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Abstract

E-cigarette use is increasingly prevalent among college students, largely due to exposure to e-cigarette-related content on social media platforms. Previous research has shown a positive association between college students’ perceived benefits of e-cigarette and their intentions to use them. This study examines whether the perceived benefits of e-cigarettes mediate the relationship between social media engagement and e-cigarette use intentions among Taiwanese college students who are current non-users. A cross-sectional design and convenience sampling was adopted. A total of 1,519 Taiwanese college students who had not used e-cigarettes in the past 30 days completed an online survey. Data were collected on the perceived benefits, social media engagement (specifically browsing, searching, and sharing e-cigarette-related content), e-cigarette use intentions, and sociodemographic variables. Descriptive statistics, independent-sample t-tests, chi-squared tests, and mediation analyses were conducted to analyze the data. Approximately 13% of participants reported an intention to use e-cigarettes. Compared with non-intenders, those with intentions to use e-cigarettes were more likely to actively search for and share related content and reported higher perceived benefits. Mediation analysis indicated that perceived benefits significantly mediated the association between sharing behavior and use intentions, but not between browsing or searching behaviors. Only searching behavior was directly associated with use intentions. Active forms of social media engagement, especially sharing, may increase e-cigarette use intentions by enhancing the perceived benefits of use. Health education efforts should include strategies to counter the influence of peer-endorsed and user-generated content on social media platforms.

Plain Language Summary

How Beliefs About E-Cigarette Benefits Influence College Students Social Media Use and Intentions

Many college students are exposed to e-cigarette content on social media, which can influence their beliefs about the benefits of e-cigarettes and their intentions to use them. This study focused on Taiwanese college students who do not currently use e-cigarettes, exploring how their perceptions of e-cigarette benefits affect the link between social media engagement and their intentions to try e-cigarettes. We surveyed 1,519 college students about their use of social media, their perceptions of e-cigarette benefits, and whether they intend to use e-cigarettes in the future. About 13% of students said they intended to try e-cigarettes. These students were more likely to believe in the perceived benefits of e-cigarette use, such as being safer than smoking traditional cigarettes or offering social advantages. They were also more likely to engage with e-cigarette content on social media, such as browsing, searching, or sharing posts. The study revealed that students beliefs about e-cigarette benefits play a key role in explaining why social media use is linked to their intentions to try e-cigarettes. For example, students who shared e-cigarette-related posts on social media were more likely to believe in the benefits of e-cigarettes, which then influenced their desire to use them. These findings highlight the need for health interventions to address the perceived benefits of e-cigarettes among young adults. Campaigns should focus on correcting misconceptions about e-cigarettes and providing clear information about the risks. Teaching students to critically evaluate social media content could also reduce the influence of misleading e-cigarette promotions. By addressing the role of perceived benefits, health campaigns can more effectively prevent young adults from starting to use e-cigarettes.

Keywords

social media e-cigarette perceived benefits mediation effect college students

Background

The increasing prevalence of e-cigarette use among young adults has emerged as a significant public health concern. Although initially marked as smoking cessation tools, e-cigarettes have become the most commonly used tobacco product within this demographic. In the United States, the proportion of adults who currently use e-cigarettes rose from 4.5% in 2019 to 6.5% in 2023. Notably, young adults aged 21 to 24 reported the highest usage rate at 15.5%, while approximately 1 in 10 adults aged 18 to 20 also reported current use (Vahratian et al., 2025). Similarly, in Taiwan, e-cigarette use among college students more than doubled, from 2.5% in 2018 to 5.4% in 2020 (Taiwan National Health Administration, 2021). Despite widespread perceptions of reduced harm, an increasing body of literature has documented serious health risks, including nicotine addiction, neurodevelopmental impairment among individuals under age 25, increased anxiety and mood disorders, respiratory inflammation, and the potential transition to traditional cigarette smoking (Centers for Disease Control and Prevention [CDC], 2023); Meehan et al., 2024). While some public health experts promote e-cigarettes as harm-reduction tools for adult smokers, this perspective remains controversial, particularly when applied to younger populations. Critics argue that such narratives may minimize risks and reinforce misperceptions, especially when spread through unregulated social media channels (Green et al., 2016; McNeill et al., 2018).

