When Peer Recognition Backfires: The Impact of Peer Information on Subsequent Helping Behavior*
Bibliographic record
Abstract
ABSTRACT Peer recognition systems (PRS) have gained popularity in recent years as a means for organizations to promote employee helping behavior. However, there are theoretical reasons to believe that peer information that is publicly disclosed in PRS may reduce subsequent helping behavior, and I use an experiment to test my theory. Specifically, I examine a three‐employee setting where an employee (the worker) receives no recognition for helping a coworker (the recognizer) but another coworker (the helper) does. I predict and find that the worker's willingness to subsequently help the recognizer/helper is lower when the worker perceives that the worker's initial help exceeds (vs. subceeds) the helper's. I also find that the worker's perception of fairness mediates the process, and the worker's willingness to help the recognizer has a spillover effect on the worker's willingness to help the helper. My study provides the first empirical evidence of the negative impact that PRS have on helping behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".