When Do Firms Adjust Bonus Targets <scp>Intrayear</scp>? Evidence from Sales Executives' Targets*
Bibliographic record
Abstract
ABSTRACT This study investigates when and why intrayear bonus target revisions occur. This is important as intrayear target revisions occur regularly in practice but are not well understood. Specifically, we analyze two potential drivers of intrayear bonus target revisions: reduced managerial incentives owing to managers dropping out of the incentive zone of their piecewise defined bonus function and potential spillovers from planning target revisions that reflect changes in performance expectations during the year. We also investigate the effects of organizational characteristics on intrayear bonus target revisions. Using data collected from sales executives via multiple waves of surveys, we find evidence for both predicted drivers. In addition, consistent with our predictions, we find that the levels of delegated decision authority, intrafirm interdependencies, and information asymmetry negatively moderate the positive association between reduced managerial incentives and revision likelihood. Our paper contributes to the target setting literature by being the first study to investigate intrayear bonus target revisions and shed light on when firms commit to not revising such targets intrayear.
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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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".