When marketing discourages consumption: demarketing of single-use plastics for city tourism in Ottawa, Canada
Bibliographic record
Abstract
Single-use plastics (SUP), have been widely criticized for contributing to pollution and the throwaway culture. This paper applies the demarketing framework to SUP consumption in city tourism to spur tourists’ anticipated reduction benefits of SUP. More specifically, this study quantifies the impact of the well-established demarketing mix components, adding people’s motivations to avoid SUP. Data were collected through 326 self-administered questionnaires from city tourists in Ottawa, Canada, visiting areas surrounding the Canadian Parliament. A structural equation model was fitted for statistical data analysis covering the demarketing mix with three moderating variables – individual commitment, assigned responsibilities, and recycling attitude. The results suggest that modulating, respectively, promotion by showing the negative consequences of SUP; place, by reducing on-site availability of SUP; people’s motivation, to reduce SUP usage; price, by imposing a price premium on SUP; and product, by substituting SUP for alternatives, will most strongly increase city tourists’ anticipated reduction benefits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".