Source matters, AI anxiety less so: comparing employee reactions to human, AI, and hybrid performance feedback and the limited role of AI anxiety.

INTRODUCTION

Large language models (LLMs) are increasingly integrated into human resource management practices and are becoming capable of supporting managerial tasks such as performance feedback. Although these technologies may improve the efficiency and quality of feedback delivery, it remains unclear whether knowledge of AI involvement influences recipients' reactions to it.

METHODS

This work presents results from a vignette-based experiment (N = 192) examining employees' reactions to a hypothetical performance feedback scenario. Participants were randomly assigned to one of four disclosed feedback-production workflows (fully human-written, fully AI-generated, AI-generated and human-refined, or human-written and AI-refined), while the feedback text itself was identical across conditions. Participants evaluated the feedback on a range of emotional, motivational, relational, and performance-related outcomes while AI anxiety was measured as a covariate in the analysis.

RESULTS

Results demonstrated that fully AI-generated feedback elicited less favorable affective, motivational, relational, and performance-related responses than fully human-written feedback, whereas hybrid feedback generally occupied an intermediate position, with human-written feedback refined by AI producing outcomes comparable to purely human feedback. AI anxiety showed only limited effects.

DISCUSSION: These findings suggest that the perceived source of performance feedback influences how feedback is evaluated, whereas AI anxiety showed only limited associations with employees' reactions. The discussion addresses the theoretical implications of these findings for performance feedback, as well as their practical implications for organizations.