Disadvantaged but not dissatisfied: How agency ameliorates negative reactions to unequal pay.
Bibliographic record
Abstract
Workers tend to be dissatisfied when their peers receive more than them for doing the same work. The fear of creating such dissatisfaction may cause leaders in organizations to waste resources that cannot be allocated equally between their workers. Here we explore the effectiveness of a procedure designed to reduce such waste by empowering workers with the agency to decide whether or not to pay other workers more. We predict that workers' sense of agency reduces their dissatisfaction with others' better outcomes. Seven studies supported this prediction by demonstrating that agentic participants, who were involved in creating allocations, tended to be more satisfied with others' better outcomes than nonagentic participants, who were not involved in creating allocations. Longitudinal lab studies, measuring real behavior, showed that agentic participants remained more satisfied than nonagentic ones even five weeks after their initial decision. The findings provided evidence for two mechanisms underlying the effect: increased feelings of generosity, and reduced perception of unfairness. We found that the agency procedure was comparable with other fair procedures in its ability to improve worker satisfaction. We discuss our findings in relation to the literatures on social preference, fairness, and voice, and highlight the implications for organizational efficiency. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".