Procedural justice criteria in salary determination
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this research is to identify the dimensionality of the procedural justice construct and the criteria used by employees to assess procedural justice, in the context of salary determination. Design/methodology/approach Based on a survey of 297 Canadian workers, the paper uses confirmatory factor analysis (CFA) to test the dimensionality and the discriminant and convergent validity of our procedural justice construct. Convergent and predictive validity are also tested using hierarchical linear regressions. Findings The paper shows the multidimensionality of the procedural justice construct: justice of the salary determination process is assessed through the perceived characteristics of allocation procedures, the perceived characteristics of decision‐makers, and system transparency. Research limitations/implications Results could be biased towards acceptance; this is discussed. The results also suggest possible extensions to the study. Practical implications Knowledge of the justice standards improves the ability of organizations to effectively manage the salary determination process and promote its acceptance among employees. Emphasizes the need to adequately manage the selection, training, and perception of decision makers. Originality/value The paper identifies the standards of procedural justice for salary determination processes. It contributes to the theoretical literature by providing a new multidimensional conceptualization, which helps to better understand the psychological process underlying the perception of procedural justice. The presence of a dimension associated with decision makers is novel and critical for compensation studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it