Bibliographic record
Abstract
This research extends social exchange theory by investigating unethical reciprocity induced by high compensation in employee–manager exchange relationships. Two experimental studies based on behavioral games showed that even after employees had reciprocated their managers’ wage offers with commensurate work efforts, managers’ previous compensation decisions still had potent effects on employees’ subsequent ethical behaviors. Specifically, Study 1 showed that high wages led employees to engage in unethical reciprocity to benefit their managers at the expense of honesty. In addition, when managers had the possibility of rewarding employees’ unethical reciprocity, only underpaid employees demonstrated more unethical reciprocity, and high-paid employees were not affected by their potential personal payout. Study 2 replicated Study 1’s results using different designs and behavioral games. Its results consistently showed that high-paid employees were more likely to act dishonestly to advance their managers’ interests, irrespective of their own payouts. Finally, Study 3 complemented our experimental results with initial field evidence, suggesting that higher salaries were positively related to the likelihood of police officers engaging in unethical and illegal actions to help their organization. We discuss our results by applying cross-disciplinary insights on exchange models and compensation to organizational studies.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".