Social media has become a primary channel through which young adults encounter and absorb information about e-cigarettes. As a space saturated with both commercial advertising and user-generated content, social media facilitates the dissemination of pro-vaping messages that often frame e-cigarettes as trendy, safer alternatives to traditional cigarettes, or as tools for stress relief and social connection (Chen-Sankey et al., 2024; Jung et al., 2024; Sidani et al., 2022). Research indicates that college students are frequently exposed to tobacco-related content online, with approximately 30% reporting having seen tobacco product advertisements, and 23% engaging with such content, primarily on platforms such as Facebook (Clendennen et al., 2020).

These promotional messages, often delivered in informal, peer-endorsed formats, are typically more positively framed and emotionally resonant than traditional advertising, reinforcing favorable perceptions of vaping (Kwon & Park, 2020). According to social cognitive theory (Bandura, 1999), behaviors and beliefs can be shaped through observation of others, particularly within socially interactive environments such as social media. Emerging evidence further distinguishes between passive and active engagement with vaping-related content: while passive exposure (e.g., browsing) may normalize e-cigarette use, active engagement (e.g., searching, sharing) is more strongly associated with use frequency and is mediated by changes in perceived risk (Li et al., 2024). Consequently, social media use is linked not only to more favorable attitudes toward e-cigarettes, but also to greater susceptibility and stronger intentions to experiment with them (Cavazos-Rehg et al., 2021; Vogel et al., 2021). These findings underscore the critical role of social media in shaping both risk and benefit perceptions and emphasize the importance of further examining its influence on behavioral intentions, particularly among non-users, whose decision-making processes may be more susceptible to external cues.

Perceptions play a central role in individuals’ decisions to adopt or avoid health-related behaviors, particularly when evaluating novel or emerging products such as e-cigarettes. This concept is foundational to several major health behavior theories, including social cognitive theory (Bandura, 1999), the health belief model (Becker, 1974), and the theory of planned behavior (Ajzen, 1991), all of which emphasize the influence of perceived benefits and risks on behavioral intentions. In health psychology, behavioral intentions are considered to result from individuals’ cognitive evaluations of anticipated outcomes. Perceived benefits, such as stress relief, social integration, or reduced harm, serve as core cognitive constructs that shape these evaluations (Ajzen, 1991; Bandura, 1999). Exposure to positively framed messaging on social media can influence benefit perceptions through psychological mechanisms such as observational learning, social reinforcement, and affective framing (Bandura, 1999; Zheng et al., 2024). These mechanisms are especially salient for youth and non-users, whose beliefs may be more susceptible to external influence. Although existing studies have demonstrated that perceived benefits of e-cigarettes are positively associated with use among young people (Pokhrel et al., 2018; Romijnders et al., 2018; Schaupp et al., 2020), much of the research has focused primarily on perceived harm. For instance, Jiang et al. (2022) found that perceived harm mediated the relationship between exposure to e-cigarette advertising and use intentions. However, the mediating role of perceived benefits, particularly in the context of social media engagement, remains underexplored. This represents a critical gap, particularly given the overwhelmingly positive framing of e-cigarettes on social platforms.

Investigating perceived benefits as a mediator is especially important among non-users, who represent a key population for prevention efforts. Behavioral intentions are established precursors to future use (Ajzen, 1991), and non-users’ beliefs may be more susceptible to influence from social messaging. Prior research suggests that non-users differ from users not only in usage status but also in how they assess the risks and benefits of e-cigarettes (Kelsh et al., 2023; L.-L. Liao et al., 2024). This raises concerns that positive framed portrayals on social media may indirectly shape behavioral intentions by altering perceived benefits.

