When Wanting To Be Fair Is Not Enough: The Effects of Depletion and Self-Appraisal Gaps on Fair Behavior
Bibliographic record
Abstract
Ensuring that managers engage in fair behaviors is critical for the effective functioning of organizations. Previous research has focused on increasing the enactment of interactional justice (i.e., justice as a dependent variable) by enhancing managers’ willingness to be fair. Drawing upon the limited strength model of self-regulation, we argue that the enactment of interactional justice may not depend solely on managers’ willingness or motivation but also on the extent to which managers have the self-regulatory resources required to engage in these behaviors. Using four experimental studies, our results indicate that the depletion of self-regulatory resources is negatively associated with the enactment of interactional justice. Furthermore, we argue that depletion can give rise to self-appraisal gaps (i.e., individuals’ ability to accurately appraise the fairness of their behavior is hampered), which can diminish the impetus to regulate fair behaviors (i.e., diminish interactional justice). Results provide support for self-appraisal gaps as an underlying explanation for why depletion can negatively affect the enactment of interactional justice. Moreover, the negative effects of depletion can be overcome by increasing managers’ awareness that they may be overestimating the fairness of their behavior. Theoretical and practical implications are discussed.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".