Is shooting for fairness always beneficial? The influence of promotion fairness on employees' cognitive and emotional reactions to promotion failure
Bibliographic record
Abstract
Abstract Promotions, as a part of organizational incentive and reward systems, can motivate employees to perform well and to increase commitment to their firms. But very little is known about why and when promotion failure influences employees' subsequent responses. Integrating social‐cognitive theory and the cognitive appraisal theory of emotion in justice literature, we investigated the effect of promotion failure on employees' work engagement through cognitive and emotional processes and the moderating effects of perceived promotion fairness. Employing two survey studies (Study 1 and Study 2) and an experimental study (Study 3), we found that: (1) promotion failure was negatively related to self‐efficacy and positively associated with anger; (2) promotion failure was negatively related to work engagement through reduced self‐efficacy and elevated anger; (3) promotion perceived to be fair amplified the negative effect of promotion failure on employee self‐efficacy but mitigated its influence on anger; (4) promotion fairness perception strengthened the indirect negative relationship between promotion failure and work engagement through self‐efficacy but weakened this indirect relationship via anger. Our work contributes to promotion and justice literature and enlightens practitioners about how to manage promotion practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".