Taiwan presents a distinct regulatory and cultural context for examining e-cigarette use. In 2023, the government implemented a comprehensive ban on e-cigarettes under the Tobacco Hazards Prevention Act (Ministry of Health and Welfare, 2023). Nevertheless, social media platforms in Taiwan remain saturated with vaping-related content, including product promotions, lifestyle imagery, and peer endorsements, often beyond the scope of regulatory enforcement. For example, a survey of 1,571 Taiwanese college students found that 75% had been exposed to e-cigarette content on social media, 31% had actively searched for such content, and 16% had shared it (L. L. Liao et al., 2024). Additionally, Taiwanese youth are among the most active social media users globally, with platforms such as LINE, Facebook, Instagram, and Dcard dominating daily engagement; over 83.2% of students reported using Instagram daily (Ho et al., 2022). This disconnect between regulatory policy and digital exposure underscores the need for locally relevant evidence to guide youth-oriented prevention strategies.

Therefore, this study examines whether perceived benefits mediate the relationship between different forms of social media engagement (browsing, searching, and sharing) and e-cigarette use intentions among Taiwanese college students who were not using e-cigarettes at the time of the survey. This study aims to generate foundational evidence to support public health strategies and policy interventions aimed at reducing the growing interest in e-cigarette use among young adults.

Methods

Study Design and Participants

A cross-sectional survey design was adopted to examine factors influencing e-cigarette use intentions among Taiwanese college students. The study specifically targeted students who had not used e-cigarettes in the past 30 days, referred to as non-current users (Giovenco et al., 2014). Eligible participants were those enrolled in at least one course at a 4-year university in Taiwan at the time of data collection.

To recruit participants, we employed a combination of convenience and snowball sampling. Six universities across different geographic regions of Taiwan were initially contacted and agreed to participate. Recruitment was conducted through campus announcements, outreach via student organizations, and electronic communications (e.g., email and student portals), including online survey links disseminated through social media. This non-probability sampling approach was selected due to the difficulty of identifying non-user populations using randomized methods, especially given the sensitive nature of e-cigarette-related behaviors. While this method limits generalizability, it is widely accepted in exploratory studies that investigate behavior-specific or hard-to-reach populations (Shaghaghi et al., 2011; Valerio et al., 2016). Moreover, because social media use is nearly universal among Taiwanese college students (Ho et al., 2022), online recruitment was not expected to introduce significant sampling bias in this population.

Data Collection

Data were collected during the fall semester of 2020 through a self-administered, anonymous online questionnaire. The survey took approximately 15 min to complete and was accessible via a secure web link. Informed consent was obtained electronically, and participants were assured that they could withdraw from the study at any time without penalty. To express appreciation for their participation, students received a nominal incentive valued at approximately $2 in the form of gifts, which were mailed to them. A total of 1,519 participants were successfully recruited. All participants reported having experience using social media, reflecting the near-universal use of platforms such as LINE, Instagram, and Dcard among Taiwanese college students (Ho et al., 2022). Therefore, although recruitment was conducted online, the risk of excluding students unfamiliar with social media was minimal.

Ethical approval for the study was obtained from the Research Ethics Committee (approval number). All data were anonymized to ensure participant confidentiality.

Measures

The questionnaire was divided into four sections. It began with the E-cigarette Benefit Perception Scale, adapted from Taiwan’s E-cigarette Risk and Benefit Perception Scale (L. L. Liao et al., 2024). This instrument is based on Copeland et al.’s (2017) Risks and Benefits of E-cigarettes scale and has been refined to include perceptions specific to Taiwanese college students (L.-L. Liao et al., 2024). The E-cigarette Benefit Perception Scale included 22 items measuring the perceived benefits of e-cigarettes. Items were rated on a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). This scale has been validated in previous Taiwanese research and demonstrated strong internal consistency of 0.94 in the college student population.

The second section assessed social media engagement using items adapted from Emery et al. (2014), which classify user interactions with tobacco-related content into distinct behavioral modes. Participants were asked to report how often they engaged in three types of behaviors related to e-cigarette content on social media: browsing (passively viewing content without intent or interaction), searching (actively seeking out information using keywords or hashtags), and sharing (reposting, forwarding, or publicly commenting on such content). Response options ranged from “never” to “often” (more than three times). These behaviors reflect varying levels of engagement: browsing represents passive exposure with minimal user involvement, while searching and sharing represent more intentional and interactive forms of active engagement. This classification aligns with prior research that distinguishes passive consumption from active participation in digital media contexts.

