Shaping Governance with AI: Trends, Opportunities, and Challenges Mapped through Bibliometrics Analysis

Shaping Governance with AI: Trends, Opportunities, and Challenges Mapped through Bibliometrics Analysis

Najeh Wassim Abdessalam^*, Mahir Aya, Benarbi Houda

Economics and Management Studies and Research Laboratory National School of Business and Management Chouaib Doukkali University – El Jadida – Morocco

*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000047

Received: 21 July 2025; Accepted: 25 July 2025; Published: 27 August 2025

ABSTRACT

Artificial intelligence (AI) is making fast inroads as a transformative force reshaping the performance of organizations worldwide. While AI offers unprecedented opportunities for innovation, efficiency, and scalability, it also introduces complex challenges, including ethical concerns, workforce disruptions, and fears of over-reliance on algorithms. This dichotomy underscores the urgent need for a balanced approach to integrating AI into organizational processes. In this context, governance is pivotal in addressing these challenges, ensuring organizations adopt AI responsibly and effectively. Integrating advanced technologies into decision-making processes is no longer optional but essential to maintaining competitive advantage and fostering sustainable growth. Effective governance frameworks can bridge the gap between technological potential and organizational resilience, enabling informed, ethical, and transparent decision-making. This study explores the interplay between AI, governance, and organizational performance through a bibliometric analysis of academic research published between 2003 and 2025. Using advanced tools such as the R Bibliometric, we aim to map the key themes, trends, and collaborations shaping this emerging field. Although the study is still in progress, preliminary insights suggest that the integration of AI into governance is a promising yet underexplored domain. Early findings highlight its potential to revolutionize traditional decision-making processes while also pointing to the importance of addressing ethical considerations and fostering global collaboration.

Keywords: Artificial Intelligence, corporate governance, Bibliometric analysis, Bibliometric package, Bibliophagy

INTRODUCTION

Nowadays, corporate governance continues to be a highly relevant topic due to its significant importance as a key field of study among management science researchers. It encompasses the practices and mechanisms through which organizations direct, manage, and control their operations. More specifically, “corporate governance” includes the various policies, standards, legal frameworks, and institutions designed to shape how organizations conduct and supervise their affairs.

Traditionally, the core challenge of corporate governance involves the separation of ownership from managerial control, seeking primarily to reduce agency costs through contractual analysis particularly through transaction cost theory. Jensen’s contributions from the theoretical basis of the shareholder-oriented governance model, emphasizing the resolution of conflicts and minimizing agency costs between shareholders and managers (Meckling, 2019). Nonetheless, a deeper analysis of the shareholder-centric view highlights limitations due to an excessive focus on cost reduction and fund efficiency, drawing criticism for inadequately addressing the diverse dynamics among all stakeholders involved.

To overcome these limitations, the stakeholder governance model broadens the perspective to include numerous actors such as managers, shareholders, employees, clients, and suppliers each capable of influencing organizational decisions. However, this inclusive model encounters difficulties in managing stakeholders various, often competing interests, prompting questions about how organizational actions correspond to these varied objectives. Critics assert that both the shareholder and stakeholder models tend to overly concentrate on preserving wealth and distributing value equitably. In contrast, cognitive theories propose a broader understanding of governance, describing it as a process that generates value through learning, innovation, and effective knowledge management. The cognitive approach emphasizes aligning perceptions, values, cognitive structures, and behavioral norms within organizations, thus minimizing cognitive distances to enhance internal coordination. Building upon this cognitive foundation, the behavioral governance approach places further emphasis on skills, behaviors, and cognitive biases as critical drivers of organizational performance.

In this context, artificial intelligence (AI) and machine learning present promising opportunities to mitigate cognitive biases and judgment errors commonly affecting university governance decisions. By objectively analyzing vast amounts of data, these technologies help overcome human cognitive limitations as confirmation, anchoring, or availability biases which often distort stakeholder decision-making. Through their predictive capabilities, AI tools enable organizations to anticipate outcomes more accurately, reducing reliance on subjective intuition or heuristics.

