Consumer preferences for electronic cigarettes: results from a discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: E-cigarettes present a formidable challenge to regulators given their variety and the rapidly evolving nicotine market. The current study sought to examine the influence of e-cigarette product characteristics on consumer perceptions and trial intentions among Canadians. METHODS: An online discrete choice experiment was conducted with 915 Canadians aged 16 years and older in November 2013. An online commercial panel was used to sample 3 distinct subpopulations: (1) non-smoking youth and young adults (n=279); (2) smoking youth and young adults (n=264) and (3) smoking adults (n=372). Participants completed a series of stated-preference tasks, in which they viewed choice sets with e-cigarette product images that featured different combinations of attributes: flavour, nicotine content, health warnings and price. For each choice set, participants were asked to select one of the products or indicate 'none of the above' with respect to the following outcomes: interest in trying, less harm and usefulness in quitting smoking. The attributes' impact on consumer choice for each outcome was analysed using multinomial logit regression. RESULTS: Health warning was the most important attribute influencing participants' intentions to try e-cigarettes (42%) and perceived efficacy as a quit aid (39%). Both flavour (36%) and health warnings (35%) significantly predicted perceptions of product harm. CONCLUSIONS: The findings indicate that consumers make trade-offs with respect to e-cigarette product characteristics, and that these trade-offs vary across different subpopulations. Given that health warnings and flavour were weighted most important by consumers in this study, these may represent good targets for e-cigarette regulatory frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".