The third section collected participants’ background information, including sex, age, academic performance (self-rated as top, middle, bottom of their class, or unsure), and social media use. Participants were asked whether they used any social media platforms (e.g., LINE, Dcard, Instagram) and, if so, how many days per week they typically used them. Response options ranged from 0 to 7 days. We also collected data on current use of combustible cigarettes. Participants who had smoked a combustible cigarette within the past 30 days were classified as current users. Furthermore, we gathered data on past e-cigarette use. Participants who had used e-cigarettes more than 30 days ago were considered to have past e-cigarette experience.

The fourth section assessed behavioral intentions regarding e-cigarette use, using two items adapted from previous research (H. Y. Lee et al., 2017; Zou et al., 2024). Participants responded to the following questions: “Do you think you will use e-cigarettes in the next 12 months?” and “If one of your best friends offers you an e-cigarette, will you use it?” Responses were recorded on a 5-point Likert scale ranging from “definitely yes” to “definitely not.” Participants who responded “definitely not” to both questions were categorized as having no intention to use e-cigarettes.

Data Analysis

Descriptive statistics were calculated using means, standard deviations (SDs), frequencies, and percentages. Independent sample t-tests and chi-square tests were conducted to examine differences in participant characteristics based on whether they had reported intentions to use e-cigarettes. To assess whether perceived benefits mediated the relationship between social media engagement and e-cigarette use intentions, we applied the approach outlined by Baron and Kenny (1986). This method requires the following three conditions to establish mediation: (1) the independent variable must significantly affect the dependent variable (M1); (2) the independent variable must significantly affect the mediator (M2); and (3) both the independent variable and the mediator must significantly affect the dependent variable when included in the same model (M3). If all three conditions are met, mediation is supported. We then tested the indirect effects of each independent variable using the bootstrap resampling method (Efron, 1979). Given that the dependent variable was dichotomous, binary logistic regression was used. To analyze the indirect effects of the three forms of social media engagement separately, we followed Hayes’s (2018) recommendations: one engagement type was treated as the independent variable, while the other two were included as covariates. Indirect effects were computed, and the variables were rotated to evaluate each one in turn. Two-sided p-values less than .05 were considered statistically significant. All analyses were conducted using IBM SPSS version 25.0 for Windows.

Results

A total of 1,519 college students participated in this study. All participants reported using at least one social media platform (e.g., LINE, Instagram, or Dcard), and 89.9% reported daily use. Based on self-reported responses, participants were categorized into two groups: those without e-cigarette use intentions (N = 1,320, 86.90%) and those with e-cigarette use intentions (N = 199, 13.10%; Table 1). The total sample was predominantly female (63.92%) with an average age of approximately 20.75 years. Regarding academic performance, the largest proportion of respondents (36.93%) rated themselves as middle performers. Overall, social media engagement related to e-cigarettes was low. Most participants (70.24%) had never searched for e-cigarette content, and 84.79% had never shared it. Both past e-cigarette use (8.62%) and current use of combustible cigarettes (4.74%) were also relatively low. On average, the participants’ perceived benefits of e-cigarettes were moderate, with a mean score of 3.35 on a 1 to 7 scale.

Table 1.

Participants Characteristics and Comparison Based on Whether They Had E-cigarette Use Intentions.