This research aims specifically to examine the impact of cognitive biases on decision-making in university governance, emphasizing how such biases shape stakeholder behaviors within organizations. By employing advanced techniques such as machine learning and behavioral analytics, the study seeks to deepen the understanding of governance processes and provide evidence-based recommendations to improve practices. To achieve this objective, a bibliometric analysis is conducted using the R software package bibliometrix, focusing on a ten-year corpus of academic literature covering governance, cognitive biases, and behavioral analysis.

Theatrical background

Behavioral approach to governance

Corporate governance remains a pertinent topic in management science research. Historically, the concept primarily addressed the relationship between executives and shareholders from a purely financial perspective (Jensen & Meckling, 1976). Over time, the scope of corporate governance has progressively expanded to include other stakeholders and dimensions, notably cognitive and behavioral aspects (Aguilera & Jackson, 2003).

This advancement in knowledge and competencies in the field has been notably marked by the adoption of good governance standards by an increasing number of companies and by benchmarking the guidelines set forth by the OECD (OECD, 2015). The OECD approaches corporate governance from multiple perspectives: shareholder governance, stakeholder governance, and cognitive and behavioral governance (Shleifer & Vishny, 1997). This plurality of approaches explains the absence of a universal and unanimous definition of corporate governance.

Introducing the behavioral aspect into governance leads to an exploration of various behavioral biases relevant to cognitive sciences (Kahneman & Tversky, 1979), followed by an in-depth analysis of literature from behavioral finance and economics (Thaler, 1999). Studying cognitive biases such as anchoring, availability, and representativeness helps elucidate certain decision-making behaviors of economic actors and corporate leaders (Bazerman & Moore, 2013).

Behavioral Biases and Heuristics

The concept of cognitive biases originates from the development and emergence of decision-making theories. Since Simon’s (1955) work on bounded rationality, researchers have explored how individuals make decisions in uncertain contexts. Despite their evolution over time, these theories maintain a central objective: to explain how individuals make decisions and to improve this process through various concepts, notably cognitive biases and heuristics (Kahneman & Tversky, 1979).  Similar to the concept of inefficiency, behavioral bias is often defined by referring to an “ideal” standard that corresponds to perfect rationality (Thaler, 1980). Cognitive biases can be analyzed as deviations from “optimal” decisions, leading individuals to interpret and process the same reality in multiple ways (Tversky & Kahneman, 1974). A cognitive bias is a systematic reasoning error, although not all errors necessarily constitute biases.  The most widely accepted definition is that of Kahneman (2011), who considers a bias as a systematic reasoning error resulting from heuristics that simplify reality. Heuristics are cognitive shortcuts that enable individuals to make quick decisions, though sometimes in a biased manner. For instance, anchoring bias causes individuals to rely heavily on the first piece of information received, while the availability bias leads them to assess the probability of an event based on the most recent or memorable examples in their minds (Kahneman, 2011; Bazerman & Moore, 2013).

Behavioral biases have been extensively studied across various disciplines, including behavioral economics, cognitive psychology, and management sciences (najeh, 2023). Understanding these biases helps improve decision-making in organizational and financial environments (Ariely, 2008).

The distinction between different biases (cognitive or emotional) is made by Greenfich (2005) based on a two-axis framework: “collective or individual,” as shown in the following table:

Table 1: The Types of Biases

Source: based on Greenfinch (2005)

Heuristics are general decision-making rules that individuals use to assess probabilities, helping to streamline the decision-making process (Grether, 1992). More specifically, they serve as cognitive shortcuts that simplify complex problems (Najeh et Benarbi, 2023), making them easier to process (Yachanin & Tweney, 1982). As Kahneman & Tversky (1974 p. 23) point out: “In practice, people rely on a small number of heuristic principles that reduce the task of assigning probabilities to simpler judgmental activities. These heuristics are often indispensable, although they can also lead to serious and systematic errors”

In their article “Judgment under Uncertainty: Heuristics and Biases”, Kahneman & Tversky identify the main heuristics that impede rational decision-making: representativeness, Availability, and adjustment-anchoring.

