The Effect of Relative Performance Evaluations on Employee Judgments of and Behavioral Responses to Managerial Monitoring
Bibliographic record
Abstract
ABSTRACT We examine whether and how a widely established finding—employees respond negatively to managerial monitoring—generalizes to different performance-evaluation systems. We predict that relative performance evaluations (RPEs), a common evaluation feature in multiagent settings, attenuate employees’ negative responses to managerial monitoring. The results of our experiment support our theory. Consistent with prior literature, we find that without RPE, employees respond significantly more negatively to managerial monitoring compared to no monitoring. However, we find that the effect of managerial monitoring on employee responses is moderated in the presence of RPE—employees respond similarly regardless of whether their manager implements a monitoring control. We provide robust analyses to support our theory-derived mechanisms, demonstrating that our results are driven by employees’ fairness perceptions of managers’ monitoring decisions. Collectively, this study aids in the understanding of how and under what circumstances managerial monitoring may be more or less beneficial. Data Availability: Available upon request. JEL Classifications: C90; D91; J31; M40.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".