In the Aftermath of Unfair Events: Understanding the Differential Effects of Anxiety and Anger
Bibliographic record
Abstract
After decades of domination by social exchange theory and its focus on a manager-centered perspective, fairness scholars have recently issued numerous calls to shift attention toward understanding employees’ subjective “lived-through” experiences and in situ responses to unfair events. Using appraisal theories, we argue that focusing on the employee’s perspective highlights the importance of emotions in fairness experiences. Further, this emphasis creates opportunities for novel insights regarding the emotions that are likely to be relevant, the constructive responses that can emerge from unfairness, and the interplay between unfair events and entity fairness judgments. Using a daily diary study with event sampling, we highlight the importance of anger and anxiety in understanding how individuals experience and react to unfair events. Results indicated that anger elicited counterproductive work behaviors, whereas anxiety initiated problem prevention behaviors (i.e., a subdimension of proactive work behavior). Further, by engaging in problem prevention behaviors, employees can positively influence their subsequent overall fairness judgments. Experiences of an unfair event can also be shaped by individuals’ preexisting overall fairness judgments, such that preexisting overall fairness judgments are negatively associated with anger but positively associated with anxiety. Implications for theory and practice are discussed, including the influential role of emotions for fairness experiences, how employees’ own behaviors can influence subsequent overall fairness judgments, the interplay between unfair events and entity judgments, and ensuring that fairness is effectively managed on a daily basis.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".