Jun 2026· Journal of Political Stability Archive· Vol 4, pp. 985-1000· 0 citations· 18 references
TL;DR
The findings illustrate that the influence of AI governance on firm performance is significantly moderated by Digital Literacy, underscoring the influence of employees' digital skills in unlocking the value of AI-enabled governance system.
Abstract
Artificial Intelligence (AI) has revolutionized corporate governance practices, improving transparency, accountability, compliance monitoring, and strategic decision-making. This study analyzes the influence of AI-Driven Corporate Governance (AICG) on the performance of the companies, examining also the mediation effect of Decision Intelligence and the moderation effect of Digital Literacy. The study integrates the views of the Resource Based View (RBV) and Dynamic Capability theory to explore the link between AI's governance mechanisms and organizational performance. The research adopts an integrated moderated mediation approach, grounded in the Resource Based View (RBV) and Dynamic Capability Theory, to explain the role AI’s governance mechanisms play in enhancing organizational performance. The data which have been collected were analyzed on correlation, reliability, regression on 235 employees that work in organizations that in the process of digital transformation. The results clearly indicate that there is a positive effect of AI Corporate Governance on firm performance. Additionally, AI governance is a key lever for employing Decision Intelligence to boost organizational performance by maximizing the efficiency, agility and quality of managerial decision-making. The findings also illustrate that the influence of AI governance on firm performance is significantly moderated by Digital Literacy, underscoring the influence of employees' digital skills in unlocking the value of AI-enabled governance system. By bringing together technological capabilities and human competencies under one umbrella, the study adds to the increasing amount of literature on AI governance. It builds on existing work, by bringing the experiences of a developing economy context, where AI adoption and digital transformation is not yet mature. The results are of practical relevance for managers, policymakers, and organizations aiming to enhance governance efficiency, decision-making and the performance of their organizations by means of responsible use of AI and digital competences. The study builds that there is a need to not only invest in technology but also nurture intelligent decision making and a digitally empowered workforce to enable AI governance in the right path.
Agentic AI can be viewed as a governance enhancing mechanism that enhances transparency, decreases information asymmetry and promotes adaptive, evidence-based board leadership.
Mahesh Agarwal· Journal of Intelligent Decis...· 0 citations
AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.
Dareen Alshamsi, Dr. Mohamed Manea Almansoori, Dalal S. Almansoori et al.· Journal of Intelligent Decis...· 0 citations
Background: Artificial Intelligence (AI) has become a pervasive part of
organisational activities, transforming it from a tool of the past into a
fundamental tool of the modern administration and forcing scholars to rethink
how AI is changing the nature of strategic decision-making, operational
efficiency, ethical governance and managerial accountability (Weismann, 2024;
Ouabouch & Yahyaoui, 2025). Research Problem: While there have been
individual studies on the impact of AI in specific functional aspects like HR,
marketing or finance, there is not yet a holistic understanding of the impact of AI
on all these four interdependent pillars of corporate administration. Objectives:
This study explores the effect of AI adoption on strategic decision-making,
operational efficiency, ethical governance and managerial accountability, and
suggests an integrated framework for AI-based Corporate Administration.
Design: The quantitative, cross sectional survey design was used. Data collected
were primary data, obtained by designing a structured questionnaire based on the
5-point Likert scale, which was then answered by 100 corporate managers,
executives, department heads and employees. The primary data were then
analysed by reliability test, descriptive analysis, correlation and regression
analysis. Key Findings: Good internal consistency of the constructs was
observed (Cronbach's alpha ranged from 0.90 to 0.92). Statistically significant
and strong positive relationships were found between AI adoption and outcomes
of strategic decision making, operational efficiency, moral governance, and
managerial accountability (all p < .001) and explained 82% to 88% of the
variance in each outcome. Practical Implications: The results indicate that
corporate decision makers, boards, and policy makers should view the use of AI
as an administrative strategy, not a mere technical upgrade, and implement
algorithmic governance measures to address algorithmic risk. Conclusion: The
findings indicate a strong positive relationship between the use of AI and the
four dimensions of corporate administration that were investigated, thereby
suggesting that the proposed integrated framework could serve as a basis for
developing a theory and organizational practice for the future.
