Navigating the Productivity-Engagement Paradox: A Socio-Technical Framework for Algorithmic Management in Talent Acquisition and Workforce Governance

Abstract

This study aims to analyze the sociotechnical transformation of Human Resource Management (HRM) driven by the adoption of Algorithmic Management (ALMA). Specifically, it explores the impact of automated systems on applicants' fairness perceptions during talent acquisition and evaluates how algorithms reconfigure internal power dynamics and employee engagement. The core problem lies in the widening gap between algorithmic operational efficiency-which delivers a 14.6% productivity increase-and the erosion of qualitative human experiences. The reliance on "black-box" decision-making models often leads to the deterioration of the psychological contract and a "uniqueness paradox" in hiring, where individuals feel reduced to standardized data points. The study employs a Systematic Literature Review (SLR) integrated with Secondary Data Analysis (Meta-analysis) of 83 academic sources and international institutional reports (2015-2025). The methodology adheres to the PRISMA 2020 guidelines to ensure rigorous quantitative synthesis and objectivity. The analysis reveals that while ALMA increases recruitment efficiency by 89.6%, it triggers "algorithmic anxiety" and resistance when transparency is absent. Implementing Explainable AI (XAI) is found to raise system acceptance rates from 47% to 81%. Furthermore, results indicate that AI dependence fosters employee "voice behavior" only when moderated by supportive "coaching leadership".