Public company audits are not homogenous. Varying audit partner attributes, including gender, age, location, and expertise, play important roles in explaining audit outcome disparities. The extant literature underscores the influence of firm-level and office-level differences, in areas such as size, culture, and industry expertise, in shaping audit outcomes. Commonwealth countries, such as the U.K., Australia, New Zealand, and Continental European countries, along with Asian economic entities, mandate the disclosure of audit partner names. Consequently, significant research efforts have been devoted to investigating the impact of audit partner characteristics on audit outcomes in these jurisdictions. With the adoption of Public Company Accounting Oversight Board (PCAOB) Rule 3211, mandating disclosure of audit engagement partner details for U.S.-registered public accounting firms on Form AP, there has been a surge in U.S. firm research exploring the significance of audit partner characteristics on audit outcomes in recent years. This paper outlines research that considers audit partner attributes’ influence on audit partner selection and audit quality across different economic entities. This entry contributes by synthesizing findings from recent research across diverse economic contexts, including the recently available insights from U.S.-based audits. The analysis of this entry not only provides insights into the current state of audit partner research but also delineates avenues for future research on this topic.

Information cascades refer to a type of learning behavior in social networks where individuals make decisions by observing the actions of others, rather than relying solely on their own private information. Network centrality, which measures the relative importance or influence of a node within a network, plays a significant role in initiating and shaping information cascades across four key dimensions. First, nodes with high degree centrality often initiate information cascades due to their large number of direct connections to other nodes. Second, nodes with high betweenness centrality serve as bridges between different parts of the network, thereby controlling the flow of cascading information. Third, nodes with high closeness centrality can access and disseminate information more quickly, accelerating the spread of cascades throughout the network. Fourth, nodes with high eigenvector centrality augment the impact of information cascades through their visibility and connections to other influential nodes. Synthesizing research findings on executive and director networks from management, finance, and accounting, this entry provides insights into emerging trends in corporate governance by highlighting the interaction between network structure and information dissemination.

Information cascades refer to a type of learning behavior in social networks where individuals make decisions by observing the actions of others, rather than relying solely on their own private information. Network centrality, which measures the relative importance or influence of a node within a network, plays a significant role in initiating and shaping information cascades across four key dimensions. First, nodes with high degree centrality often initiate information cascades due to their large number of direct connections to other nodes. Second, nodes with high betweenness centrality serve as bridges between different parts of the network, thereby controlling the flow of cascading information. Third, nodes with high closeness centrality can access and disseminate information more quickly, accelerating the spread of cascades throughout the network. Fourth, nodes with high eigenvector centrality augment the impact of information cascades through their visibility and connections to other influential nodes. Synthesizing research findings on executive and director networks from management, finance, and accounting, this entry provides insights into emerging trends in corporate governance by highlighting the interaction between network structure and information dissemination.

Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI.

Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI.

Public company audits are not homogenous. Varying audit partner attributes, including gender, age, location, and expertise, play important roles in explaining audit outcome disparities. The extant literature underscores the influence of firm-level and office-level differences, in areas such as size, culture, and industry expertise, in shaping audit outcomes. Commonwealth countries, such as the U.K., Australia, New Zealand, and Continental European countries, along with Asian economic entities, mandate the disclosure of audit partner names. Consequently, significant research efforts have been devoted to investigating the impact of audit partner characteristics on audit outcomes in these jurisdictions. With the adoption of Public Company Accounting Oversight Board (PCAOB) Rule 3211, mandating disclosure of audit engagement partner details for U.S.-registered public accounting firms on Form AP, there has been a surge in U.S. firm research exploring the significance of audit partner characteristics on audit outcomes in recent years. This paper outlines research that considers audit partner attributes’ influence on audit partner selection and audit quality across different economic entities. This entry contributes by synthesizing findings from recent research across diverse economic contexts, including the recently available insights from U.S.-based audits. The analysis of this entry not only provides insights into the current state of audit partner research but also delineates avenues for future research on this topic.