Turning Up the Volume: An Experimental Investigation of the Role of Mutual Monitoring in Tournaments
Bibliographic record
Abstract
This study investigates experimentally how mutual monitoring affects effort when employees are compensated via rank‐order tournaments. Theory and anecdotal evidence suggest that mutual monitoring may either decrease effort by facilitating collusion or increase effort by stimulating competition. In our first experiment, we find that mutual monitoring increases effort, because participants do not attempt to collude but rather behave competitively. This result leads us to expand our theory and develop hypotheses to predict that the effect of mutual monitoring depends on whether employees have the inclination to collude or compete. Specifically, we predict that mutual monitoring decreases effort when employees are inclined to collude and increases effort when employees are inclined to compete; that is, mutual monitoring will not change the basic inclination created by the workplace setting, but will “turn up the volume” on the effect that such inclination has on effort. Consistent with our predictions, our second experiment finds that mutual monitoring leads to lower effort when participants have a collusive inclination and (eventually) higher effort when they have a competitive inclination. Overall, the results from these two experiments suggest that allowing employees to observe each other's productive effort in tournament incentive settings may have positive or negative consequences for the firm, depending on whether environmental factors predispose employees to collude or compete.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".