	Total		Without e-cigarette use intentions		With e-cigarette use intentions
	(N = 1,519)		(N = 1,320)		(N = 199)
Variable	N	%	N	%	N	%	X ² /T	p-Value
Sex							27.652^a	<.001
Male	548	36.08	443	33.56	105	52.76
Female	971	63.92	877	66.44	94	47.24
Age^†	20.75 (1.39)		20.73 (1.36)		20.87 (1.53)		−1.277	.203
Academic performance							32.159^a	<.001
Top	538	35.42	500	37.88	38	19.10
Middle	561	36.93	475	35.98	86	43.22
Bottom	174	11.45	136	10.30	38	19.10
Unsure	246	16.19	209	15.83	37	18.59
Social media engagement
Browsing^†	0.98 (0.70)		0.95 (0.70)		1.18 (0.70)		−4.304	<.001
Never	389	25.61	355	27.10	34	17.09		<.001
Rarely	765	50.36	669	51.07	96	48.24
Often	355	23.37	286	21.83	69	34.67
Searching^†	0.34 (0.56)		0.30 (0.53)		0.60 (0.67)		−6.153	<.001
Never	1,067	70.24	967	73.82	100	50.25		<.001
Rarely	376	24.75	298	22.75	78	39.20
Often	66	4.34	45	3.44	21	10.55
Sharing^†	0.17 (0.43)		0.15 (0.40)		0.32 (0.58)		−4.068	<.001
Never	1,288	84.79	1,141	87.10	147	73.87
Rarely	185	12.18	145	11.07	40	20.10
Often	36	2.37	24	1.83	12	6.03
Past use of e-cigarettes (>30 days ago)							245.478^a	<.001
Never used	1,388	91.38	1,264	95.76	124	62.31
Used	131	8.62	56	4.24	75	37.69
Current use of combustible cigarettes							180.746^a	<.001
No	1,447	95.26	1,295	98.11	152	76.38
Yes	72	4.74	25	1.89	47	23.62
Perceived benefits of e-cigarettes^†	3.35 (1.48)		3.25 (1.48)		3.99 (1.31)		−7.279	<.001

Note. Range: Age 18 to 27 years, E-cigarette perceived benefit 1 to 7 (agreement level), social media e-cigarette exposure 0 to 2 (“never” to “often”).

†

Mean (SD).

X² analysis was performed.

Comparison Between Respondents with and Without e-cigarette Use Intentions

The proportion of male participants differed significantly between the two groups, with a higher percentage observed among those with e-cigarette use intentions (t = 27.652, p < .001; Table 1). Academic achievement also varied significantly between the two groups (X² = 32.159, p < .001).

All three types of social media engagement differed significantly between the groups: browsing (X² = 19.102, p < .001), searching (X² = 52.335, p < .001), and sharing (X² = 27.800, p < .001). Respondents with e-cigarette use intentions were most likely to engage frequently in browsing (34.67% vs. 21.83%), searching (10.55% vs. 3.44%), and sharing (6.03% vs. 1.83%) content related to e-cigarettes compared to those without such intentions.

Past use of e-cigarettes (X² = 245.478, p < .001), current use of combustible cigarettes (X² = 180.746, p < .001), and perceived benefits of e-cigarettes (t = −7.279, p < .001) also differed significantly between the two groups. A larger proportion of participants with e-cigarette use intentions reported past e-cigarette use (37.69% vs. 4.24%) and current combustible cigarettes use (23.62% vs. 1.89%). They also perceived greater benefits from e-cigarettes, with an average rating of 3.99 compared to 3.25 among those without use intentions.

Mediating Effect of Perceived Benefits of E-cigarettes

To examine the mediating role of perceived benefits, three regression models were estimated (see Table 2 and Figure 1). In Model 1, social media engagement significantly predicted e-cigarette use intentions, with a Nagelkerke R² of 28.3% and model log-likelihood of 250.67 (p < .001). Among the engagement behaviors, only searching for e-cigarette information was significantly associated with higher intention to use (log-odds = 0.46, p < .05), while browsing and sharing showed no significant direct effects.

Table 2.

Mediating Effect of Perceived Benefits of E-cigarettes.

