Drivers of Purchase Decisions Among Consumers of Dried Flower Cannabis Products: A Discrete Choice Experiment
Bibliographic record
Abstract
OBJECTIVE: Cannabis was legalized for nonmedical use in Canada in 2018. However, with a long-established illegal market, it is important to understand cannabis consumers' preferences in order to create a market that encourages purchasing cannabis through legalized channels. METHOD: A survey including a discrete choice experiment was conducted to estimate preference weights for seven attributes of dried flower cannabis purchases (price, packaging, moisture level, potency, product recommendations, package information, and regulation by Health Canada). Participants were at least 19 years of age, lived in Canada, and purchased cannabis in the last 12 months. A multinomial logit (MNL) model was used for the base model, and latent class analyses to identify subgroups preference profiles. RESULTS: A total of 891 participants completed the survey. The MNL model showed that all attributes significantly influenced choice, except product recommendations. Potency and package information were most important. A three-group latent class model showed that about 30% of the sample were most concerned with potency, whereas two groups--jointly making up the remaining 70%--were most concerned with package type (about 40% preferred bulk packaging, and about 30% preferred pre-rolled joints). CONCLUSIONS: Consumer purchase preferences for dried flower cannabis were influenced by different attributes. Preference patterns can be grouped into three categories. About 30% of the population appeared to have their preferences met by the legalized market, whereas another 30% appeared to be more loyal to the unlicensed market. The remaining 40% represented a group that may be influenced through regulatory changes to simplify packaging and increase availability of product information.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".