making x corporate governance -> Organizational performance -0.041 -0.039 0.017 2.356 0.019 These hypothesis-testing outcomes provide significant empirical insights into the interplay among corporate governance, AI-enabled decision-making, ESG practices, and organizational performance. The results show that AI- driven decision-making directly impacts organizational performance (β = 0.716, p = 0.000), emphasizing the strategic benefits of companies that leverage AI technology. This finding is consistent with previous evidence, which suggests that AI enhances predictions, operations, and data-driven decision-making (Zhang, 2022), thereby improving resource allocation and competitive advantage. As influential as the relevance of AI adoption is, the realization of AI’s potential depends to a great extent on the proper corporate governance context, in which the ethical use of AI, risk management, and adherence to the regulatory environment are possible. Without such governance structures, over- reliance on AI may give rise to various risks, including algorithmic bias, cyber threats, and general ethical dilemmas that could threaten long-term organizational sustainability (Al Rousan, 2024). The results further highlight the importance of corporate governance as a determinant of ESG adoption (β = 0.713, p = 0.000). In firms with transparent leadership, stringent regulatory compliance, and effective risk management practices, sustainability principles are more likely to be embedded in the business model. This perspective has important implications, as it highlights that governance is not limited to financial oversight but includes environmental and social responsibility to ensure that organizations act according to stakeholder demands and the law. Moreover, the mediation analysis verifies that such ESG practices are an essential mediation factor in Humanities and Social Sciences Letters, 2026, 14(1): 20-43 38 © 2026 Conscientia Beam. All Rights Reserved. the relationship between governance and performance (β = 0.157, p = 0.003). This suggests that the superior performance of companies with strong governance structures is driven mainly by incorporating sustainability into their strategic goals, consistent with the evolution of modern business whereby socially responsible and environmentally sustainable companies increasingly attract investors and customers. Corporate governance weakly impacts organizational performance (β = 0.102, p = 0.007), but its indirect effects through AI and ESG practices are relatively more substantial (β = 0.215, p = 0.000). This indicates governance is not yet a sufficient condition. This enabling factor is necessary but not enough to achieve high performance, and organizations need to be complemented by AI-driven innovation and a commitment to ESG. As this insight outlines, organizations can maximize value by shifting their governance frameworks from purely compliance-driven to embracing technology and sustainability. Results also show that when an enterprise adopts AI to manage the decision-making process, the relationship between corporate governance and corporate performance becomes stronger (β = 0.041, p = 0.019), providing additional support for the idea that AI is a catalytic agent that amplifies the effects of governance on performance. Instead, the surprising finding is the negative indirect effect of AI-driven decision-making on performance (β = -0.041, p = 0.019). This implies that, if well integrated, AI can boost performance; if too dependently used, without proper governance oversight, this could lead to inefficient performance, biased judgment, or deviation from strategy. The implications of this use case are essential for organizations adopting AI technologies. Governance mechanisms are needed to balance AI-driven automation with human expertise and ethical considerations optimally. A broader implication of these findings is that organizations need to take an integrated approach that aligns corporate governance with AI adoption and ESG implementation to ensure they can achieve sustainable performance. The strong R² coefficients (Organizational Performance = 0.841, ESG Practices = 0.508) provide greater confidence that these variables have a pathway towards sustainable business success, with corporate governance serving as a centralizing motivator for AI and ESG. These results contribute to an increasing body of literature advocating that corporate governance adopt a long-term perspective, structuring beyond a checklist of compliance requirements and considering technological innovation and sustainability commitments. The findings of hypothesis testing reveal that an understanding of corporate governance, AI-based decisions, and ESG compliance has a significant relationship with the overall performance of the organization. Yet, companies that adopt AI within good governance frameworks generally perform better than their peers, and those that rely too heavily on AI without proper governance can be risky. Moreover, ESG practices serve as an important intermediary construct, reinforcing the belief that sustainability is not just a moral urgency but is ingrained in business excellence itself. Businesses aiming at ‘building back better’ in a long-term resilient manner and being positioned to compete proactively will need to focus on a holistic governance framework that brings together compliance, technology-driven, and sustainability-first approaches. 7. DISCUSSION The empirical results from this study provide robust evidence for the subtle links between corporate governance, AI-based decision-making, ESG practices, and firm performance. This outcome aligns with previous research, which indicates that good corporate governance is a key foundation for a corporation's success, and ESG practices serve as a strategic mediating variable. On the other hand, AI may moderate decision-making and improve governance efficiency. The research discovered the positive effect that corporate governance has on organizational performance, a finding that aligns with previous research (Kim & Li, 2021; Saygili et al., 2022). The impact of governance on performance is positive, but weak in magnitude (β = 0.102, p = 0.007), underscoring that governance alone does not lead to superior performance. This result supports the argument of Bergh et al. (2019), who point out that while governance may lead to transparency and accountability, the requirements of bureaucratic control may limit