Managers' Discretionary Adjustments: The Influence of Uncontrollable Events and Compensation Interdependence
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Discretionary bonus adjustments allow managers to restore the alignment of employee effort and compensation when bonus amounts are based on noisy objective performance measures. The implications of discretionary adjustments for employees' future efforts and fairness perceptions present important trade‐offs for managers to consider. Adjustments may be used to motivate different types of effort in future periods, but may also create perceptions of unfairness among employees who are not affected by negative events. This study examines the joint influence of the likelihood of future negative uncontrollable events and compensation interdependence (i.e., the extent to which one employee's compensation influences others' compensation) on managers' willingness to make adjustments for the effect of a negative uncontrollable event on a single employee. In our experiment, we manipulate the likelihood of future uncontrollable events and whether bonuses are determined individually or are drawn from a shared bonus pool. Results show that managers are less willing to adjust when the likelihood of future events is high to avoid setting a precedent, thereby motivating employees to adapt to changing conditions. We also find that managers are less willing to adjust, regardless of event likelihood, when compensation interdependence is high, to avoid demotivating unaffected employees. Finally, we find that participants' general attitudes toward compensation significantly influence their adjustment decisions beyond the effects of our independent variables. Our results highlight the unique nature of discretionary adjustments, help explain findings from previous research, and demonstrate important considerations managers must make when using the flexibility provided to them in pay‐for‐performance contracts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it