Bibliographic record
Abstract
ABSTRACT This study investigates whether task interdependence in teams alters the effectiveness of managerial discretion in motivating team performance. Teams are particularly useful when employees' tasks are interdependent—that is, when the degree to which the increase in team performance resulting from a team member's effort depends on the effort and skills of the other team members. The reason is that the more interdependent tasks are, the more employees need to coordinate their actions and help one another to achieve their objectives. Prior research analyzing settings where task interdependence is absent suggests that providing managers with discretion over team bonus allocation can improve team performance relative to equal team bonus allocations because it strengthens the link between contributions to team output and rewards. Economic theory suggests that managerial discretion will also improve team performance when task interdependence is present and information is efficiently used. However, we use behavioral theory to predict that managerial discretion is less effective in the presence of task interdependence, because managers do not fully incorporate all relevant information into bonus decisions and because managerial discretion hurts coordination and helping, which is particularly problematic under task interdependence. We find that while discretion over compensation has a positive effect on team performance relative to equal bonus allocation when task interdependence is absent, it has a negative effect when task interdependence is present. Additional analyses provide support for our underlying theory. Results of our study contribute to both theory and practice by suggesting that, ironically, managerial discretion may be most useful when the potential benefits of employing teams are lowest and least useful when the potential benefits are highest. Our results help explain why firms often grant managers only partial or no discretion over team members' compensation.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".