APPLYING DATA-DRIVEN DECISION-MAKING TO ACADEMIC HIRING PROCEDURES: AN HR ANALYTICS IN HIGHER EDUCATION

Abstract

Higher education's adoption of data-driven decision-making (DDDM) in hiring processes for academic positions holds the potential to revolutionize HRM. This case study examines the implementation of HR analytics at a prestigious university, focusing on the impact on hiring administrators and professors. The study aims to understand how HR analytics can aid strategic planning, enhance hiring efficiency, and improve candidate selection. Using a mixed-methods approach, we analyzed quantitative recruitment data and qualitative interviews with academic administrators and HR professionals. Findings indicate that HR analytics significantly enhances recruitment diversity, reduces time-to-hire, and improves the accuracy of candidate assessments. The university leveraged predictive analytics to streamline the hiring process and better identify high-potential candidates. The study also highlights implementation challenges such as data integration, staff training requirements, and data protection concerns. By addressing these challenges, the institution aligned its recruitment process with strategic goals of diversity and quality, making it more equitable and transparent. This case study contributes to the growing body of research on DDDM in higher education, offering practical insights and recommendations for institutions looking to implement HR analytics in their hiring processes.