strategic flexibility and slow down decision-making. In addition, the study highlights the indirect mediating roles played by ESG practices and AI-based decision-making, making an argument for the importance of governance systems that Humanities and Social Sciences Letters, 2026, 14(1): 20-43 39 © 2026 Conscientia Beam. All Rights Reserved. are responsive, proactive, united, and aligned with sustainability and technological reinventions to achieve the best results (Kuzey et al., 2023). These results are consistent with prior research, which suggests that firms with good governance ratings are more likely to gain investors’ trust, experience fewer financial risks, and are better managed by a professional and ethical team (Câmara, 2022; Kottagoda & Gamage, 2024). The results also verify that corporate governance is the key determinant of ESG adoption (β = 0.713, p = 0.000), thus aligning with the theory of the “ESG cascade effect” (Câmara, 2022). This effect occurs when well-governed firms support the implementation of ESG-related practices by their suppliers, investors, and industry peers, thereby nurturing a positive feedback loop in which sustainability initiatives become isomorphic and are enforced at the market level. Similarly, Saygili et al. (2022) reported that independent boards and greater transparency in reporting increase the likelihood of ESG integration, thereby ensuring compliance with environmental regulations and fair practices. Nonetheless, corporate governance encourages ESG engagement, but the effectiveness of the relationship between governance and ESG depends on regulation enforcement and norms in the industry. Kumar et al. (2024) emphasize the need for governance structures to be agile and responsive to fast-evolving ESG norms. In contrast (Elamer & Boulhaga, 2024) highlight the risks of greenwashing, whereby firms overstate efforts to promote a positive image for reputational capital. Such a reality highlights the importance of good governance controls and independent ESG reviews to ensure sustainability statements match what happens on the ground. These findings provide further evidence to support the positive relationship between ESG practices and organizational performance (β = 0.633, p = 0.003), corroborating previous studies Kim and Li (2021) and Perdana et al. (2023). Therein lies the governance benefit of the ESG commitment that is observed in higher market valuations, better risk management and brand reputation, and long-term financial sustainability as well as investor attraction (Saygili et al., 2022). According to the resource-based view (RBV), sustainability-oriented innovations lead to competitive advantage by allowing firms to differentiate themselves to augment their offerings, lower costs, and manage regulatory risk (Barney, 1991). This is based on the results obtained by Mukhtar, Shad, Woon, Haider, and Waqas (2024), who found that green innovation strategies with higher levels of integrated ESG in firms generate firm growth in revenue and operational efficiency. Moreover, a study by the International Journal of Accounting Research & Education (2024) reported that firms with robust ESG policies displayed resilience in the financial crises, aligning with the theory that ESG practices mitigate exposure to environmental, social, and increased governance risks. However, these trends differ by industry and firm size. Dubey et al. (2020) discovered that although resource- intensive industries were affected most by environmental and social factors (energy, manufacturing), service-based sectors (IT, retail) were more sensitive to informed governance and ethical labor practices. In developing markets, however, companies face difficulties implementing ESG because of high compliance costs and weak enforcement of regulations (Kuzey et al., 2023). It indicates that firms should adopt ESG strategies appropriate to their domain context to optimize performance advantages and minimize economic and operational challenges. The study's results validate that ESG practices mediate corporate governance and organizational performance with β = 0.157, p = 0.003, consistent with Kottagoda and Gamage (2024). Such firms perform better through embedding sustainability into their strategic objectives as an outcome of considered governance structures. This aligns with Freeman’s stakeholder theory Freeman (1984) where companies are more likely to receive lasting stakeholder trust and investment by simultaneously focusing on social responsibility and financial goals. Additionally, sustainability reporting and regulatory compliance help boost corporate credibility and improve economic returns. Perdana et al. (2023) discovered that banks that embedded ESG within their risk management frameworks had superior credit ratings and lower risk of loan defaults, supporting the notion that ESG approaches positively affect financial resilience. However, the effectiveness of ESG initiatives is exposure-sensitive; poor governance and lack of regulatory enforcement can cause them to devolve into box-ticking exercises with minimal performance leverage (Elamer & Boulhaga, 2024). AI-enhanced decision-making also acts as a moderator to the relationship between corporate governance and organizational performance (β = 0.041, p = 0.019), supporting the view that AI improves Humanities and Social Sciences Letters, 2026, 14(1): 20-43 40 © 2026 Conscientia Beam. All Rights Reserved. governance efficiency and organizational strategic agility (Kapoor & Agarwal, 2025; Zhang et al., 2022). By allowing firms to analyze real-time data, automate compliance, and optimize decision-making, AI ultimately contributes to improved financial performance, risk mitigation, and market adaptability (Giachino et al., 2024). Nonetheless, the study finds a negative indirect effect of AI on performance (β = -0.041, p = 0.019) that was not anticipated. Although governance is inherently required to orchestrate the role of AI in major decisions, an overdose of AI, even leading to potential inefficiencies, algorithmic biases, or strategic non-conformance, should not be a deal-breaker. Dubey et al. (2020) caution that because AI is rooted in historical data, it might not account for the risk of sudden disasters emerging in governance, such as geopolitical instability or unexpected legal shifts. Moreover, Elamer and Boulhaga (2024) warned that the opacity of AI algorithms can seriously undermine governance transparency and accountability, with ethical concerns around AI-led compliance monitoring. This finding highlights the importance of hybrid governance modes, which integrate AI-driven insights with human expert judgment to achieve the moral integration of AI and strategic alignment. AI is demonstrating that it can perform better than human beings. Nevertheless, as in all phases, people are the secret to minimizing AI-related risks and enabling the agility of governing bodies in a shifting landscape. 