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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".