Evaluation of Performance Appraisal Methods through Appraisal Errors by Using Fuzzy VIKOR Method
Bibliographic record
Abstract
Performance appraisal has a vital importance both for employees’ motivation and organizations’ effectiveness. However, unless using a true and equitable performance appraisal method, which is debugged from appraisal errors, an effective performance appraisal can not be attained. The aim of this study is to evaluate the performance appraisal methods with regard to the appraisal errors in an attempt to rank them according to their level of clarity from the errors. To this end, the evaluation results of 29 Human Resources managers evaluated for 11 performance appraisal techniques against 8 potential appraisal errors are dealt within this study. These evaluations were analyzed by fuzzy VIKOR method and a consequent list of a performance appraisal methods by rank was achieved. According to the findings of the study, the most accurate alternative was determined as the Graphic Rating Scales Method while the least one was the Comparison Method. It is suggested that human resources managers should choose the most appropriate appraisal method for their organizations by following the steps that presented in this study.
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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 | 0.059 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".