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. Author manuscript; available in PMC: 2026 Oct 1.
Published in final edited form as: J Am Coll Health. 2026 Feb 24;74(7):2031–2038. doi: 10.1080/07448481.2026.2630041

Perceived norms and e-cigarette use among young adults in US college: The mediation role of outcome expectancies

Joshua Awua a, Micki Washburn a, MariePearl Agordzo b, Yao Wang a, Ernestina Aidoo b, Dorothy Gabben-Mensah c, Conor A Mullarkey d, Derek Falk a, Matthew K Meisel e, Antover P Tuliao b
PMCID: PMC13625621  NIHMSID: NIHMS2212107  PMID: 41735253

Abstract

Objectives:

This study examines the mediating roles of outcome expectancies in the relationship between perceived norms and e-cigarette use among college-aged young adults.

Participants:

The sample of the study includes 616 college students (Mage = 20.49, SD = 1.48; women = 82.0%; White = 83.4%).

Methods:

A confidential online survey on young adults’ e-cigarette network was administered to college students at a large Hispanic-serving public university in the southwestern United States

Results:

Positive reinforcement expectancies partially mediated the association between descriptive norms and daily e-cigarette use frequency. Thus, descriptive norms had a positive indirect effect on daily e-cigarette use frequency through positive reinforcement expectancies.

Conclusions:

The findings highlight that positive reinforcement outcome expectancies, such as enjoying the flavor of e-cigarettes, may be critical in explaining the association between descriptive norms and e-cigarette use among college students. College-based e-cigarette prevention campaigns should challenge positive beliefs about e-cigarettes and target students with negative reinforcement expectancies, who are at higher risk for dependence.

Keywords: College students, descriptive norms, e-cigarette use, injunctive norms, outcome expectancies, young adults

Introduction

Although electronic nicotine delivery systems (e-cigarettes) use has been promoted as a safe alternative to combustible cigarettes1,2 and an effective smoking cessation aid,2,3 emerging evidence indicates that e-cigarette use among young adults is associated with negative health outcomes such as cardiovascular and respiratory diseases, acute lung injury, and other health-related outcomes.4,5 Moreover, among young adults, frequent e-cigarette use may increase the odds of nicotine addiction, combustible cigarette smoking, and mental health problems.6,7 Recent national prevalence estimates among US college students indicate that approximately 15.8% used e-cigarettes in the past three months.8 Examining the potential factors that promote e-cigarette use among college students is essential for identifying modifiable factors that can be targeted in college-based intervention programs to reduce e-cigarette use.

Social cognitive theory (SCT)9,10 is useful for understanding how socio-cognitive factors drive young adults’ e-cigarette use. Social cognitive theory posits that the interactions among an individual’s social environment and cognitions influence human behavior.10 According to SCT, an individual’s behavior is largely influenced by what they observe others doing in their social environment and by the expected outcomes of that behavior.11,12 Peer pressure influences college students’ attitudes, expectations, and behaviors toward substance use.13 Youth and young adults often conform their behaviors to those of their peers to gain acceptance in the group, which can lead to substance use.14 Studies demonstrate that perceiving that peers use or approve of alcohol is associated with a greater risk of developing positive expectancies about the effects alcohol will have on their lives, which, in turn, increases the risky drinking.13,15–18

Given that alcohol expectancies can be influenced by perceived norms and that these influence alcohol use outcomes,17,18 it is possible to hypothesize that e-cigarette expectancies may mediate the association between perceived norms and e-cigarette use among college students. Thus, if young adults increasingly perceive that their peers use and approve of e-cigarettes and expect e-cigarettes to taste good or help relieve their stress, they are more likely to try e-cigarettes themselves. However, to our knowledge, the indirect association has not yet been tested with regard to e-cigarette use. This study addresses this critical knowledge gap by cross-sectionally examining the possible indirect association between perceived norms and e-cigarette use among college students via e-cigarette outcome expectancies.

Perceiving that friends use and approve of e-cigarettes is consistently associated with young adults’ e-cigarette use.19,20 In the literature, this is termed perceived norms- the perception that others use (i.e., descriptive norms) or approve of (i.e., injunctive norms) e-cigarette use.21,22 Existing evidence indicates that young adults tend to overestimate their peers’ e-cigarette use.19,20 For example, when young adults believe that their peers use or approve of using e-cigarettes, they are more likely to initiate using e-cigarettes themselves, often to gain acceptance and a sense of belonging among group members.23,24 Thus, individuals may engage in behaviors similar to those of their peers because of the anticipated social benefits or costs associated with those behaviors.

