How meaningless and substantive green claims jointly determine product environmental perceptions
Bibliographic record
Abstract
This research examines how consumers perceive products in the presence (and absence) of substantive attribute information and ‘meaningless’ claims. Meaningless claims are product information devoid of any factual, substantive, objective, or concrete detail, which consumers may nonetheless ‘believe’ is a useful claim and on which they base their perceptions. Across three experiments we predict and find that meaningless claims of being ‘friendly to’ or ‘caring about’ the environment are sufficient to increase consumer pro-environmental perceptions. Most importantly, we find that this effect is not additive when meaningless claims co-occur with more substantive information, and that it holds while controlling for consumer environmental identity and skepticism. This has theoretical implications for understanding how consumers assess product information, demonstrating that the impact of peripheral cues such as meaningless claims is not over and above that of objective claims, when these pieces of information are presented together. It also has practical implications for policymakers in terms of consumer advocacy, justifying the need for regulation of such meaningless claims.
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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 | 0.006 | 0.043 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".