Threat specificity in fear appeals: examination of fear response and motivated behavior
Bibliographic record
Abstract
Purpose A fear appeal is a communication tactic designed to scare people into adopting desired behaviors (e.g. wash hands to avoid contracting COVID-19). While it is generally acknowledged that fear appeals can be persuasive at motivating behavior, this paper aims to identify how to optimally identify how to optimally frame the focal threat to increase their effectiveness as well as to uncover additional underlying processes. Design/methodology/approach The authors conducted four experimental studies. Findings This research validates that exposure to fear appeals can strongly motivate behavior. However, this study shows that this effect is moderated by threat specificity. Specifically, this study demonstrates that people are more motivated to engage in behaviors that facilitate threat avoidance after exposure to a personally relevant threat that represents a nonspecific (e.g. aging appearance) rather than a specific outcome (e.g. wrinkles). This effect is mediated by perceptions of assimilation (versus contrast) to the focal threat. This study reliably shows the effect across three threat domains (i.e. aging appearance, weight gain, illness) and for different behaviors. Originality/value Theoretically speaking, this study contributes to the fear appeal literature by identifying a new type of message framing that has the potential to increase fear appeal’s persuasive power, and uncovering a distinct mechanism by which fear appeals impact behavior. Practically speaking, the findings confirm that fear appeals have the potential to help marketers mobilize consumer behavior, especially when the communication highlights a nonspecific rather than specific threat.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".