Peer-to-Peer Recognition Leaderboards and Employee Proactive Helping Behavior
Bibliographic record
Abstract
ABSTRACT Firms commonly employ leaderboards within their peer-to-peer recognition programs. We experimentally investigate how ranking basis—variation in the measure firms use to determine leaderboard rankings—affects employees’ proactive helping behavior. We find that leaderboards ranking employees based on the number of times peer-to-peer recognition is received decrease proactive helping compared with when no leaderboard is provided. Conversely, leaderboards ranking employees based on the number of times peer-to-peer recognition is given increase proactive helping compared with when no leaderboard is provided. These findings underscore the influence of ranking basis on shaping motives linked to proactive helping behavior. Furthermore, these findings highlight for firms the importance of judiciously selecting a ranking basis when utilizing peer-to-peer recognition leaderboards. JEL Classifications: C92; D91; M41; M54.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".