The Impact of Better-than-Average Bias and Relative Performance Pay on Performance Outcome Satisfaction
Bibliographic record
Abstract
Drawing on equity and expectancy theories, we hypothesize that the perception of accountants about their ability to contribute relative to a peer (operationalized as the better-than-average [BTA] bias) negatively influences their satisfaction with the outcomes of the performance evaluation process (operationalized as performance outcome satisfaction [POS]). We hypothesize further that this negative influence is mitigated by the amount of relative performance pay. We test these hypotheses using data collected from a survey of and an experiment involving 164 entry-level accountants. We found that in general our participants rated themselves better than the average audit professional and their immediate work associate; that is, they displayed a BTA bias. Moreover, we found that both the BTA bias and performance pay individually influenced POS; we also found a moderately significant interaction effect. In their entirety, the results indicate that the greater an entry-level accountant believes that she or he is better than average the more likely her or his performance outcome satisfaction will fall.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".