An established body of research indicates that young adults’ beliefs about the consequences of e-cigarette use (i.e., outcome expectancies) are associated with e-cigarette use initiation, frequency, and dependence.25,26 Outcome expectancies are formed based on observations and social exposure to e-cigarette use.27 E-cigarette use outcome expectancies consist of positive reinforcement, negative reinforcement, negative consequences, and weight and appetite control.25 These subscales exert differential influences on e-cigarette use outcomes. For instance, young adults who perceive e-cigarettes as safer, more enjoyable, and promote social acceptance (i.e., positive reinforcement),28,29 control weight and appetite,24,30,31 and manage negative affect32,33 are at risk of e-cigarette use. However, expectancies concerning negative health implications correlate with a reduced likelihood of e-cigarette use among young adults.28,29

However, research has yet to examine the indirect association between perceived norms and e-cigarette use through e-cigarette outcome expectancies. Of note, limited studies have reported that outcome expectancies mediate the association between perceived norms of alcohol use; however, the results differ by expectancies subtypes.13,15,16,18 For instance, Walther et al.18 found a significant indirect association between close friend alcohol use and alcohol use through social enhancement expectancies, but not cognitive and motor enhancement expectancies. In addition, Bartolo et al.13 found a significant indirect association between peer pressure and motivation for responsible drinking through positive alcohol expectancies. There is reason to believe that e-cigarette outcome expectancies may mediate the association between perceived norms and young adults’ e-cigarette use. Understanding how specific outcome expectancies relate to the association between perceived norms and e-cigarette use has implications for prevention campaigns targeting e-cigarette use by directly addressing expectancies that promote use.

The present study

Although the direct effect of perceived norms and outcome expectancies on e-cigarette use has been well established in the literature,20,34 the indirect association between perceived norms and e-cigarette use via outcome expectancies remains unexamined. The current study extends the literature by using cross-sectional data to examine whether the dimensions of outcome expectancies mediate the association between perceived norms (descriptive and injunctive) and e-cigarette use. We hypothesized that the dimensions of e-cigarette outcome expectancies: (1) positive reinforcement, (2) negative reinforcement, (3) negative consequences, and (4) weight or appetite control will mediate the association between perceived norms and e-cigarette use among young adults. A graphic depiction of these relationships is shown in Figure 1.

Figure 1.

Figure 1.

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Study model showing the association between perceived norms, outcome expectancies, and e-cigarette use.

Methods

Participants

The sample was 616 college students (age range 18–25) recruited from a large public university in the southwestern United States. Most participants were White and seniors. Sample descriptive statistics in Table 1.

Table 1.

Descriptive characteristics of study variables.

n %
Sex
 Male 111 18.0
 Female 505 82.0
Race
 White 513 83.4
 Black 33 5.4
 American Indian 6 1.0
 Asian American 17 2.8
 Native Hawaiian 2 0.3
 Other 44 7.2
Tobacco use
 Yes 433 73.0
 No 160 27.0
Cannabis vaping
 Yes 496 80.7
 No 119 19.3
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Procedures

Data were collected from undergraduate students attending a large public university in the Southern United States from February 2023 to December 2023 using the SONA online survey system (www.sona-systems.com). SONA is a web-based survey management system that allows students to participate in online surveys and receive course credit. A Qualtrics online survey data collection platform link was integrated into SONA, which directed students to complete the survey. Students were eligible to complete the survey if they were at least 18 years old and reported lifetime e-cigarette use. Informed consent was obtained electronically, and the survey took approximately 40 min to complete. Participants’ attention was checked with three items (e.g., “When asked for your favorite color, you must choose ‘Red’”); participants were flagged and removed from the study if they inaccurately responded to two of the three attention check questions.35,36 The Institutional Review Board (IRB) at Texas Tech University) approved the research protocols.

Measures

Perceived norms:

Descriptive norms were measured with the item, “How many times per day do your friends usually vape/use e-cigarettes?” Injunctive norms were measured with the item, “How many times a day do your friends approve it is okay to vape?” Participants reported how many times per day they perceive their friends use or approve of e-cigarettes. Items were adapted from studies that used a single item to assess descriptive and injunctive norms.37,38 Perceived norms were assessed using a frequency-based format (i.e., number of times per day), consistent with prior work in the alcohol literature that operationalizes descriptive and injunctive norms using parallel behavioral metrics.37 This approach aligned the measurement of perceived norms with the frequency-based e-cigarette use outcome, thereby ensuring consistency in the underlying construct being assessed.

