The impact of altruistic attribution and brand equity in food label campaigns
Bibliographic record
Abstract
Purpose The purpose of this study is to analyse the impact of two determinants of purchase intention in food label campaigns: altruistic attribution and brand equity. Design/methodology/approach A 2 × 2 between-group factorial experimental design was used, with 2 levels of altruistic attribution (high/low) and 2 levels of brand equity (high/low). The product used for the study was pork chops. A survey was conducted on 602 respondents representing the population of Quebec, Canada. Findings Structural equation modelling was used to evaluate the fit of the data with the proposed mod el. The results demonstrate that altruistic attribution and brand equity have an indirect impact on purchase intention via perceptions of taste and food safety. Altruistic attribution, but not brand equity, also has a direct impact on purchase intention. Research limitations/implications The experiment in this study was conducted via an online consumer panel to increase internal validity. As a result, one of the limitations of the study concerns its external validity. Practical implications This research provides strategic guidelines for businesses or organisations that wish to develop food label campaigns. They must simultaneously consider both altruistic attribution and pre-existing brand equity. Originality/value This study contributes to the literature by demonstrating the impact of altruistic attribution and brand equity on purchase intention in the context of food label campaigns. The study mobilises attribution theory and the multidimensional consumer-based brand equity scale.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".