Availability bias and heterogeneity in saliency, recency, and frequency of promotions for plant-based foods: a naturalistic observation
Bibliographic record
Abstract
Availability bias influences decisions by how readily certain events, objects, or people can be brought to mind. This “out of sight, out of mind” effect depends on whether these elements are present during decision-making. To promote sustainable food consumption, understanding this bias is crucial, as marketing promotions exhibit heterogeneity in terms of the salience, recency, and frequency with which they are administered. Our research examines the impact of different promotions that vary across these three dimensions on the demand for plant-based food products and their interaction with price sensitivity. We analyzed weekly purchases of 21 plant-based beverage brands across 242 stores in Quebec, Canada, from 2015 to 2016 using two-level mixed-effect regression models across four studies. Results from Study 1 indicate that flyer promotions that had high salience, recency, and frequency were most effective (B = 0.417, p < 0.001), compared to mobile promotions with low salience and variable recency and frequency (B = 0.233, p < 0.001) or in-store promotions of high salience but low recency and frequency (B = 0.073, p < 0.001). Of the mobile promotions evaluated in Study 2, advertisements promoting bonus loyalty points were the most effective in driving demand (B = 0.776, p < 0.001), followed by general advertisements (B = 0.125, p < 0.001). Demand was elastic across all models, and most promotions increased price sensitivities in Studies 3 and 4 regardless of their salience, recency, or frequency. The findings highlight the synergistic effect of promotional elements delivered both before and at the decision-making moment in overcoming availability bias to boost demand for sustainable products. However, frequent promotions may increase price sensitivities due to anchoring to promotional prices. This article has implications for theory and practice.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".