Manikanta R

https://doi.org/10.64823/ijpeh.2601005

Artificial intelligence (AI) is increasingly transforming organizational work by enabling humans and intelligent systems to collaborate in decision-making, problem-solving, and operational activities. Despite rapid adoption, existing research remains fragmented across organizational behavior, human factors, information systems, and AI governance, providing limited guidance on how organizations can effectively design and manage human–AI collaboration. This conceptual paper develops the Collaborative Intelligence Framework (CIF) through an integrative review of peer-reviewed literature published between 2020 and 2026. The framework identifies four essential conditions for successful human–AI teaming: task interdependence, calibrated trust, role clarity, and organizational enablement. It explains how these conditions promote effective collaboration while reducing the risks of algorithmic aversion and excessive reliance on AI recommendations. The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design. The paper contributes to the growing literature on responsible AI by providing a structured framework that can guide researchers and practitioners in designing sustainable human–AI collaboration. It concludes by discussing managerial implications, study limitations, and opportunities for future empirical validation across diverse organizational and occupational contexts.

https://doi.org/10.64823/ijebm.2601006

This study examines the economic contributions generated by household death-related activities in urban India and provides one of the first multi-sector empirical analyses of the country's mortality-driven economy. Despite millions of deaths occurring annually and triggering substantial economic activity across formal and informal sectors, the economic dimensions of mortality remain underexplored in Indian literature. The study utilizes primary survey data collected from 283 vendors across nine sectors in Bengaluru, including funeral services, transport, healthcare, flower markets, hospitality, religious services, textile supply, printing and media, and legal and financial services. Descriptive statistics, Pearson correlation analysis, and ordinary least squares (OLS) regression were employed to examine demand patterns, revenue determinants, and sectoral interdependencies. The findings reveal that the surveyed sectors collectively generate approximately ₹4.40 crore in monthly revenue, demonstrating the significant scale of mortality-linked economic activity. Funeral services recorded the highest average vendor revenue, while transport services generated the largest aggregate sectoral revenue. Regression models showed strong explanatory power, with adjusted R-squared values ranging from 0.87 to 0.98. The study identifies three distinct economic archetypes within the death economy: volume-driven sectors, value-driven sectors, and balanced sectors. The findings contribute to the emerging field of death economics by establishing a sectoral taxonomy, quantifying mortality-related economic circulation, and offering policy recommendations for household financial protection, market formalization, and service-sector development in India.

The rapid diffusion of artificial intelligence (AI) across recruitment, performance evaluation, workforce planning, and employee monitoring has outpaced the governance structures needed to ensure these systems are used responsibly. This conceptual paper examines AI governance in human resource management (HRM) as an emerging organizational capability that integrates trust, accountability, and responsible decision-making into the design and deployment of algorithmic HR systems. Drawing on a non-empirical, literature-based synthesis of scholarship published between 2020 and 2026, the paper traces the evolution of AI adoption in HRM, the ethical and regulatory pressures created by instruments such as the European Union's Artificial Intelligence Act, and the persistent gap between technical capability and organizational governance readiness. The review identifies four recurring themes in the literature: algorithmic fairness and employee justice perceptions, transparency and explainability, data governance and privacy, and the erosion of managerial accountability when decisions are delegated to opaque systems. Building on stakeholder theory, institutional theory, and the technology acceptance literature, the paper proposes an integrative conceptual framework, termed the AI-HR Governance Triad, that positions trust, accountability, and responsible decision-making as interdependent pillars supported by organizational, technological, and regulatory enablers. The discussion synthesizes how these pillars interact to shape employee acceptance, legal compliance, and organizational legitimacy. The paper concludes with managerial implications for HR leaders seeking to institutionalize governance practices, acknowledges the limitations inherent in a conceptual, non-empirical approach, and outlines a future research agenda centered on empirical validation of the proposed framework across organizational and cultural contexts.

Organizations are increasingly adopting people analytics and artificial intelligence (AI) to improve workforce planning and strategic human resource management. However, research on people analytics and AI-enabled HRM has largely evolved as separate streams, creating a fragmented understanding of workforce intelligence. This narrative literature review synthesizes peer-reviewed studies published between 2020 and 2026 to develop an integrated perspective on workforce intelligence as a strategic organizational capability. The review examines the evolution of people analytics, the integration of AI into HR decision-making, organizational capabilities required for successful implementation, and the ethical challenges associated with AI-driven workforce management. Building on resource-based view and dynamic capabilities theory, the paper proposes a Workforce Intelligence Maturity Model that explains how organizations progress from descriptive HR analytics to integrated AI-supported strategic workforce intelligence. The model highlights the importance of governance, analytical capability, change management, transparency, and ethical AI practices in creating long-term organizational value. The study contributes to the growing literature on AI-enabled HRM by providing an integrative conceptual framework for researchers and practitioners. It concludes with managerial implications, identifies limitations of the literature-based approach, and recommends future empirical research to validate the proposed maturity model across different organizational contexts.

The phrase "weaker gender" continues to influence social attitudes despite growing psychological and social evidence demonstrating women's resilience under adversity. This paper critically examines this stereotype through a qualitative observational research approach informed by lived observations across family life, workplaces, public transport, community settings, and everyday social interactions. Drawing upon established psychological theories including resilience, post-traumatic growth, self-efficacy, emotional labour, ethic of care, and social identity, the study interprets recurring patterns of women's endurance, caregiving, sacrifice, and informal leadership. These observations are further examined alongside documented national and international evidence relating to unpaid care work, labour-force participation, domestic violence, and community-based collective action to provide broader social context. The analysis suggests that psychological strength extends far beyond physical capability and includes resilience, adaptive coping, emotional regulation, long-term responsibility, and leadership under sustained adversity. The findings challenge the continued use of the "weaker gender" stereotype and argue that it fails to reflect contemporary psychological understanding of human strength. The paper concludes by recommending greater recognition of women's invisible labour, psychological resilience, and social contributions within education, workplaces, public policy, and mental health discourse, while encouraging a more accurate and evidence-informed understanding of strength in contemporary society.