Tulika Dutta Roy· International Journal of Mod...· 0 citations
Background: It is generally believed that human and social capital in the board and top leadership leads to better performance of the organization, but the way through which this happens is still little specified and poorly documented. Human capital provides knowledge, experience, judgement and problem solving ability while social capital provides access to information, trust, legitimacy, and external resources. These resources don't automatically lead to better performance. They can be valuable if governance actors use them for monitoring, to offer advice to decision-makers, for selecting executives, to design incentives, to give strategic attention to, and to visibly demonstrate good governance behavior to, decision makers.
Methods: This study is not a systematic review or meta-analysis. Conceptual and empirical articles by leading journals in the fields of management, organizational behavior, strategy, finance, and corporate governance were purposively selected and augmented with seminal articles published prior to 2016, appraised for their construct definition, research design, and level of analysis, and narratively synthesized into a multilevel mediation architecture, integrating agency theory, stewardship theory, resource-dependence theory, social-capital theory, strategic human-capital research, and dynamic-capabilities logic.
Results: The synthesis suggests that integrity, voice, learning, coordination and adaptability in culture can translate into board capital, knowledge integration, ethical self-regulation, innovation and cooperation with stakeholders, and resilient execution. Opposite trends are also clear: experience can lead to 'stiffness', networks can become cohesive, and links outside the network can lead to overload or conflict. Evidence for the distinctiveness of the relationships between governance, capital and culture and between culture and performance is still there, although direct tests of the full mediation chain remain scarce.
Conclusion: Organizational culture is best conceptualized as a partial, delayed, and recursive mediator that converts governance resources into widely repeated norms and routines, rather than as a simple intervening variable. The article develops ten propositions, identifies measurement and causal-inference problems, and recommends multilevel longitudinal designs combining board biographies, network measures, board-process evidence, repeated employee culture data, and lagged multidimensional performance outcomes.
Novelty: The review contributes an explicitly multilevel and temporally specified mediation architecture that links director-level capital, board-level process, organization-level culture, and differentiated performance outcomes within a single framework, and it specifies dysfunctional as well as beneficial cultural pathways that earlier reviews have treated separately.
B. Gyawali, Mahananda Chalise, Dilli Raj Sharma· NPRC Journal of Multidiscipl...· 0 citations
This structured literature review explores the role of corporate governance in enhancing the effectiveness of management control systems (MCS), with a particular focus on the emerging challenges and strategic directions in implementation. Drawing on peer-reviewed publications from 2020 to 2025, the study synthesizes four critical themes: (1) the functional role of governance in MCS design, (2) multidimensional indicators of control system effectiveness, (3) barriers to governance-aligned MCS implementation, and (4) strategic responses to contemporary governance demands. The review reveals that corporate governance improves MCS by reinforcing transparency, accountability, risk mitigation, and stakeholder engagement, particularly when supported by internal audit, board independence, and digital capabilities. It also highlights emerging concerns such as cybersecurity, remote work productivity, technological adaptation, and employee well-being. Theoretically, this study contributes by integrating control systems theory with digital governance and behavioral insights; practically, it offers guidance for designing responsive, ethical, and performance-driven control architectures. Limitations include the reliance on secondary sources and the lack of empirical generalization, prompting future research to investigate causal mechanisms through mixed-methods approaches
Amalia Sholehah· Jurnal Profesi Dan Manajemen...· 0 citations
The rapid diffusion of generative artificial intelligence (GAI) is transforming organizational knowledge processes, yet existing research offers limited insight into its governance implications. This study addresses this gap by conceptualizing GAI as a mechanism of knowledge governance and examining its relationship with digital trust, information security maturity, and decision quality. Data from 300 firms across Poland, Germany, and Spain were collected using a CAWI survey of top management teams. Structural equation modeling (SEM) and multilevel analysis were applied to test relationships between AI-mediated knowledge practices, digital trust, information security maturity, and perceived decision quality. The results show that AI use significantly increases digital trust, which in turn improves decision quality. Digital trust acts as a mediator, while information security maturity moderates this relationship. The findings indicate that the impact of generative AI on decision-making depends on governance conditions rather than technology alone.
Justyna Żywiołek· European Conference on Knowl...· 0 citations