	Mediator	Dependent
	Perceived benefits of e-cigarettes	E-cigarette use intentions
Variable	M2	M1	M3	Bootstrap
Effect		Total effect	Direct effect	Indirect effect
X1: Browsing e-cigarette information	−0.02 (p > .05)	0.06 (p > .05) (OR = 1.06)	0.06 (p > .05) (OR = 1.07)	−0.01 (0.96–1.03) (OR = 0.99)
X2: Searching e-cigarette information	0.08 (p > .05)	0.46* (OR = 1.59)	0.47* (OR = 1.59)	0.03 (0.97–1.09) (OR = 1.03)
X3: Sharing e-cigarette information	0.42***	−0.13 (p > .05) (OR = 0.88)	−0.28 (p > .05) (OR = 0.76)	0.13 (1.05–1.25) (OR = 1.14)
Mediator: Perceived benefits			0.31*** (OR = 1.36)
Model summary
R ²	0.05	−2LL = 926.16	−2LL = 898.16
R ²	0.05	CoxSnell R² = .153	CoxSnell R² = .169
F	8.38***	Nagelkerke R² = .283	Nagelkerke R² = .311
df	9.1499	Model LL = 250.67***	Model LL = 278.66***

Note. Sex, age, academic performance, past use of e-cigarettes, and current use of combustible cigarettes were controlled for. M1: Relationship between independent and dependent variables; M2: Relationship between independent and mediating variables; M3: Combined effect of independent and mediating variables on dependent variables. X1, X2, and X3 = independent variables. Coefficients for mediating variables are unstandardized regression coefficients (β). For dependent variables, the coefficients are log-odds and odds ratios. Indirect effects are shown as log-odds and 95% CI from 5,000 bootstrap samples.

p < .05. ***p < .001.

Figure 1.

Mediating effects of perceived benefits of e-cigarettes.

Model 2 tested the association between social media engagement and perceived benefits. The overall model was significant (F = 8.38, p < .001; R² = .05), and sharing behavior emerged as the only significant predictor of perceived benefits (B = 0.42, p < .001).

In Model 3, when perceived benefits were included alongside the engagement variables, it remained a significant predictor of e-cigarette use intentions (log-odds = 0.31, p < .001), and the overall model fit improved (Nagelkerke R² = 31.1%; log-likelihood = 278.66, p < .001). These results support a mediating effect of perceived benefits.

Bootstrapping procedures using 5,000 resamples confirmed a significant indirect effect for sharing through perceived benefits (OR = 1.14, 95% CI [1.05, 1.25]). No indirect effects were found for browsing or searching. These findings suggest that searching directly influenced behavioral intentions, while sharing exerted an indirect influence via perceived benefits, and browsing had no measurable impact on either outcome.

Discussion

This study examined how different forms of social media engagement, including browsing, searching, and sharing, are associated with e-cigarette use intentions among Taiwanese college students who were not current users. Searching for e-cigarette-related content was directly associated with greater use intentions, while sharing exhibited an indirect effect mediated by perceived benefits. In contrast, browsing was not significantly related to either perceived benefits or behavioral intentions. These findings suggest that different types of engagement carry varying psychological weight and that perceived benefits may serve as a key mechanism linking specific forms of social media engagement with e-cigarette use intentions.

The results indicate a strong link between e-cigarette use intentions and increased engagement with e-cigarette-related content on social media. Previous research has shown that individuals with stronger use intentions are more likely to interact with tobacco-related content on digital platforms (Alpert et al., 2020; Emery et al., 2014; J. Lee et al., 2023). A recent systematic review further found that social media exposure increases the likelihood of tobacco use even among never-users (Donaldson et al., 2022), underscoring the influential role of social media in shaping e-cigarette-related attitudes and behaviors.

According to Bandura’s (1999) Social Cognitive Theory (SCT), individuals form beliefs and intentions by observing others’ behaviors and the social feedback they receive. In the context of social media, sharing e-cigarette-related content may not only reflect pre-existing attitudes but also function as a form of observational learning and social reinforcement. Positive peer responses (e.g., likes, comments) can amplify perceived benefits by signaling social approval, thereby increasing behavioral intentions through vicarious experience and cognitive consistency (Bandura, 2001; Turel & Qahri-Saremi, 2024). The present findings extend SCT by demonstrating that not all forms of engagement operate equally: active behaviors such as sharing influence behavioral intentions indirectly through perceived benefits. This highlights the need for future theoretical models to explicitly differentiate between engagement types when predicting health risk behaviors in digital environments. Such differentiation is particularly relevant for understanding how digital platforms shape emerging health risk behaviors.

