AI-DRIVEN PERFORMANCE APPRAISAL AND EMPLOYEE OUTCOMES: A BIBLIOMETRIC ANALYSIS

Abstract

This study uses bibliometric analysis to assess the relationship between AI-Driven Performance Appraisal and Employee Outcomes. It identifies key themes, trends, and research gaps in the existing literature to provide insights into academic discussions and practical applications. A systematic review of 603 peer-reviewed articles from Scopus was conducted. Bibliometric techniques were employed to analyse metrics, source contributions, and the effect of AI-Driven Performance Appraisal and Employee to advanced technologies incorporating artificial intelligence and big data analytics. The output of AI-Driven Performance Appraisal results from decreased operational expenditures and boosted employee productivity in combination with defined workforce goals that benefit organisational targets. The research identifies leading authors along with crucial academic journals and primary geographic regions working within the AI-Driven Performance Appraisal and Employee Outcomes, demonstrating an active ethical research domain.