Observation Moderates the Moral Licensing Effect: A Meta-Analytic Test of Interpersonal and Intrapsychic Mechanisms
Bibliographic record
Abstract
Moral licensing occurs when someone who initially behaves morally subsequently acts less morally. We apply reputation-based theories to predict when and why it occurs. As pre-registered, we predicted: (1) being observed would be associated with larger licensing effects and (2) unambiguous outcomes would have smaller licensing effects. In a multi-level meta-analysis of 115 experiments ( N = 21,770), moral licensing was stronger when participants were observed ( g = 0.65) than unobserved ( g = 0.13). After accounting for publication bias with robust Bayesian meta-analysis, there was moderate evidence for licensing when participants were observed ( g = 0.51; BF 10 = 9.14), but moderate evidence against licensing when unobserved (Hedge’s g = −0.01; BF 10 = 0.11). Ambiguity did not moderate moral licensing. These findings suggest that moral licensing is elicited through interpersonal motives, clarify when licensing (vs. consistency) occurs, and explain why many online studies failed to replicate. Evidence for intrapsychic motives is inconclusive.
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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 | 0.043 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".