Employees’ Perception of AI- Based HRM Practices: A Systematic Literature Review and Future Research Agenda

Abstract

Purpose: This systematic literature review maps present research on how employees’ perceive Artificial Intelligence based HRM. Analyzing and synthesizing evidence through the dual theoretical lenses of the Technology Acceptance Model (TAM) and Organizational Justice Theory, we examine the socio-technical factors shaping adoption of AI based HRM in evolving markets with attention to South Asian IT sector. Design/ Methodology/ Approach: We searched academic databases and Google Scholar for studies published between years 2017-2026, adhering to PRISMA 2020 guideline. A final 68 peer reviewed articles underwent for qualitative thematic analysis and conceptual synthesis. Findings: The synthesis reveals that employee system trust and adoption are heavily mediated by perceived procedural fairness and algorithmic transparency (explainable AI). While automated AI tools speedup high volume talent acquisition, opaque “black-box” evaluations and algorithmic surveillance induce techno-stress, resistance, and feelings of dehumanization. Employees adapt much better to AI when they feel confident using technology, trust the company’s leadership, and receive honest, clear communication from management. Informational justice and management trust act as primary buffers against algorithmic anxiety. Research Limitations/Implications: This paper builds a simple, step-by-step roadmap showing how AI feature design affects employee feelings, and how those feelings drive actual workplace behavior. It highlights what current research has missed specifically showing that we need long-term studies across different industries, especially in fast-growing, developing countries. It provides practical advice for companies on how to introduce AI responsibly while keeping human values at the center. Value: This study offers an integrated dual-anchor framework (TAM+ Organizational Justice) bridging technological usability with ethical fairness evaluations in automated management.