Peer-shared messages, such as endorsements, lifestyle portrayals, or influencer posts, are often perceived as more authentic and persuasive than commercial advertisements, especially among adolescents and young adults (Sidani et al., 2022; Zarouali et al., 2018). This may help explain why the sharing dimension, more than browsing, was indirectly associated with use intentions via perceived benefits. Previous research has shown that such user-generated content frequently frames e-cigarettes as fashionable, beneficial, or less harmful (Lazard et al., 2016; McCausland et al., 2019; Sidani et al., 2022), reinforcing social norms that favor vaping.

Importantly, only active engagement behaviors, specifically searching and sharing, were significantly associated with e-cigarette use intentions. This finding supports our operational distinction between passive (browsing) and active (searching, sharing) engagement and aligns with evidence from a meta-analysis of 141 studies, which found that active social media use is linked to more positive psychosocial outcomes, whereas passive use is associated with negative outcomes such as depressive symptoms (Godard & Holtzman, 2023).

In contrast, searching was directly associated with use intentions but was not mediated by perceived benefits. This suggests a goal-directed information-seeking process, where individuals selectively search for content that confirms pre-existing beliefs or satisfies curiosity. This pattern is consistent with Selective Exposure Theory (SET; Knobloch-Westerwick et al., 2015). By demonstrating that searching behavior may influence behavioral intentions without altering perceived benefits, our findings refine SET by suggesting that confirmation-driven information seeking can directly strengthen use intentions independent of benefit perception shifts.

Given the central role of social media in health information-seeking among young adults (Choi et al., 2015), this pattern warrants further investigation in future research, particularly in terms of its implications for prevention strategies. Beyond engagement behaviors, differences in content characteristics may also shape users’ perceptions. Although the current study did not formally categorize content types, previous research indicates that various forms of content, such as peer-shared messages, influencer portrayals, and commercial advertising, may differ in their impact on perceived benefits of e-cigarettes (Kwon & Park, 2020; Lazard et al., 2016; Zarouali et al., 2018). Future studies should explore how content framing and source credibility contribute to these psychological processes.

The prevalence of e-cigarette use among Taiwanese college students remains relatively low (5.4%; Taiwan National Health Administration, 2021). However, this study found that 13.1% of non-users reported intentions to use e-cigarettes, indicating the presence of a sizable at-risk group. Although this rate is lower than that reported in U.S. samples (16.6%; H. Y. Lee et al., 2017), it exceeds the prevalence observed in recent Chinese research (8.0%; Zou et al., 2024), suggesting that local cultural and regulatory factors play an important role. Despite a comprehensive ban on e-cigarettes in Taiwan (Ministry of Health and Welfare, 2023), exposure to pro-vaping messages remains widespread on social media platforms such as LINE, Instagram, and Dcard. Peer-shared content, in particular, has been shown to significantly influence youth attitudes more than commercial advertising. For instance, Zarouali et al. (2018) found that adolescents viewed ads more positively and expressed less skepticism after peer discussion. Consequently, media literacy interventions should target not only tobacco-related corporate marketing but also equip students to critically evaluate user-generated content, which remains pervasive in e-cigarette discourse (Primack et al., 2009)

Given the widespread use of social media among young adults, targeted strategies are needed to counter the influence of pro-vaping messages. Based on our findings, interventions should be tailored to address the specific psychological mechanisms through which searching and sharing behaviors shape behavioral intentions. For educational institutions, critical media literacy should be integrated into health curricula to help students identify persuasive tactics in both corporate advertising and peer-generated content. For policymakers, collaboration with public health professionals and digital platforms is needed to curb algorithmic promotion of vaping-related content, particularly peer-endorsed material, to reduce exposure among at-risk youth. For social media companies, interventions such as fact-checking overlays and redirecting search results toward credible health information may help interrupt the direct pathway between goal-directed searching and increased use intentions.