Artificial intelligence:

Artificial intelligence (AI) represents one of the most recent and unique advancements in the field of computer science and digital technology. Since it was first conceptualized by Marvin Minsky and further developed by John McCarthy, the scientific community has remained divided on its implications some view it as a tool for human progress, while others raise concerns about its potential risks to human existence and ethical boundaries (Russell & Norvig, 2020). The extraordinary computational power of AI, particularly in correcting human limitations, aligns with Leibniz’s vision of mathematizing human actions and reasoning. This raises questions about whether AI could ultimately become a substitute for human intelligence rather than just an assistant (Bostrom, 2014). The historical relationship between workers and machines serves as a warning automation in the Industrial Revolution led to the replacement of human labor, and AI might follow a similar trajectory by challenging human intellectual and operational capabilities (Brynjolfsson & McAfee, 2014). With its vast scope of application spanning all known economic sectors, immense computational power, and the ability to process massive volumes of data in record time, sometimes even blurring the lines between a human-made creation and an autonomous entity (Makridakis, 2017). As AI advances, its potential transcends traditional roles, raising fundamental questions about its autonomy, ethical considerations, and the extent to which it may surpass human cognitive capabilities (Tegmark, 2017).

Artificial intelligence (AI), as it continues to evolve, appears increasingly poised to substitute human capabilities, particularly in neural processing and cognitive functions, while simultaneously compensating for human limitations. Through groundbreaking algorithms, especially those driven by deep learning, AI is not only capable of analyzing vast amounts of data but also of identifying complex patterns imperceptible to human cognition, engaging in human interaction, continuously learning, and, most importantly, enhancing its performance over time (LeCun, Bengio, & Hinton, 2015).

Among the three primary types of AI :Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI) it is evident that AI applications extend across virtually all aspects of human life, raising both unprecedented opportunities and profound risks (Bostrom, 2014). The transition from narrow AI, which specializes in specific tasks, to general AI, capable of human-like reasoning, and eventually to superintelligence, which could surpass human intellect, presents significant ethical, social, and existential challenges (Tegmark, 2017).

METHODOLOGY

This study uses a research methodology based on bibliometric analysis procedures to consolidate previous literature and find novel perspectives. To select the documents to be analyzed, we followed a rigorous method that aimed to identify and detect the eligibility and inclusion of reports in the scope of the review (Selçuk, 2019).  The analysis of bibliometrics uses quantitative methods on bibliometric information (e.g., units of citation and publication) to determine publication, impactful publications, and current research areas.

Database selection

Currently, Web of Science (WoS) and Scopus are the two leading bibliographic databases that cover multiple academic disciplines (El Akrami et al., 2023). In this study, the Web of Science Core Collection was selected as the primary data source due to its comprehensive coverage and high- quality indexing. This database encompasses over 18,000 esteemed journals spanning diverse research fields, making it a robust and reliable source for bibliometric analysis. Additionally, the Web of Science Core Collection maintains an extensive citation network with approximately 1.3 billion references dating back to 1900, providing a rich foundation for citation analysis (Zhang et al., 2022).

Beyond its broad disciplinary scope, Web of Science offers access to millions of bibliographic records, facilitating detailed metadata exploration, including authors, institutional affiliations, research keywords, and cited references. This structured and interconnected dataset enhances the reliability of research mapping and impact assessment (Mongeon & Paul-Hus, 2016; Waltman, 2016).

Data review

The research was conducted in March 2025. Currently, the research is at the stage of selecting pertinent keywords for bibliometric analysis. To achieve this, the study identifies relevant terms within two primary research domains: Corporate Governance and Artificial Intelligence. These keywords will serve as core terms guiding the bibliometric mapping to analyze literature trends and identify intersections between these two fields. We have subsequently selected the top 500 high-cited articles and review articles. Table 2 provides an overview of the data extraction procedure and data retrieval process

Table 2: Overview of the data extraction procedure and data retrieval process.

