The Segmentation Strategies for Social Media-based Smoking Education in China: Based on Health Belief Model
Keywords:Scanning information acquisition, Anti-smoking behaviors, Health belief model, Social media, Segmentation strategies
[Objectives] The current study set out to examine the effects of unintended exposure to anti-smoking-related information during the routine of social media use on actual anti-smoking behaviors based on the health belief model. And it aims to determine the smoking characteristics of the social media users as well as ascertaining the choice of social media platforms of smokers. In addition, this research was conducted to identify the differences in path coefficients among different subgroups. [Methods] An online survey provided quantitative data from 921 social media users, including 466 smokers and 455 non-smokers. Chi-square tests were utilized to identify the differences in smoking characteristics and the choice of social media platforms. Then, partial least squares structural modeling (PLS-SEM) was employed using Smart PLS 3.3.5 to test hypotheses and the multi-group analysis (MGA) was conducted to quantitively describe the differences in path coefficients. [Results] Firstly, social media users who are male, or elder, or with higher monthly income are more likely to be addicted smokers and reported using regular cigarette more frequently, while the female smokers tend to use e-cigarette. Second, smokers who reported higher monthly income or higher education level or using e-cigarette rely on Sina microblog to obtain anti-smoking information. And the smokers who reported using regular cigarette prefer TikTok. Thirdly, there was empirical evidence that anti-smoking information scanning via social media platforms exerts a positive effect on smokers’ anti-smoking behaviors (by directly influencing their perceived barriers (, perceived severity ( and self-efficacy . And the anti-smoking information scanning via social media platforms is positively associated with non-smokers’ self-efficacy, thereby promoting their anti-smoking behaviors . Finally, there was no significant difference in the direct and total effect of anti-smoking information scanning on smokers’ perceptions and actual behaviors among subgroups (age, gender, income, CPD, TTF). And anti-smoking information scanning exerts a more decisive influence on perceived barriers of female non-smokers than male non-smokers (Diff.= 0.219, <.001). [Conclusion] The present results highlight the effectiveness of anti-smoking information dissemination via social media on public anti-smoking behaviors and put forward the segmentation strategies targeted to various users and tailored to three social media platforms.