“They Did Not Walk the Green Talk!:” How Information Specificity Influences Consumer Evaluations of Disconfirmed Environmental Claims
Bibliographic record
Abstract
While environmental claims are increasingly used by companies to appeal consumers, they also attract greater scrutiny from independent parties interested in consumer protection. Consumers are now able to compare corporate environmental claims against external, often disconfirming, information to form their brand attitudes and purchase intentions. What remains unclear is how the level of information specificity of both the environmental claims and external disconfirming information interact to influence consumer reactions. Two experiments address this gap in the CSR communication literature. When specific (vs. vague) claims are countered by specific (vs. vague) external information, consumers report more negative brand attitudes and lower purchase intentions (Experiment 1). The effect is serially mediated by (1) skepticism toward the claims and (2) lack of corporate credibility (Experiment 2). We conclude by discussing strategies that firms can utilize to avoid information dilution and ensure that external disconfirming information percolates to consumers as specific.
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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 | 0.028 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".