Source: Authors

Software selection

We need the right tools to conduct systematic bibliometric analysis and generate significant research findings from the exported data set (Moral-Muñoz et al., 2020). In an attempt to make bibliometric analysis more accessible and user-friendly, several visual analysis tools and support software have been developed recently, including VosViewer CiteSpace, RefViz, and Leximancer. In our study, we chose Bibliometrix.is a free software program for quantitative research in scientometrics and bibliometrics that was created by (Aria & Cuccurullo, 2017). This tool provides quick data matrix building and analysis for performance analysis and scientific mapping of bibliographic collections (Derviş, 2019). For this study, the latest version of Bibliometrix was used through its web application, Biblioshiny. to investigate the networks and connections among the bibliometric analysis’s items. Bibliometrix stresses the accuracy and statistical completeness of the results in addition to data display (Shah et al., 2020).

Findings

Overview of the main information

Source: Bibliometrix

The dataset offers valuable insights into the evolution and characteristics of the research landscape between 2003 and 2025. 500 documents were identified, drawn from 365 different sources, suggesting a moderate but diverse academic interest in the field. The annual growth rate of 9.91% reflects a steady, though not exponential, increase in scholarly output possibly indicating a phase of theoretical consolidation within the domain.

The research is notably collaborative, with an average of 5.07 co-authors per document and only 44 single-authored papers. Furthermore, 38.2% of the documents involve international co- authorship, underscoring the global nature and cross-border relevance of AI-driven governance issues. The community of contributors is substantial, comprising 2,426 authors, and the field exhibits thematic richness, as evidenced by 1,762 author keywords.

The average document age is 3.84 years, indicating that this field is relatively young and rapidly evolving. Despite its youth, the average citation rate of 31.52 citations per document reflects high academic impact and growing recognition within the broader scientific community.

The most cited article, with over 3,000 citations by Wolfert Sjaak titled: Big Data in Smart Farming – A review. In their article, the authors state that Smart Farming utilizes information and communication technologies to enhance agriculture. It relies on the Internet of Things, cloud computing, and artificial intelligence to collect and analyze data, enabling farmers to make informed decisions. This approach impacts the entire food supply chain and can significantly change roles and power dynamics. The future of Smart Farming is poised between closed, proprietary systems and open, collaborative ones.

Evolution of the scientific production

Source: Bibliometrix

The graph depicting annual scientific production from 2003 to 2025 offers key insights into the evolution of research in the context of this study. From 2003 to 2017, the field remained relatively underexplored, with limited annual publications. A turning point occurred around 2018, followed by a significant surge in scholarly output, culminating in a peak in 2022 with over 120 articles published. This rise reflects growing global attention to the integration of artificial intelligence into governance frameworks, especially in areas such as algorithmic decision-making, digital ethics, and public policy. However, the sharp decline observed in 2023 and 2025 may be attributed to the incomplete indexing of recent data or redirection of research toward applied and interdisciplinary domains. Overall, the trend illustrates the rapid emergence and academic consolidation of AI governance as a critical and timely field of study.

Most relevant sources

Source: Adapted by authors

The table of leading sources reveals the most active journals contributing to the field. At the top of the list is the journal Sustainability, with 14 articles, making it the most prolific source. Sustainability is an open-access, peer-reviewed journal published by MDPI (Multidisciplinary Digital Publishing Institute), a Swiss academic publisher. The journal is indexed in major databases such as Scopus, Web of Science (ESCI), and DOAJ, and has gained visibility for its interdisciplinary focus on the environmental, cultural, economic, and social sustainability of human beings. It regularly publishes research that intersects sustainability with innovation, digital transformation, and governance, making it a natural outlet for studies on AI’s role in shaping governance frameworks.

