Bibliographic record
Abstract
Purpose This paper aims to assess the interactive impact of dispositional threat orientation and affirmation (both self-affirmation and self-efficacy) on the effectiveness of fear appeals. Design/methodology/approach A 3 × 2 × 2 × 2 fully crossed, mixed experimental design is used. The study is conducted through an on-line survey platform. Participants are nationally representative in terms of age, gender and geographic location within the USA. Findings Threat orientation impacts individuals’ responses to fear appeals. Control-oriented individuals respond in a more adaptive manner, heightened-sensitivity-oriented individuals are a “mixed-bag” and denial-oriented individuals respond in a more maladaptive manner. Affirmations (both self-affirmation and self-efficacy) interact with threat orientation in some cases to predict response to threat. Research limitations/implications This research used a cross-sectional approach in an on-line environment. A longitudinal study with a stronger self-affirmation intervention and self-efficacy manipulation would offer a stronger test. Practical implications Social marketers should consider whether their primary target market has a general tendency toward a particular threat orientation when considering the use of fear appeals. Social marketers should consider the potential benefits of a self-affirmation intervention. Social implications Individuals’ personality dispositions impact how they respond to fear appeals, which may explain why some seemingly well executed fear appeals are unsuccessful whereas others succeed. Originality/value Little or no research has examined the use of self-affirmation to overcome the challenges posed by dispositional threat orientation. This research gives an early glimpse into how these issues interplay.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".