Appealing to motivation to change attitudes, intentions, and behavior: A systematic review and meta-analysis of 702 experimental tests of the effects of motivational message matching on persuasion.
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Bibliographic record
Abstract
Message matching refers to the design and distribution of persuasive messages such that message features (e.g., the themes emphasized) align with characteristics of the target audience (e.g., their personalities).Motivational message matching is a form of this technique that seeks to enhance persuasion by matching specifically to differences in motivational characteristics (e.g., salient goals, needs, values).Despite widespread use of motivational matching, there is little understanding of how and when to use it.We conducted a preregistered (PROSPERO CRD42019116688; osf.io/rpjdg) systematic review and three-level meta-analysis of 702 experimental studies on motivational matching (synthesizing 5,251 effect sizes from N = 206,482).Studies were inclusive of publications until December 2018, and primarily identified using APA PsycInfo, MEDLINE, and Scopus.We evaluate moderation using meta-regressions, and provide bias assessments (sensitivity analyses, funnel plots).Motivational matching increases persuasion by an average of r = .20(95% CI: .18,.22)as assessed by differences in attitudes, intentions, self-reported behavior, and observed behavior, relative to comparison conditions.This effect is larger than previously observed for other message matching approaches (e.g., message tailoring, message framing) which usually average r < .10.Although motivational matching can effectively improve persuasion, its effects are also marked by meaningful heterogeneity.Notably, motivational matching effects are largest when matching to contextual factors (than to individual differences), when compared to messages that conflict with people's motivations, and when target characteristics are manipulated rather than assessed.Through this review, we develop and evaluate theoretical propositions that inform the optimization of motivational matching.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it