8. CONCLUSION The review provides a deep understanding of the correlation between corporate governance and ESG integration, such as AI decision-making and its impact on organizational performance. The findings are consistent with the suggestion that corporate governance is, in part, a determinant of firm success. Moreover, organization-level prosperity depends on adopting corporate governance alongside ESG integration and AI-driven decision-making. This finding converges with antecedents emphasizing the effects of governance transparency, sustainability commitments, and technological advancements on firms' long-term resilience and competitive advantage (Kim & Li, 2021; Saygili et al., 2022). This research's main lesson is that corporate governance and performance must be best practices but not necessarily in a precise ratio. Governance frameworks enhance financial stability, risk management, and strategic oversight, but their impact is context-specific and depends upon incorporating ESG guidelines and AI- based decision-making across all sectors. The findings prove that ESG practices play an essential mediating role in performing firms with well-developed corporate governance mechanisms perform better because they can better integrate sustainability initiatives into their firm strategies (Kottagoda & Gamage, 2024). As the stakeholder theory explains (Freeman, 1984), companies must pursue environmental and social responsibilities alongside profit to sustain stakeholders' trust and a profitable future. Likewise, AI-backed decision-making modifies, reinforcing the association of governance with performance. However, the study observes that the use of AI in quantifying qualitative data for early warning indicators contributes to objective and realistic anticipation of variability through better decision- making, financial forecasting, and compliance automation, leading to overall improvements in agility in the organizational pursuit of success(Zhang et al., 2022). However, it is essential to note that these results carry a significant caution, as overreliance on AI without governing oversight can introduce algorithmic biases, diminish managerial accountability, and spawn unintended risks (Dubey et al., 2020). That might sound like a challenge, but it clearly indicates the impact of the human element in AI integration, ensuring organizations are not replacing strategic leadership with AI but instead making it an enabler. In addition, organizations that successfully implement ESG practices into their governance structures enjoy better financial and operational performance. These ESG-based business models foster financial stability over time by enhancing investor confidence in the company, reducing regulatory risks, and improving the firm's brand image, ultimately leading to long-term economic stability and a sustainable leadership position (Perdana et al., 2023). Nevertheless, the study identifies notable hindrances, such as the risks of greenwashing, regulatory fragmentation, and the tool’s inherent limitations in the corporate sector, and thus, reinforces the requirement for more robust governance structures that would ensure ESG initiatives are credible and substantial (Al‑Zakwani et al., 2025; Elamer & Boulhaga, 2024). The empirical evidence from this study, at the Humanities and Social Sciences Letters, 2026, 14(1): 20-43 41 © 2026 Conscientia Beam. All Rights Reserved. juncture of AI and ESG research, shows that firms seeking to achieve optimal performance serve strategic management interests by embracing a comprehensive governance approach in AI consensus adoption, ESG consensus incorporation, and governance paradigms integration, thereby managing them for optimal performance outcomes. Suppose corporations are to remain viable in the dynamic corporate world. In that case, fitness-based, data-driven, sustainability-oriented, and technology-driven strategies should be deployed across different organizations, rather than traditional compliance-oriented governance mechanisms. This paper provides evidence of the growing interconnectedness between corporate governance, AI-based decision-making, and ESG behaviors in shaping firm performance. These are some of the findings that underscore the need for governance models to evolve beyond traditional oversight models, leveraging technological, sustainable, and stakeholder-based approaches to drive lasting prosperity. With organizations navigating through an age of new digital transformation trends and biosphere towards sustainability, the need for AI-powered governance mechanisms will be indispensable; marry that with actual initiatives in ESG, and organizations will ensure they have the financial resiliency, regulatory compliance, and ultimately strategic competitiveness to go the distance in the global marketplace. This study makes notable contextual contributions by providing new empirical data for the Jordanian business context. In this emerging market, there is a lack of empirical evidence on AI-driven decision-making, ESG practices, and governance linkages. Through this study, which examines various industries in Jordan and draws insights from executives and board members, we contribute to a limited body of literature on the intersections of technological innovation, corporate governance, and corporate performance in developing countries. This sociological understanding serves to link global corporate governance scholarship to its application in the socio-economic environment of emerging Middle Eastern markets. Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the Universiti Sains Malaysia, Malaysia has granted approval for this study on 1 March 2025 (Ref. No. USM/ANTARABANGSA/31925). Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Both authors contributed equally to the conception and design of the study. Both authors have read and agreed to the published version of the manuscript. REFERENCES Al‑Jamal, W. Q. (2024). 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