Amina Elshamly

Type of the article: Research Article

Abstract
This study examines how artificial intelligence (AI) is associated with perceived performance management efficiency of organizations in the United Kingdom. Drawing on the technology acceptance model (TAM), the study tests a revised partial least squares structural equation modeling (PLS-SEM) model in which AI implementation level and AI sophistication predict perceived usefulness, AI training for HR teams predicts perceived ease of use, perceived ease of use predicts perceived usefulness, and perceived usefulness, together with AI utilization maturity, predicts performance management efficiency. Data were collected from 320 decision-makers in the United Kingdom using AI in performance management. The results support all six hypotheses: AI implementation level (beta = 0.188, p < .001), AI sophistication (beta = 0.222, p < .001), AI training for HR teams (beta = 0.256, p < .001), perceived ease of use (beta = 0.336, p < .001), perceived usefulness (beta = 0.324, p < .001), and AI utilization maturity (beta = 0.405, p < .001) were positively associated with their respective endogenous constructs. The model explained 36.0% of the variance in perceived performance management efficiency. The findings contribute to research on AI-enabled HR analytics by showing how organizational AI capabilities and TAM perceptions jointly predict perceived improvements in performance management.