Unpacking the relationship between procedural justice and job performance
Bibliographic record
Abstract
Purpose This study investigates the mediating role of improvisation behavior in the relationship between employees' perceptions of procedural justice and their job performance, as evaluated by their supervisors, as well as the invigorating role of their organization-based self-esteem in this process. Design/methodology/approach Survey data were collected in three rounds among employees and their supervisors in Pakistan. Findings An important factor that connects procedural justice with enhanced job performance is whether employees react quickly to unexpected problems while carrying out their jobs. This mediating role of improvisation is particularly salient to the extent that employees consider themselves valuable organizational members. Practical implications For organizations, this study pinpoints a key mechanism—willingness to respond in the moment to unanticipated organizational failures—by which fair decision-making processes can steer employees toward performance-enhancing activities. It also reveals how this mechanism can be activated, namely, by ensuring that employees feel appreciated. Originality/value Improvisation represents an understudied but critical behavioral factor that links employees' beliefs about fair decision-making procedures to enhanced performance outcomes. This study shows, for the first time, how this beneficial role can be reinforced by organization-based self-esteem, as a critical personal resource.
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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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".