Do hedonic motives moderate regulatory focus motives? Evidence from the framing of persuasive messages.
Bibliographic record
Abstract
Research on regulatory focus has established a regulatory matching effect: The persuasiveness of a message is enhanced when regulatory orientations of message and perceiver match (i.e., both are promotion or both are prevention). We report evidence that varying the hedonic outcome reverses this effect. We manipulated hedonic outcome by explicitly stating pleasurable versus painful outcomes as part of the message frame as well as by priming perceivers to focus on either pleasurable or painful outcomes. When both message and perceiver were focused on pleasurable outcomes, we replicated the regulatory matching effect. However, the matching effect reversed when the hedonic outcome of the message was opposed to that of the perceiver (i.e., one was pleasurable and the other painful). Under these conditions, messages that mismatched the perceivers' regulatory orientation were more persuasive (i.e., promotion message for a prevention oriented perceiver or vice versa). We also examined the persuasion effects when both message and perceiver were focused on painful outcomes and found that the regulatory matching effect re-emerged. The reversal of the regulatory matching effect by hedonic outcome strongly suggests that hedonic motives (approach of pleasure vs. avoidance of pain) and regulatory focus motives are distinct constructs. This is important because contrary to theoretical statements these constructs have often been confounded.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".