South Asian Journal of Social Studies and Economics

Human behavior in organizations reflects how employees perceive and respond to workplace constraints, which vary across individuals and significantly influence performance outcomes amid skill shortages and operational challenges. Currently, organizations are facing various employee-related constraints, including skill deficiencies, excessive workloads, limited resources, and barriers to effective decision-making, which collectively impede organizational performance and success. This study introduces a new theoretical framework of the Constraint-Responsive Human Behavior Theory (CRHBT) that addresses organizational constraints with AI-driven Human Resource Management (AI-HRM). The study employed Swanson and Chermack’s five-phase theory-building method for the development of the theory. The theory suggests that the application of AI-driven human resource management (AI-HRM) systems can improve traditional HR practices. These systems assist to modernize recruitment, training and development, and performance management, while also enhancing the speed, accuracy, and better organizational decision-making. The study findings show that the CRHBT framework connects organizational constraints with technological solutions. These solutions enable organizations to enhance their performance and improve operational efficiency. The study contributes to the existing literature by demonstrating how AI technologies enable organizations to overcome operational constraints and strengthen long-term performance outcomes within human resource management contexts. The study is subject to certain limitations.