Exploring the Influence of Online Price Anchoring and Attribute Framing on the Likelihood of Hearing Aid Purchases
Bibliographic record
Abstract
Background/Objectives: This study investigated whether exposure to various types of information online can influence adults aged 40 and above in their likelihood to purchase hearing aids (HAs). Specifically, it examined the effects of price anchoring, using high or low HA prices in advertisements, and attribute framing, using product descriptions that highlighted lifestyle appeal or technological capabilities. Methods: In a 2 × 2 experimental design, 271 participants browsed a website simulating an online search for hearing health information. Participants then rated their likelihood of purchasing three fictitious HAs as well as their likelihood of not purchasing any device. Results: Two-way ANCOVAs indicated no significant main effects of anchoring or framing on purchase likelihood for the fictitious devices (covariates included self-rated hearing ability, trust in online health information, and HA knowledge and importance). No significant interaction effects emerged. However, exploratory analyses revealed significant anchoring effects for two of the three devices among participants with below-median self-rated hearing. Additionally, self-rated knowledge was a significant covariate in the model for all devices (p < 0.001), positively correlating with purchase likelihood. Participants with above-median self-rated knowledge showed significantly higher purchase likelihood for all devices (p < 0.001, d ≥ 0.572 for all comparisons), while those with below-median knowledge displayed a significant anchoring effect for two outcomes. Conclusions: Enhanced HA knowledge may increase HA purchase likelihood and reduce the potential anchoring effects of online advertising. Further research is needed to determine the impact of exposure to high or low prices on HA purchase decisions, especially among those with poorer hearing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".