Motivational compatibility and the role of anticipated feelings in positively valenced persuasive message framing
Bibliographic record
Abstract
Abstract Previous research on message framing has focused on the effect of overall valence on persuasion, since most studies compare positively versus negatively valenced frames that are anchored by the same end‐state. Unlike previous studies, this paper investigates the role of end‐states, or outcome focus, in message framing by using two positively valenced, factually equivalent message frames that are anchored by opposing end‐states: the presence of gain (P/G) frame versus the absence of loss (A/L) frame. It is proposed that anticipated feelings and persuasion are greater when the end‐state of the message frame is motivationally compatible with a consumer's regulatory focus, either chronic or situational. The major hypothesis is that the P/G frame leads to the anticipation of more intense positive feelings and subsequently produces greater persuasion when promotion focus versus prevention focus is salient, whereas the opposite holds for the A/L frame. Furthermore, it is proposed that the effect of motivational compatibility on persuasion is mediated by the anticipation of positive feelings. These hypotheses are generally supported in two experiments. © 2008 Wiley Periodicals, Inc.
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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 | 0.005 | 0.029 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".