Adaptive HRM Selection Practices in Pune IT Firms: Skill-Based and Technology-Enabled Recruitment Insights

Abstract

Human Resource Management (HRM) selection processes critically influence organizational performance in knowledge-intensive sectors such as Information Technology (IT). This study examines the selection practices of large IT firms operating in Pune, including TCS, Infosys, Wipro, and HCL Technologies, against the backdrop of a recent industry-wide hiring slowdown. Using a mixed-methods approach, data were collected through structured surveys of HR professionals and secondary industry reports. The study employs descriptive statistics, correlation, regression, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA) to analyze multi-stage selection processes encompassing resume screening, online assessments, technical and behavioral interviews, and final evaluation. Results indicate that firms rely heavily on technology-enabled recruitment and emphasize skill-based evaluation, particularly in emerging domains such as AI, cloud computing, and cybersecurity. EFA identifies four latent dimensions of selection effectiveness: technology adoption, technical competency assessment, behavioral and cultural evaluation, and strategic talent potential, while CFA confirms the construct validity of this four-factor model. Despite minimal net hiring in FY 2025–26, campus recruitment remains a significant source of talent. The study underscores the importance of adaptive, data-driven selection strategies that balance quality with efficiency, aligning HR practices with organizational goals. These findings provide actionable insights for HR managers aiming to enhance recruitment effectiveness and contribute to theoretical understanding of HRM selection frameworks in IT firms under evolving workforce dynamics.