E-cigarette outcome expectancies:

E-cigarette expectancies were measured with the 21-item Short Form Vaping Consequences Questionnaire (SVCQ).25 This scale measures expectancies for positive reinforcement (e.g., I enjoy the flavor of cigarettes), negative reinforcement (e.g., smoking calms me down when I feel nervous), negative consequences (e.g., Smoking is hazardous to my health), and appetite/weight control (e.g., e-cigarettes keep me from overeating). Participants rated the likelihood of each outcome occurring using a 10-item scale, ranging from 0 (completely unlikely) to 9 (completely likely). The items were mean-scored, with higher means indicating higher expectancies. The SVCQ showed good internal consistency alpha ranging from .88 to .95.25 The alphas for the present study were positive reinforcement = .91, negative reinforcement = .94, negative consequence = .92, and weight/appetite control = .94.

Daily frequency of e-cigarette use:

An item was adapted from the Penn State Nicotine Dependence Index39 to measure e-cigarette use. Daily frequency of e-cigarette use was assessed with a single item, “How many times do you usually vape per day?”. Participants reported the number of times they usually used an e-cigarette per day.6,7,40,41

Control variables:

Sex, race, age, lifetime tobacco use, and cannabis vaping were included in the statistical analyses as control variables.

Data analytic plan

Descriptive statistics and bivariate correlations were conducted using SPSS Version 29 (IBM Corp., 2022). Primary analyses were performed in Mplus Version 8.2,42 utilizing robust maximum likelihood (MLR) estimation and Monte Carlo integration. MLR estimation was chosen for its ability to provide robust standard errors and test statistics less sensitive to the data’s non-normality. Monte Carlo integration was employed to accurately estimate model parameters and standard errors in complex models, particularly those involving indirect effects and latent constructs. There was missing data across key study variables. Specifically, missingness was 11.8% for descriptive norms, 5.1% for injunctive norms, 3.4% for expectancy domains related to appetite control, negative reinforcement, and negative consequences, 3.6% for positive reinforcement, and 6.7% for the daily frequency of e-cigarette use. Full information maximum likelihood estimation was used to account for missing data. In this mediation model, perceived norms (i.e., descriptive and injunctive) were entered simultaneously to examine their unique contributions while accounting for shared variance. Outcome expectancies and daily frequency of e-cigarette use were modeled as mediator and outcome, respectively (perceived norms-> outcome expectancies -> daily frequency of e-cigarette use).

Given that the outcome variable is a count variable (i.e., daily frequency of e-cigarette use), severely positively skewed, and over-dispersed, the models used a negative binomial distribution for daily vaping episodes. Daily frequency of e-cigarette use was modeled using negative binomial regression. The conditional variance was greater than the conditional mean, indicating overdispersion. All log counts were exponentiated into incidence rate ratios (IRRs) as a measure of effect size, and all predictors were mean-centered for ease of interpretation. The indirect associations between perceived norms and e-cigarette use via outcome expectancies were estimated, an indirect association was considered statistically significant if the 95% confidence interval did not include zero.

Results

Bivariate correlations

Table 2 presents the means, standard deviations, and bivariate correlations among the predictors, outcomes, and covariates. Daily e-cigarette use frequency was positively correlated with injunctive norms, descriptive norms, positive reinforcement, negative consequences, negative reinforcement, and appetite control. Injunctive and descriptive norms were positively associated with positive reinforcement, negative consequences, negative reinforcement, and appetite control.

Table 2.

Correlation among study variables.

1 2 3 4 5 6 7 8 9 10 11 12
1.Age --
2.Sex −0.16** --
3.Race .01 −0.11** --
4.Tobacco .13** −0.15** −0.09* --
5.Cannabis vaping −0.03 −0.00 .05 .25** --
6.Injunctive norms −0.00 .02 .01 .08* .04 --
7.Descriptive norms −0.03 .06 −0.09* .11* .07 .64** --
8.Positive reinforcement −0.03 −0.04 .03 .21** .15** .17** .21** --
9.Negative reinforcement −0.03 .06 −0.03 .09* .15** .15** .18** .70** --
10.Negative consequences −0.05 .00 −0.01 .17** .19** .14** .18** .66** .49** --
11.Appetite control −0.09* .06 −0.02 .11** .18** .08 .12** .55** .76** .42** --
12.Daily e-cigarette use .07 −0.01 −0.02 .19** .10* .62** .69** .37** .33** .26** .21** --
 Mean 20.49 16.89 20.54 13.63 11.84 14.91 11.17 17.37
 Standard deviation 1.48 17.37 19.44 4.11 4.97 3.88 5.26 19.59
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Note.

*

p < .05.

**

p < 0.01.

