Health Risk Perceptions and Consumer Psychology
Bibliographic record
Abstract
This chapter outlines recent developments in the consumer psychology literature examining people’s health-related risk perceptions. We first define risk, and discuss the importance of studying risk perceptions in the health domain. We integrate extant models proposed in social and health psychology and build a theoretical model for examining risk perceptions. We then describe the model in terms of the antecedents of health risk perceptions (e.g., motivational, cognitive, affective, contextual, and individual differences), their consequences (e.g., awareness and interest in the health hazard, trial and adoption of precautions or medical treatments, and subsequent behavior in terms of continued adoption or repetition, and word-of-mouth/recommendations of precautionary steps or treatments), and the factors that moderate the link between these two (e.g., financial, performance, psycho-social, and physiological risk). A primary contribution of our approach is to suggest that eliciting risk perceptions serves a persuasive role besides a measurement role, leading to the provocative question as to whether marketers should knowingly leverage their knowledge of how consumers assess risk to encourage behaviors leading to a healthier lifestyle. Implications for public policy makers, consumer welfare advocates, and commercial marketing companies are also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".