As many young adults actively seek health information through social media (Adekunle et al., 2019; Choi et al., 2015), prevention efforts must leverage the same platforms to counter persuasive and peer-endorsed content. Evidence-based social media interventions have demonstrated success in other areas of public health (Maher et al., 2016), suggesting similar potential for vaping prevention among non-users. Implementing such strategies in an integrated manner may enhance their preventive impact. This approach aligns with broader digital public health initiatives aimed at mitigating the influence of harmful product marketing.

Limitations

This study has several limitations. First, its cross-sectional design limits the ability to infer causality between social media engagement, perceived benefits, and e-cigarette use intentions. Future studies using longitudinal or experimental designs are recommended to strengthen causal inferences. Second, participants were recruited from six universities via convenience sampling; therefore, the sample may not fully represent the broader population of Taiwanese college students. Third, the reliance on self-reported data may have introduced biases affecting the accuracy of reported behaviors. Fourth, the predominantly female sample may limit the generalizability of the findings across sexes. Prior research has shown that males and females may differ in their social media usage patterns and responses to e-cigarette-related content, including advertising and peer influence (Pokhrel et al., 2015; Vogel et al., 2014). Therefore, overrepresentation of females in our sample may have influenced the observed associations. Future studies should ensure a more balanced sex distribution to examine potential sex-specific mechanisms. Fifth, although major platforms such as LINE, Instagram, and Dcard were commonly used by participants, the study did not assess whether the observed associations varied by platform. Future research should explore platform-specific effects, as variations in content types and engagement norms may influence outcomes. Lastly, although the survey was conducted anonymously, responses related to social media engagement and e-cigarette use intentions may still be subject to social desirability bias. Future studies may consider using behavioral trace data or experimental exposure paradigms to obtain more objective measures. Additionally, while this study adopted a two-item measure of behavioral intentions based on previous literature, future research could consider multi-item scales to better capture the underlying psychological dimensions of decision-making.

Conclusions

This study highlights the significant role of social media in influencing e-cigarette use intentions among young adults. Active forms of engagement, particularly searching and sharing, were more strongly associated with behavioral intentions than passive browsing, and perceived benefits mediated the effect of sharing. These findings underscore the importance of addressing how social media fosters positive perceptions of e-cigarettes among non-users.

Given the high prevalence of social media use among youth, public health campaigns should incorporate media literacy strategies and platform-specific interventions to counter the influence of user-generated content. Although this study offers valuable insights, its cross-sectional design and limited generalizability highlight the need for longitudinal research to examine causal pathways and platform-specific effects.

Footnotes

Acknowledgements

The authors thank all participants who contributed to this work for their support and assistance.

ORCID iDs

I-Ju Lai

Chia-Kuei Lee

Li-Ling Liao

Ethical Considerations

Ethical approval was obtained from the National Cheng Kung University Human Research Ethics Committee (NCKU HREC-E-109-011-2). To ensure confidentiality, the collected data were anonymized.

Consent for Publication

Not applicable.

Author Contributions

Li-Ling Liao: Conceptualization, methodology, funding acquisition, and supervision. I-Ju Lai: Investigation, formal analysis, and writing-original draft. Li-Chun Chang: Data curation, validation, writing - original draft. Chia-Kuei Lee: Software, validation, and writing - review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Taiwan National Science and Technology Council under Grant [NSTC 109-2511-H-214-003].

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author L-L Liao upon reasonable request.

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Perceived Benefits as a Mediator of Social Media’s Influence on E-cigarette Intentions Among Current Nonusers in Taiwanese College Students

Abstract

Plain Language Summary

Keywords

Background

Methods

Study Design and Participants

Data Collection

Measures

Data Analysis

Results

Comparison Between Respondents with and Without e-cigarette Use Intentions

Mediating Effect of Perceived Benefits of E-cigarettes

Discussion

Limitations

Conclusions

Footnotes

Acknowledgements

ORCID iDs

Ethical Considerations

Consent for Publication

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References