Following Sustainability, other key journals include Business Strategy and the Environment (7 articles) and the Journal of Medical Internet Research (6 articles), indicating the relevance of AI governance in strategic, environmental, and digital health contexts. Several journals contributed equally with 5 articles each, including AI & Society, BMJ Open, Expert Systems with Applications, JMIR Research Protocols, and the Journal of Business Research. These reflect the field’s broad scope, bridging ethics, medicine, decision support systems, and organizational research. Technical and operational dimensions are also represented by Engineering Applications of Artificial Intelligence and the European Journal of Operational Research (4 articles each), further emphasizing the multidisciplinary nature of AI governance.

Most productive authors

The list of most productive authors highlights the key contributors to the academic development of the field. At the top are DENNISTON AK and LIU XX, each with 6 articles, indicating sustained research activity and thought leadership on topics related to AI and governance. They are followed closely by HOGG HDJ and JIRAPORN P, with 5 publications each, suggesting consistent engagement with interdisciplinary or domain-specific aspects of the field.

Other notable contributors, each with 4 articles, include CALVERT MJ, CHATJUTHAMARD P, GUPTA A, JONES S, MANIATOPOULOS G, and RIVERA SC. This distribution reflects a diverse authorship base likely spanning multiple regions, disciplines, and institutional affiliations. The presence of these recurring authors also suggests the emergence of a core research community that is shaping the discourse on how artificial intelligence intersects with governance structures, ethical frameworks, public administration, and sectoral policy-making.

This author’s productivity chart underscores the growing depth and specialization of the field, driven by a mix of technical, managerial, and societal perspectives. If needed, I can provide brief profiles or affiliations of the top authors to enrich the analysis further.

The most important fields

Source: Web of Science

The treemap visualization presents the disciplinary distribution of the literature analyzed in our study. It reflects the multidisciplinary nature of the field, with strong representation from both computer science and management-related domains.

At the top are Computer Science – Information Systems (134 articles) and Artificial Intelligence (132 articles), confirming that the core of this research area is driven by technological and systems- level approaches to AI. This is followed closely by Computer Science Theory & Methods (108) and Interdisciplinary Applications (102), highlighting the conceptual, methodological, and cross- functional exploration of AI in governance contexts.

Beyond computer science, Environmental Sciences (107 articles) and Management (104) emerge as prominent disciplines, reflecting the growing relevance of AI in addressing sustainability goals, strategic planning, and organizational decision-making. Other notable categories include Business (85), Business Finance (76), Environmental Studies (71), and Engineering – Electrical & Electronic (71), which further emphasize how governance challenges are being addressed through AI in various sectors ranging from public policy and corporate strategy to infrastructure and environmental monitoring.

This visualization confirms that the field is not confined to a single domain but rather thrives at the intersection of technology, sustainability, economics, and organizational science, showcasing the comprehensive scope of AI’s impact on governance structures.

Productive countries

The bar chart highlights the geographic distribution of scientific production in the field explored by the study “Shaping Governance with AI: Trends, Opportunities, and Challenges Mapped through Bibliometrics Analysis.” The United States stands out as the clear leader, contributing over 300 publications, which reflects its dominant role in both AI research and governance-related innovation. Following the U.S., China ranks second with around 165 articles, indicating its growing investment in digital governance and AI technologies.

England comes third with approximately 150 publications, showing strong academic engagement likely driven by its policy-oriented research institutions and universities. Other key contributors include Germany, Canada, Australia, and India, each producing between 80 and 100 publications, confirming a broad international interest across both developed and emerging economies. Countries such as the Netherlands, Italy, and France also feature among the top 10, reflecting Europe’s active participation in debates around ethical AI, regulation, and digital governance.

This distribution illustrates that the topic of AI in governance is globally relevant, with contributions from North America, Europe, Asia, and Oceania, reinforcing the field’s international and collaborative nature.

Keywords Statistics

This section discusses the results of keyword analysis, to highlight central intellectual themes and craft the identity of the research discipline. Keyword analysis involves a close examination of the prevalent keywords in research documents. In our study, we relied on the authors’ keywords.

The bubble chart titled “Most Relevant Words” provides insight into the thematic focus of the literature on AI and governance by analyzing the most frequent “Keywords authors.” The term “management” leads with 50 occurrences, indicating that managerial implications and organizational contexts are central to the discourse. It is closely followed by “performance” (44) and “impact” (31), suggesting a strong emphasis on evaluating the outcomes and effectiveness of AI integration in governance processes.

