The impact of non‐performance information on ratings of job performance: A policy‐capturing approach
Bibliographic record
Abstract
Abstract Researchers have suggested that rater motives and the organizational context should be considered as sources of performance appraisal inaccuracies. A review of the performance appraisal literature revealed three primary non‐performance factors that managers consider when rating employee performance: (a) Potential negative consequences of ratings, (b) organizational norms, and (c) the opportunity to advance self‐interests. Using a policy‐capturing methodology, the current study investigated if these three non‐performance factors, as well as individual rater differences (e.g., conscientiousness, agreeableness, and performance appraisal experience), influence performance ratings. A sample of 303 experienced managers rated the performance of a fictitious employee, featured in a series of hypothetical scenarios, in which the above information was manipulated. Using hierarchical linear modeling, the results revealed that each of the three non‐performance related considerations accounted for variance incremental to objective employee performance. Managers' performance appraisal experience also predicted ratings, such that more experience was associated with lower ratings. These results provide support for the view that non‐performance factors can be a substantive component of performance ratings. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.178 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".