The brothers are watching: The peer monitoring mechanism of rivalry in reducing cheating behavior at work
Bibliographic record
Abstract
Summary The consensus of the literature has suggested that competition prompts individuals to act unethically. However, prior research that explored the ethical implication of competition has largely ignored the role of contexts within its examination—studying competition generally independent of other social relationships/actors. Drawing on the relational perspective of competition (i.e., rivalry), we propose that broader social relationships in which rivalry relationships are embedded will give rise to alternative psychological processes that can account for a curbing effect of rivalry on workplace cheating behavior. Results from one pre‐registered experiment and three surveys on working professionals suggest that exposure to rivalry leads to a heightened perception of peer monitoring, which in turn is associated with lower cheating behavior at work. We further find that the effect of rivalry on the perception of peer monitoring is stronger (weaker) when employees' leader–member exchange (LMX) relationship with their supervisor is lower (higher), highlighting the significance of other social actors in the rivalry process. This research complements the literature with a balanced perspective regarding the ethical implication of competition and contributes to the theory building of rivalry by providing an interpersonal lens to the psychology of rivalry.
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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".