Managers' motivation to evaluate subordinate performance
Bibliographic record
Abstract
Purpose This paper aims to focus on one of the most frequently cited problems with respect to the performance management process: the prevalence of performance appraisal distortion. Design/methodology/approach Through semi‐structured interviews with managers, this paper attempts to answer the following question: Which factors influence managers' motivation to distort the performance evaluation ratings of their subordinates? Findings This paper offers three main contributions or implications. First, from a methodological point of view, using a qualitative research design to investigate the appraisal of subordinates' performance is useful because it allows us to reduce the gap between research and practice. Second, this study shows that researchers must embrace or integrate various theoretical perspectives (rational, affective, political, strategic, cultural, justice, and symbolic), given that managers' motivation to evaluate subordinate performance cannot be analyzed outside of the social context. Third, from a practical point of view, managers' motivation to evaluate subordinate performance is less about the technique used and more about leadership support, execution, and overall performance culture. Originality/value To date, prior research has focused on improving performance appraisal accuracy through experimental research design by emphasizing rating criteria, rater errors, rater training, and the various rating methods. Despite extensive research, very little progress has been made toward improving rater accuracy.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".