Dhanabalan Thangam

The integration of artificial intelligence with human resource management has accelerated in a big way, fundamentally altering the organizational decision-making processes, and thereby posing unprecedented governance issues for the corporate directors and officers. This theoretical analysis explores the problem of traditional corporate governance structures and fiduciary obligations being challenged by algorithmic HRM systems. Drawing on recent scholarship from corporate governance, organizational theory, and information systems, the authors examine the development of algorithmic HRM as a field of governance that is critical and needs to be addressed at the board level. They synthesize existing research addressing questions of AI governance, fiduciary duties in algorithmic contexts, and organizational accountability mechanisms in order to propose an integrative framework for algorithmic stewarding. The analysis shows important shortcomings in current governance models, especially the lack of clarity around AI decision-making, the lack of centralised structures of accountability, and the insufficiency of traditional monitoring mechanisms. They present the notion of algorithmic stewardship, expanding the concept of fiduciary theory to non-human decision agents and suggesting specific obligations for directors and officers in monitoring AI-mediated HRM. The framework identifies key governance capabilities, such as AI literacy, audit mechanisms, and escalation protocols that enable effective oversight. This review adds to corporate governance theory by reconfiguring the board responsibilities in algorithmic organizations and suggests practical implications to regulators and practitioners, as well as researchers dealing with the intersections of AI, HRM and corporate accountability.

The fast nature of the spread of AI-based products in the sectors has reshaped the way organizations design, deliver, and scale digital innovations. Nevertheless, this growth has also increased ethical, social, and governance issues associated with algorithmic bias, lack of transparency, threats to privacy, and absence of accountability. Current strategies of AI ethics are significantly based on post-hoc audits, regulatory compliance, or reactive governance systems that are not always effective to combat ethical risks that occurred at the stage of product design and development. This chapter contributes to ethics by design as a proactive model to instill responsibility all over the life cycle of developing an AI-assisted product. By following the approach of conceptual synthesis, the research synthesizes information on AI ethics, responsible innovation, and product development literature to create a full set of ethics-by-design. The framework illustrates that the ethical aspects can be properly incorporated at major phases, such as ideation, data governance, model development, testing, deployment, and post-deployment monitoring. The chapter emphasizes the ways organizations can reduce ethical risks without jeopardizing innovation and competitive advantage by transforming ethics as an externality to an internal design capability. The chapter makes contributions to theory by establishing ethics as a design science in AI innovation, and to practice by providing practical advice to AI product teams. Policy impact focuses on supplementing regulation with design-based responsibility in promoting trustworthy, ESG-aligned, and sustainable AI innovation.

Artificial intelligence (AI) is rapidly transforming the nature of entrepreneurial ecosystems by modifying the processes of opportunity identification, resource mobilization, and venture creation and scaling. Although earlier studies have focused on the role of AI on high-growth entrepreneurship, little effort has been directed towards its impact on threshold entrepreneurial capacity, the threshold quantity of skills, resources, and cognitive preparedness to become an entrepreneur. In this chapter, the authors analyze the intersection point of AI and entrepreneurial ecosystems to learn how AI-based technologies can be used to develop threshold entrepreneurial capacity. Relying on literature on entrepreneurship and innovation systems, as well as on digital ecosystem literature, the study uses a conceptual synthesis approach to come up with an integrative framework of how AI capabilities relate to ecosystem features, including finance, talent, institutions, and market access. The discussion indicates that AI reduces entry obstacles, improves decision-making, and democratizes entrepreneurial knowledge, which serves to facilitate more people to become participants in the entrepreneurial process. Nevertheless, the chapter has also identified important limitations such as the possibility of algorithmic bias, digital divides, and gaps in governance that can skew the benefits of the ecosystem unevenly. This study is relevant to the entrepreneurial ecosystem theory and paves the way to the concept of inclusive, technology-based entrepreneurship by presenting AI as an enabling ability, not a tool of productivity. The results provide practical recommendations to builders of ecosystems, policymakers, and development agencies interested in using AI to achieve sustainable and balanced capacity development of the entrepreneur.

This manuscript investigates the role of social media as “digital bridges” which link individuals, groups, and organizations within a community in post‐disaster recoveries. By synthesizing research from disaster studies, social capital theory, and digital communication literature, the paper draws on empirical evidence on social media supporting situational awareness, community mobilization, digital storytelling, and cross‐cultural solidarity. Simultaneously, it also examines the problems of fake news, social media apartheid, surveillance dystopia, and the fleeting nature of online solidarity. Instead of a general literature review, the article provides an integrative conceptual synthesis that connects theory to policy and practice. It concludes with concrete suggestions for policymakers, platform designers, and recovery specialists who want to leverage social media's connecting power while reducing its separating dangers.

Behavioral finance has emerged as a key discipline in understanding the psychological factors influencing financial decision-making. This review explores the contributions of behavioral finance to financial risk management strategies, focusing on, how insights into investor psychology can enhance traditional risk models. Despite the significant advancements in behavioral finance, its integration into risk management techniques remains limited due to the difficulty of measuring psychological biases in financial models and the complexity of human behavior. This review aims to overcome these obstacles by synthesizing the existing literature on behavioral finance and demonstrating, how those insights can be practically implemented to improve risk management frameworks. The research provides a comprehensive overview of traditional risk management and its limitations, observed by an exploration of through which behavioral finance contributes to more accurate risk assessments. It examines behavioral biases along with loss aversion, overconfidence, and herding, and their implications for financial risk management. The review delves into behavioral risk management tools. The research offers significant benefits by highlighting, thus behavioral finance can fill gaps in traditional risk management strategies, imparting a more holistic and effective method for mitigating financial risks. The findings emphasize the importance of incorporating psychological factors into the risk assessment and decision-making process, benefiting both investors and financial institutions.

The application of Artificial Intelligence (AI) to Customer Relationship Management (CRM) is transforming the way businesses interact with their customers and grow their revenue on a sustainable basis. This article offers a conceptualization to understand the strategic change in CRM with the advent and development in AI technologies in machine learning, natural language processing, predictive analytics, and intelligent automation. Built on Resource-Based View and Dynamic Capabilities Theory, the framework posits that the development of AI-CRM systems goes through four layers: data integration, intelligence generation, customer engagement, and feedback-driven optimization.