Strategic Bias in Team Members’ Communication about Relative Contributions: The Effects of Voluntary Communication and Explanation
Bibliographic record
Abstract
ABSTRACT One significant challenge of motivating team performance is that the contribution of individual team members is difficult to observe. In this setting, managers often seek private information about team members’ relative contributions to help allocate team bonuses. However, the presence of self-interested biases in such communication, especially strategic bias, could reduce its informational value. We study the effects of two features of team members’ communication about their relative contributions—whether it is mandatory or voluntary and whether an explanation is required—on the severity of employees’ self-interested biases in their communication. As predicted, experimental results show that when explanations are not required, low-ability team members incorporate more strategic bias into their relative contribution communication when it is voluntary, compared to mandatory. We also observe that among low-ability team members, the greater strategic bias observed when the relative contribution communication is voluntary, compared to mandatory, diminishes when an explanation is required. Data Availability: Data are available from the authors upon request.
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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 | 0.012 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".