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Objective Facts or Misleading Hype? Associations between Features of E-Cigarette Marketing and Sales on a Chinese E-Commerce Platform.

Yibei ChenShaojing SunXiaoquan ZhaoHan ZhouFan Wang
Published in: International journal of environmental research and public health (2020)
Background: Electronic cigarettes (e-cigarettes) have been increasingly advertised and marketed in China in recent years. This study examined the practice and impact of e-cigarette online marketing on a major retail website-Tmall.com. Methods: Data were obtained by crawling 449 online pages of e-cigarette marketing. Content analysis was conducted to summarize the marketing practices for four types of e-cigarettes, and multilevel modeling (MLM) was implemented to explore factors predictive of the online sales of the products. Results: The sales volume of e-cigarettes ranged from 0 to 28,169, with the price per item varying from RMB 218.1 ($31.84) to RMB 385.5 ($56.29). Fruit (44.3%, n = 199), mint (33%, n = 148) and cream/sugar/ice (29.4%, n = 132) were the three flavors most often listed for sale online. Moreover, 63.4% (n = 285) of e-cigarette ads emphasized the role of the products as an aid to quit smoking. Nice taste (75.1%), big vapor (65.7%), high capacity batteries (67.9%), fashionable models (61.3%), discounted price (49.7%), and suitability for gifting (45.9%) were the most frequently touted product features in online ads. Type of e-cigarettes, diversity of products, number of online comments, and location of manufacturers were significantly associated with sales volume. Conclusions: Online marketing of e-cigarettes was common on one of China's leading e-commerce websites. Sellers employed advertising strategies targeting a wide range of potential consumers-from youth to the elderly. Stricter regulations of online marketing for e-cigarettes should be enforced in China.
Keyphrases
  • smoking cessation
  • health information
  • social media
  • replacement therapy
  • healthcare
  • primary care
  • young adults
  • mental health
  • big data
  • machine learning
  • risk assessment
  • drug delivery
  • human health
  • deep learning