When Do (and Don't) Normative Appeals Influence Sustainable Consumer Behaviors?
Bibliographic record
Abstract
The authors explore how injunctive appeals (i.e., highlighting what others think one should do), descriptive appeals (i.e., highlighting what others are doing), and benefit appeals (i.e., highlighting the benefits of the action) can encourage consumers to engage in relatively unfamiliar sustainable behaviors such as “grasscycling” and composting. Across one field study and three laboratory studies, the authors demonstrate that the effectiveness of the appeal type depends on whether the individual or collective level of the self is activated. When the collective level of self is activated, injunctive and descriptive normative appeals are most effective, whereas benefit appeals are less effective in encouraging sustainable behaviors. When the individual level of self is activated, self-benefit and descriptive appeals are particularly effective. The positive effects of descriptive appeals for the individual self are related to the informational benefits that such appeals can provide. The authors propose a goal-compatibility mechanism for these results and find that a match of congruent goals leads to the most positive consumer responses. They conclude with a discussion of implications for consumers, marketers, and public policy makers.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".