68/100

Moderate evidence

ProblemPublished recordLaborProblem Space

How AI negative feedback shapes employee appraisals and job crafting

Truvace record
TRV-2026-2493raw data (JSON)
Version
v1Published
First published
Evidence type
Controlled or validated comparison
Sources
1 sourcePeer-reviewed
sha256
2a25f34fb807…check it

Artificial intelligence increasingly delivers workplace performance feedback, but responses to AI-delivered negative feedback remain unclear. Drawing on cognitive appraisal theory, we examine how feedback source is associated with job crafting through challenge and hindrance appraisals and whether these relationships depend on performance goal orientation. Study 1, a scenario experiment with 406 working participants, found that AI-delivered negative feedback elicited stronger challenge appraisal among employees…

The problem

In a scenario experiment with 406 workers and a daily study of 280 employees, AI-delivered negative feedback produced stronger hindrance appraisal among those high in performance-avoidance goal orientation.

What this doesn’t fix

The first study used a scenario experiment, and the authors describe the findings as associations with job crafting rather than uniform effects of AI feedback.

Sources

1 on file
  1. The Service Industries Journaldoi:10.1080/02642069.2026.2754018
    Peer-reviewed2026-11-03

Cite this record

v1

Truvace Impact Record TRV-2026-2493, v1: “How AI negative feedback shapes employee appraisals and job crafting.” Truvace, 2026-11-12. /record/TRV-2026-2493 (accessed on the day you copy it). sha256 2a25f34fb807dca5…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv12a25f34fb807…

    Certified into the record

Check this record hasn’t been altered

Check this record hasn’t been altered
How to check it yourself, without trusting this page

Download any version from /api/record/TRV-2026-2493 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.