Consequences of the performance appraisal experience
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the role of low quality performance appraisals (PA) on three human resource management outcomes (job satisfaction, organisational commitment and intention to quit). Design/methodology/approach Using data from 2,336 public sector employees clusters of PA experiences (low, mixed and high) were identified. Regression analysis was then employed to examine the relationship between low quality PA experiences and job satisfaction, organisational commitment and intention to quit. Findings Employees with low quality PA experiences (relative to those with mixed and high quality PA experiences) were more likely to be dissatisfied with their job, be less committed to the organisation and more likely to be contemplating leaving the organisation. Research limitations/implications The data were collected in a large public sector research organisation where the results of the appraisal were linked to pay increments. Further research is needed to determine the applicability of the results to private sector employees. Practical implications The quality of the PA experience varies and a low quality experience results in lower job satisfaction and organisational commitment and higher quit intentions. The challenge for human resource (HR) practitioners is to decide whether the allocation of additional resources to ensure that all employees have a uniformly high quality PA experience is a worthwhile investment. Originality/value Research has tended to focus on the relationship between a single feature of a PA process and HR outcomes. Organisations need to acknowledge the importance of the overall PA experience when evaluating its consequences for HRM outcomes.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| 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".