The performance appraisal congruency scale: an assessment of person‐environment fit
Bibliographic record
Abstract
Purpose The purpose of this paper is to construct an instrument to assess employee‐perceived performance appraisal congruency and then to use the scale to predict employee attitudes about their performance appraisal systems. Design/methodology/approach The scale was developed using 28 subject‐matter experts and researcher knowledge of the extant literature. The scale was then completed by a sample of 135 individuals using internet administration. Findings Regression analyses showed that performance appraisal congruency predicted overall system satisfaction, perceived usefulness and fairness. Supplementary analyses of the performance appraisal congruency items were conducted so as to refine the original instrument for future research. Research limitations/implications Limitations of the study include: the interviews conducted to develop the instrument were conducted in a single organization; the study used an internet sample that was made up of university alumni; all measures were self‐report; and single item measures were used as the criterion variables. The findings support the utility of the use of the P‐E fit model in performance management systems. Future research should assess outcomes that would be of interest to organizations, such as the relationships with performance system satisfaction and employee commitment and turnover. Practical implications If employees perceive that the performance appraisal system is congruent with their expectations, then positive outcomes should be expected. Originality/value While congruency has been linked to important outcomes such as job satisfaction, organizational commitment, turnover intention, and actual turnover, it has not been used within a performance appraisal framework.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".