Does rejecting inequality enhance green consumption? The effect of power distance belief on organic food consumption
Bibliographic record
Abstract
With the rapid increase in the consumption of organic food, there has also been a growing interest in developing a nuanced understanding of the many different drivers of this consumption trend. Although many studies examine people's motives for consuming organic food, the role of culture has received limited attention. The present research examines the hitherto unexplored role of power distance belief (PDB)-the extent to which people accept and endorse social hierarchy- on consumers' organic food preferences and purchases. Across five studies, comprising both real and hypothetical purchases, we find that, due to their greater environmental concerns, low PDB consumers have a greater preference for organic foods than do high PDB consumers. We also demonstrate two strategies that motivate high PDB consumers to purchase more organic foods. Specifically, we show that high PDB consumers purchase organic foods more when environmental issues reduce society's power distance in the future. Moreover, high PDB consumers reported greater organic food consumption when primed with low (vs. high) level construal. We conclude the paper with the implications of these findings for policymakers and businesses in terms of, for example, their segmentation, targeting, and promotion strategies.
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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 | 0.001 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".