1. KRISHNA MOHAN V - Professor, Department of Commerce and Management Studies, Andhra University, Visakhapatnam.
2. RATNA KUMARI M - Research Scholar, Department of Commerce and Management Studies, Andhra University,
Visakhapatnam.
The public sector is experiencing a transformative shift in talent acquisition, driven by the widespread adoption of E-Human Resource Management Systems (e-HRMS). This study investigates the profound impact of e-HRMS on recruitment and selection processes in the public sector. Historically known for bureaucratic procedures and lengthy hiring cycles, public sector talent acquisition is undergoing a paradigm shift through e-HRMS adoption. These systems harness advanced technologies, such as AI, data analytics, and automation, to streamline the entire talent acquisition journey. This research employs a mixed-methods approach, encompassing a literature review, surveys, and in-depth interviews with key public sector talent acquisition stakeholders. Both quantitative and qualitative techniques are used for data analysis. The findings demonstrate significant improvements resulting from e-HRMS adoption, including enhanced efficiency, reduced administrative burdens, and improved candidate experiences. Moreover, e-HRMS facilitates data-driven decision-making, enabling public sector organizations to identify top talent more effectively and promote diversity and inclusion. Based on these findings, we recommend the widespread adoption of e-HRMS in the public sector. Policymakers should prioritize investment in e-HRMS infrastructure and HR professional training. Public sector organizations should also focus on developing data analytics capabilities and implementing diversity and inclusion initiatives. In summary, e-HRMS is revolutionizing public sector talent acquisition, promising increased efficiency, diversity, and data-driven decision-making, ultimately creating a more agile and effective workforce.
E-Human Resource Management Systems, Public Sector, Talent Acquisition, Recruitment and Selection, Data-Driven Decision-Making.