# Exploring the intention to use e-cigarettes and its influencing factors among Thai non-formal education students: A cross-sectional study

## TL;DR

This study explores the low intention to use e-cigarettes among Thai non-formal education students and identifies attitudes and social influences as key factors.

## Contribution

The study provides new insights into e-cigarette use intentions among a vulnerable, under-researched group using the Theory of Planned Behavior.

## Key findings

- Only 2.6% of participants had an intention to use e-cigarettes, with 90.52% showing no intention.
- Attitudes and subjective norms were significant predictors of e-cigarette use intention.
- The model explained 15.0% of the variance in intention to use e-cigarettes.

## Abstract

Non-formal education students are a vulnerable group due to social influences and varying health literacy. However, evidence on their intention to use e-cigarettes and influencing factors is limited. This study aimed to investigate these intentions and associated factors among students in Bangkok, Thailand.

A cross-sectional survey was conducted among non-formal education students in Bangkok, Central Thailand, aged 13–24 years, who had never used e-cigarettes. A total of 116 participants were included in the study. Data were collected between 1 and 15 August 2025 using a self-administered structured questionnaire designed to assess knowledge, attitudes, subjective norms, perceived behavioral control, and intention to use e-cigarettes, based on the Theory of Planned Behavior. Descriptive statistics and multiple linear regression were applied to identify factors associated with the intention to use e-cigarettes.

Only 2.6% of participants reported an intention to use e-cigarettes, 6.90% indicated that they might use them in the future, while 90.52% reported no intention to use e-cigarettes. Most participants demonstrated high knowledge of e-cigarettes (62.07%) and negative attitudes toward use (63.79%). Subjective norms were rated high (59.48%), and perceived behavioral control was very high (48.28%). Multiple linear regression analysis showed that attitudes (β=0.223; 95% CI: 0.03–0.41, p=0.025) and subjective norms (β=0.211; 95% CI: 0.02–0.39, p=0.032) had significant positive effects on the intention to use e-cigarettes. Overall, the model explained 15.0% of the variance (R2=0.150, adjusted R2=0.119; F=4.90, p=0.001).

The findings highlight the importance of fostering negative attitudes toward e-cigarette use and reducing the influence of subjective norms among students. However, as this study employed a cross-sectional design, further longitudinal and interventional studies are needed to confirm these relationships and provide stronger evidence for the prevention of e-cigarette use among non-formal education students.