Observation Moderates the Moral Licensing Effect: A Meta-Analytic Test of Interpersonal and Intrapsychic Mechanisms
Bibliographic record
Abstract
When someone who initially behaved morally subsequently acts less morally, this is known as moral licensing. We apply reputation-based theories to predict when and why moral licensing occurs. Specifically, our pre-registered predictions were: (1) participants observed during the licensing manipulation would have larger licensing effects, and (2) unambiguous dependent variables would have smaller licensing effects. In a multi-level meta-analysis of 115 experiments (N = 21,770), there was a larger licensing effect when participants were observed (Hedge’s g = 0.65) compared to unobserved (Hedge’s g = 0.13). After accounting for publication bias using robust Bayesian meta-analysis, support remained when participants were observed (Hedge’s g = 0.51; BF10 = 9.14) but not when unobserved (Hedge’s g = -0.01; BF10 = 0.11). Ambiguity did not moderate the moral licensing effect. These results suggest that moral licensing is predominantly an interpersonal effect based on reputation, rather than an intrapsychic effect based on self-image.
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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 | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| 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 | 0.003 |
| 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".