The Market Trajectory of a Radically New Product: E-Cigarettes
Bibliographic record
Abstract
The study analyzes the diffusion of electronic cigarettes as an innovation, as well as how industry, society, and the individual affect its market dynamics. The study is based on five surveys conducted during the years 2017-2019, and including participants of all ages (age 12 to 80 and beyond). The article describes indicators for evaluating the sustainability of a really-new product like electronic cigarettes, following the market trajectory of this product as it sets its dominant design and shapes the use-system for the product type from now onward. This process has two phases: trial and adoption. The probability of each nicotine product type’s adoption is different, depending on the prevalence of trials of that product among the population. The results of e-cigarette trials and additional indicators reveal the point (critical mass-point) where social influence outweighs rational evaluation by the individual regarding nicotine products. By using triers’ prevalence as the indicator for measuring an entry of really-new product into the market, the authors could identify the sustainability of that really-new product at a much early phase. Therefore, the prevalence of triers can be used as a predictor for the diffusion rate of an innovative product in a certain population and should be measured. The authors also propose a regression model that estimates the prevalence of triers based on the extent of users in the population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".