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TRUVACE RECORD VERSION
record: TRV-2026-2532
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:49:33.362691Z
status: published
lens: trace
sector: labor
headline: Source matters, AI anxiety less so: comparing employee reactions to human, AI, and hybrid performance feedback and the limited role of AI anxiety
dek: 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: = 192) examining employees' reactions to a hypothetical performance feedback scenario. Participants were randomly assigned to…
gain_title: In a 192-employee vignette experiment, feedback a human wrote and then refined with AI did not differ significantly from purely human feedback and beat fully AI-generated feedback on all outcomes except rumination.
problem_title: In a 192-employee vignette experiment, identical feedback disclosed as fully AI-generated scored lower than human-written feedback on work motivation (3.66 vs 4.79 on a 7-point scale) and on the supervisor relationship (3.23 vs 4.69).
trace_subject: Disclosed AI involvement in managers' performance feedback
gain_reading: In a 192-employee vignette experiment, feedback a human wrote and then refined with AI did not differ significantly from purely human feedback and beat fully AI-generated feedback on all outcomes except rumination.
gain_evidence: human-written feedback refined by AI did not differ significantly from purely human feedback and was evaluated more positively than fully AI-generated feedback on all outcomes except rumination
problem_reading: In a 192-employee vignette experiment, identical feedback disclosed as fully AI-generated scored lower than human-written feedback on work motivation (3.66 vs 4.79 on a 7-point scale) and on the supervisor relationship (3.23 vs 4.69).
problem_evidence: fully AI-generated feedback was evaluated less favorably than fully human feedback on affective response (EMMs = 3.86 vs. 4.76, p = 0.002) | relationship with the supervisor (EMMs = 3.23 vs. 4.69, p < 0.001)
quick_read: A vignette experiment with 192 employed Hungarian adults tested how people react to the same performance-review text when told it was written in different ways. Those told the feedback was fully AI-generated gave lower ratings than those told a human wrote it, for example 3.23 versus 4.69 on the effect on the supervisor relationship and 3.43 versus 5.31 on willingness to ask the supervisor for help.

Hybrid workflows landed in between. Feedback a human wrote and then refined with AI was statistically indistinguishable from purely human feedback. AI-generated feedback reviewed by a human beat fully AI-generated feedback only on relational measures: openness to ask for help, supervisor relationship and psychological distance.

AI anxiety, measured with a 21-item scale, mattered little. It was linked only to more rumination after feedback and did not change how people reacted to the different sources. The authors caution that the effects came from a hypothetical scenario and may overstate what would happen with real managers.
limitation: The study used a hypothetical vignette with 192 Hungarian convenience-sample participants and single-item measures; the authors say the largest differences (up to d = 1.46) may reflect label salience or demand characteristics and overstate real workplace effects.
tag: Dual reading
key_points: Participants were randomly assigned to four disclosed workflows: fully human-written (42), fully AI-generated (56), AI-generated and human-refined (44), and human-written and AI-refined (50). The feedback text was identical. | Fully AI-generated feedback scored lower than human feedback on willingness to correct the mistake (4.43 vs 6.02) and on organizational commitment (3.46 vs 4.57). | Expected performance was rated 4.29 for AI-generated feedback versus 5.19 for human feedback. | Rumination did not differ by feedback source. | AI-generated feedback refined by a human beat fully AI-generated feedback on openness to ask for help (4.57 vs 3.43), supervisor relationship (3.98 vs 3.23) and psychological distance. | The two hybrid workflows did not differ significantly from each other. | AI anxiety was significantly associated only with rumination (partial eta squared 0.07); its overall multivariate effect was not significant (p = 0.085). | Among 123 participants who correctly identified the disclosed source, effects were larger, with eta squared from 0.19 to 0.42.
rundown: Data were collected in 2025 through an online Qualtrics survey. Of 230 Hungarian participants recruited via social media and professional networks, 18 unemployed people and 20 who failed an attention check were excluded, leaving 192 employees (54.69% female, ages 19 to 65, mean 32.34). Two-thirds worked at subordinate level.

Participants imagined receiving a review excerpt with both praise (meeting deadlines) and a development point (communication in joint discussions). They rated eight single-item outcomes on 7-point scales plus a four-item psychological distance scale. A multivariate analysis showed a significant overall effect of source (Pillai's V = 0.37, p < 0.001), with effect sizes up to eta squared 0.24 for psychological distance.

Only 64.06% (123 participants) correctly identified the disclosed source in the manipulation check. Restricting the analysis to them reproduced the main pattern with larger effects. AI anxiety scores did not differ across conditions.

The authors conclude that managers should not replace their own judgment with AI and that using AI to refine human-written feedback looks viable, though they note that efficiency and quality benefits were not measured in this study.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1924487 | 2026-08-17
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