Perceived Controllability and Fairness in Performance Evaluation
Bibliographic record
Abstract
We investigated the effects of environmentaluncertainty, decentralization of decisions rights,and the use of subjective performance measures onmanagers’ perceptions of outcome controllabilityand performance evaluation fairness. Basedon a survey of 339 middle- and upper- levelmanagers, our results suggest that environmentaluncertainty adversely affects perceptions ofoutcome controllability and that this effectis not moderated by the decentralization ofdecision rights. Our results also show a positiveassociation between perceived controllabilityand performance evaluation fairness. Althoughwe found no direct effect of the use of subjectiveperformance measures on perceived performanceevaluation fairness, it appears to moderate thepositive effect of perceived controllability onfairness. We also show that the positive effectof the use of subjective measures may dependon contextual and job-related factors. Theoverall results underscore the need to considerthe organizational context (environmentaluncertainty and decentralization of decisionrights) to investigate how performance measuresaffect perceived controllability and fairness.Because perceived controllability and fairnessaffect individual attitudes and behaviors withinan organization, our results have importantimplications for the design and use of performanceevaluation systems.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".