Achieving Persuasion or Avoiding Resistance? Using Motivational Matching Theory to Disentangle the Benefits of Matched Messages from the Costs of Mismatched Messages
Bibliographic record
Abstract
Persuasive messages are more effective when they are tailored to engage differences in people’s motivations—such as a person’s values or identity. This phenomenon is known as “motivational matching”. Yet, studies commonly confound the beneficial effects of matching with the detrimental effects of mismatching. We present a framework to disentangle these effects, and report two experiments (using samples of American adults) that demonstrate how mismatching actively reduces persuasion. In Study 1 (N=689), messages promoting volunteerism were substantially less successful to the extent that they contained elements that conflicted with people’s motivations to volunteer. In Study 2 (N=1,101), the detrimental effects of providing mismatched messages (e.g., presenting liberal individuals with appeals highlighting conservative values) were greater in magnitude than the benefits of providing matched messages (e.g., presenting liberal individuals with appeals highlighting liberal values). We discuss implications for theory and practice, including situations when generic/neutral messaging may outperform matching-based persuasion.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".