Consumer Psychology on Food Choice Editing in Favor of Sustainability
Bibliographic record
Abstract
This article examines rationale behind consumers’ vote for or against choice editing (reducing food choice) in favor of sustainable consumption to inform marketing communication strategies and sustainability policies. Based on a Qualitative analysis of free-text comments in a UK nationwide survey on sustainable healthy food consumption using inductive thematic analysis, we found that the majority (55.4%) disagreed with governments being given the right to minimize food choice options available to consumers by requesting that food industry players supply only sustainable food products whereas only 44.6% agreed with the idea. In-depth thematic analysis revealed that those who disagreed with it expressed the reasons to be “Freedom of choice”, “Individual choice to decide and responsibility”; “Producers to be encouraged to develop sustainable products”; “Need for education”; “Consumers have power”; “Consumers should be made to fund health conditions they develop from unhealthy food.”; “Government should fund production of sustainable foods”; and “this will lead to less competition within the market”. On the other hand, the agreement expressed by respondents gave reasons such as, “Food industry’s notorious for selling unhealthy food”; “Need to keep the price of sustainable products down.”; “Government should legislate.”; “All food sold should be whole natural food.”; “Retailers should produce more healthy food as obesity is a problem.”; “Healthy food is good for us.”; “Government’s obligation.”; and “GMO foods, foods grown using artificial methods, harm the environment and humans.” Our analysis revealed that change interventions have slowly reduced the pace of growth in the food industry, partially because of consumer awareness at a gradual rate. Moreover, sustainable food products are viewed as ineffective in the short run while market share for sustainable items remains substantially low. The implications of the results include inclusive policies for sustainable consumption, government intervention by making it mandatory to consume and produce sustainable items, accountability measures for food producers, the introduction of a rebate system for sustainable production, and the monitoring of food prices ensuring organic food is affordable to all.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".