The self‐rater's personality and self‐other disagreement in multi‐source performance ratings
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine relationships between a priori‐chosen personality traits and the tendency for a manager to rate his/her job performance more favourably than well‐acquainted superiors, peers, and subordinates do. Design/methodology/approach The job performance of 204 managers was evaluated using multi‐source (i.e. 360E) ratings (self, subordinates, peers, and superiors). Managers also completed personality measures. Relationships between managers' personality and the tendency for managers to rate their own job performance higher than subordinates, peers, and superiors did were analyzed using advanced regression techniques. Findings The paper finds that self‐superior and self‐peer disagreement in performance ratings (i.e. self‐rating inflation) was associated with high Achievement and high Self‐Esteem. Additionally, self‐superior disagreement (i.e. self‐rating deflation) was associated with high Anxiety. Self‐subordinate disagreement was not associated with self‐rater personality. Research limitations/implications The paper studied a single sample of financial services managers. Generalization requires cross‐validation with other occupational groups and organizations. Practical implications Human resources professionals should be informed that self‐superior and self‐peer disagreement (i.e. self‐rating inflation) in multi‐source job performance ratings is potentially beneficial because it is associated with personality traits that can facilitate positive responses to feedback. Peers and superiors should therefore not inflate their ratings of managers in an effort to reduce self‐superior and self‐peer disagreement in ratings. Originality/value This study improved upon most previous investigations of this topic by using a field setting, considering a wider range of personality variables, using 360( job performance ratings (self‐, supervisor‐, peer‐, and subordinate‐ratings) rather than just a subset of these rating sources, and employing superior statistical procedures.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".