The effects of risk disclosure and ad involvement on consumers in DTC advertising
Bibliographic record
Abstract
Purpose The importance of consumer involvement is well recognized in marketing theory, but has been absent from past inquiries in consumers' processing of DTC advertisements. The authors believe it is necessary to account for varying levels of involvement between consumers in order to better appreciate their responses to DTC advertisement claims. The present study aims to shed additional insight into the relationship between consumer involvement and the processing of risk information in DTC advertising. Design/methodology/approach The study was a between‐subject factorial design and consisted of 156 students from a North American university. It used an instructional manipulation designed to compare how low and highly involved consumers perceive DTC drug advertisements and more specifically the benefit and risk information contained in such advertisements. Findings Findings indicate that consumers' perception and processing of DTC advertisements resembles consumers' reaction to fear appeals. Furthermore, consistent with previous studies, consumers react negatively to DTC advertisements containing a high content of risk information. Findings indicate that greater differences in consumer processing of risk information is observed when the sample is categorized as high versus low involved, rather than sufferer versus non‐sufferer, and that this consumer characteristic is important enough to include when examining consumer reactions to DTC advertisements. Originality/value The relationship between amount of risk information and consumer responses has not been empirically examined while controlling for the potential role of involvement. This study is a first step in addressing this gap.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".