Vaping versus JUULing: how the extraordinary growth and marketing of JUUL transformed the US retail e-cigarette market
Bibliographic record
Abstract
BACKGROUND: While national surveys showed declines in e-cigarette use in the USA between 2015 and 2016, recent reports indicate that JUUL, a sleekly designed e-cigarette that looks like a USB drive, is increasingly being used by youth and young adults. However, the extent of JUUL's growth and its marketing strategy have not been systematically examined. METHODS: A variety of data sources were used to examine JUUL retail sales in the USA and its marketing and promotion. Retail store scanner data were used to capture the retail sales of JUUL and other major e-cigarette brands for the period 2011-2017. A list of JUUL-related keywords was used to identify JUUL-related tweets on Twitter; to identify JUUL-related posts, hashtags and accounts on Instagram and to identify JUUL-related videos on YouTube. RESULTS: In the short 3-year period 2015-2017, JUUL has transformed from a little-known brand with minimum sales into the largest retail e-cigarette brand in the USA, lifting sales of the entire e-cigarette category. Its US$150 million retail sales in the last quarter of 2017 accounted for about 40% of e-cigarette retail market share. While marketing expenditures for JUUL were moderate, the sales growth of JUUL was accompanied by a variety of innovative, engaging and wide-reaching campaigns on Twitter, Instagram and YouTube, conducted by JUUL and its affiliated marketers. CONCLUSIONS: The discrepancies between e-cigarette sales data and the prevalence of e-cigarette use from surveys highlight the challenges in tracking and understanding the use of new and emerging tobacco products. In a rapidly changing media environment, where successful and influential marketing campaigns can be conducted on social media at little cost, marketing expenditures alone may not fully capture the influence, reach and engagement of tobacco marketing.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".