The keywords “artificial intelligence” and “governance”, both appearing 30 times, confirm the core intersection of the study. Other recurring terms such as “model” (27), “framework” (25), and “system” (24) point to a significant methodological and conceptual orientation in the field, where researchers are developing and testing structured approaches to AI governance.

Finally, the presence of “information” (23) and “big data” (20) highlights the importance of data- driven decision-making and information systems in shaping modern governance. Altogether, the chart reflects a research field focused not only on technological innovation but also on strategic application, performance measurement, and conceptual modeling

Source: Bibliometrix

The word cloud visualization offers a vivid summary of the most frequently used terms in the literature analyzed in this study “Shaping Governance with AI: Trends, Opportunities, and Challenges Mapped through Bibliometrics Analysis.” The dominant presence of keywords such as “management,” “performance,” “artificial intelligence,” “governance,” and “impact” highlights the central themes driving this research area focusing on how AI technologies are transforming organizational processes, strategic decision-making, and governance outcomes.

Words like “framework,” “model,” “system,” “information,” “big data,” and “technology” suggest a strong methodological and technical orientation, where researchers are actively developing conceptual tools and data-driven solutions. Additionally, the appearance of terms like “ethics,” “privacy,” “risk,” “trust,” and “decision-making” underscores the socio-ethical dimensions of AI in governance, pointing to concerns around accountability, transparency, and fairness in automated systems.

Other notable terms such as “health,” “climate change,” “blockchain,” and “corporate social responsibility” reflect the diverse application domains where AI governance is being explored ranging from healthcare and environmental sustainability to finance and corporate ethics. Overall, the word cloud illustrates a rich and multidisciplinary research landscape, blending technological innovation with strategic, societal, and regulatory concerns.

CONCLUSION

The bibliometric analysis conducted in the study “Shaping Governance with AI: Trends, Opportunities, and Challenges Mapped through Bibliometrics Analysis” reveals a rapidly growing and richly interdisciplinary field. The increasing number of publications, particularly since 2018, highlights the strategic importance of artificial intelligence in rethinking governance models across sectors. The predominance of keywords such as “management,” “performance,” “impact,” “framework,” and “governance” reflects a shift in academic and professional focus toward integrating intelligent technologies into decision-making, oversight, and strategic planning.

The analysis of sources and disciplines confirms the hybrid nature of the field, combining insights from computer science, management, environmental science, and business. Countries like the USA, China, and the UK lead in scientific production, while journals such as Sustainability and Business Strategy and the Environment show that AI’s role is increasingly linked to sustainable and responsible governance.

At the heart of this transformation lies the growing use of machine learning and deep learning tools, which enable organizations to process vast amounts of data, uncover hidden patterns, and support complex decision-making in real time. These technologies enhance operational efficiency, risk prediction, personalization of services, and strategic foresight, making them indispensable for both private and public sector organizations aiming to remain competitive and accountable in an increasingly data-driven world.

In sum, artificial intelligence far from being a mere technical innovation has become a cornerstone of modern governance and organizational intelligence, offering unprecedented opportunities for smarter, faster, and more ethical decision-making. Its integration through advanced tools such as machine and deep learning is not only reshaping internal processes but also redefining how institutions interact with their environment, stakeholders, and long-term objectives.

Finally, the integration of artificial intelligence into governance is transforming decision-making, service delivery, and public administration. This bibliometric analysis highlights AI’s remarkable potential to enhance transparency, efficiency, and evidence-based policymaking, underscoring its pivotal role in shaping modern governance. However, as AI systems become more influential, it is essential to address ethical considerations such as transparency, accountability, fairness, and privacy to ensure that technological progress aligns with democratic values and public trust. Striking a balance between innovation and ethical stewardship will be crucial for governments aiming to leverage AI for societal good while safeguarding fundamental rights and promoting inclusive, responsible governance.

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