How to Promote Eco-Apparel? Effects of Eco-Labels and Message Framing
Bibliographic record
Abstract
The study investigates the potential effects of eco-labels and advertisement message framings for promoting consumer attitude on eco-apparel consumption. Furthermore, this paper examines how consumers’ attitudes towards the brand and advertisement affect consumers’ evaluation of brand equity in sustainable brands. Using non-probability sampling with college students and Amazon Mturkers, the authors developed the proposed hypotheses with 2 (Eco-label: Absence vs. Presence) x 2 (Framed Messages: Positive vs. Negative) between-subject design on consumers’ attitudes toward the brand, advertisement, and evaluation of brand equity. To test hypotheses, multivariate analysis of variance (MANOVA) and a series of simple regressions were performed. Results revealed that the eco-label did not significantly increase consumers’ attitude toward the eco-apparel brand, leading to no interaction effect between eco-label and message framing on consumer attitude. However, message framing was effectively applied as positive messages were significantly associated with consumers’ attitudes toward the brand, the advertisement, and consumers’ evaluation of brand equity in the context of eco-apparel brands. This study simultaneously examines the eco-label and message framings on consumers’ attitudes toward the advertisements, consumers’ attitudes toward the brand, and their evaluations of brand equity in the eco-apparel context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".