Mediation results

The path analysis results are presented in Figure 2. After accounting for all covariates, the results show that higher descriptive norms were associated with higher positive reinforcement expectancies (b = 0.03, p = .019, 95% CI [0.01, 0.06], β = .15) and negative consequences (b = 0.03, p = .040, 95% CI [0.00, 0.05], β = .13) but not associated with negative reinforcement (b = 0.03, p = .072, 95% CI [−0.00, 0.06], β = .11) and appetite or weight control (b = 0.03, p = .092, 95% CI [−0.01, 0.06], β = .10). Injunctive norms were not associated with any of the e-cigarette use outcome expectancy subscales. Daily frequency of e-cigarette use was associated with positive reinforcement expectancies (b = 0.69, IRR = 1.99, p = .001, 95% CI [1.21, 2.77]) and negative reinforcement expectancies (b = 0.63, IRR = 1.88, 95% CI [1.10, 2.66], p = .003) but not negative consequences expectancies (b = −0.02, IRR = 0.98, p = .934, 95% CI [0.66, 1.30]), and appetite control (b = −0.24, IRR = 0.79, p = .145, 95% CI [0.54, 1.04]). In addition, after adjusting for all subscales of e-cigarette outcome expectancies, descriptive norms (b = 0.47, IRR = 1.60, 95% CI [1.43, 1.78], p > .001) and injunctive norms (b = 0.31, IRR = 1.36, p = .001, 95% CI [1.19, 1.52]) were directly associated with daily frequency of e-cigarette use. Of the four subscales of e-cigarette use expectancies, only positive reinforcement e-cigarette use expectancies partially mediated the association between descriptive norms and daily frequency of e-cigarette use. There was no mediation effect for negative reinforcement, negative consequences, and appetite or weight control subscales.

Figure 2.

Figure 2.

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Final model showing the association between perceived norms, outcome expectancies, and e-cigarette use. Note. The model controlled for age, gender, tobacco use, and cannabis vaping. Standardized coefficients are shown in the figure. *p < .05; ***p < .001.

The total effect indicated that the daily frequency of e-cigarette use was positively associated with descriptive norms (b = 0.50, p > .001, 95% CI [0.39, 0.62]) and injunctive norms (b = 0.33, p > .001, 95% CI [0.20, 0.46]). The total indirect effect of descriptive norms on daily frequency of e-cigarette use was significant (b = 0.03, p = .027, 95% CI [0.01, 0.06]). Thus, descriptive norms influence daily frequency of e-cigarette use through only positive reinforcement expectancies (b = 0.02, p = .032, 95% CI [0.00, 0.04]). The total indirect effect of injunctive norms on daily e-cigarette use episodes was not significant.

Auxiliary analysis

Given that the data were cross-sectional, the research team cannot make causal claims regarding the indirect effect of perceived norms on e-cigarette use. It is possible that participants perceived norms mediate the association between expectancies and e-cigarette use. To address these alternative explanations, the indirect association between outcome expectancies and e-cigarette use via perceived norms was tested. The results showed that positive (IRR = 2.01, b = 0.70, p > .001, 95% CI [1.22, 2.80]) and negative (IRR = 1.83, b = 0.61, p = .004, 95% CI [1.07, 2.60]) reinforcement expectancies were directly associated with daily e-cigarette use. Only positive reinforcement expectancies were directly associated with descriptive norms (b = 0.65, p = .033, 95% CI [0.05, 1.24], β = .17), but no significant indirect association was found (b = 0.30, p = . 050, 95% CI [0.00, 0.59], β = .07). Both descriptive and injunctive norms were associated with daily e-cigarette use.

Discussion

This study extended previous literature by examining the indirect association between perceived norms and daily e-cigarette use episodes via e-cigarette use outcome expectancies. We hypothesized that there would be indirect associations between perceived norms and daily e-cigarette use episodes through the dimensions of outcome expectancies. Our hypothesis was partially supported, with positive reinforcement expectancies partially mediating the association between descriptive norms and daily e-cigarette use frequency.

Indirect association between descriptive norms and daily e-cigarette use episodes

The results showed that positive reinforcement expectancies partially mediated the association between descriptive norms and daily e-cigarette use frequency. This suggests that the association between the perception that peers use e-cigarettes and individuals’ e-cigarette use can partly be explained by the positive outcomes people expect to get from using e-cigarettes. E-cigarettes come in various attractive shapes and flavors, appealing to young adults to use. For instance, flavors enhance the appeal of e-cigarettes by creating sensory perceptions of sweetness and coolness and masking the aversive taste of nicotine.43,44

Given that all participants in the present study had a history of e-cigarette use, the outcome expectancies likely reflect learned or subjective effects of use rather than naïve beliefs formed before initiation of e-cigarette use. In this context, expectancies may be shaped by repeated experience of reinforcing sensory effects of e-cigarette use, which may in turn influence more frequent use. Consistent with existing evidence,17 outcome expectancies have been found to influence not only substance use but also its maintenance, suggesting that the observed indirect association may reflect sustained use rather than initial experimentation.

This finding also enhances our understanding of how social perceptions initiate behavior, with the expected reward reinforcing the behavior. Beliefs that peers use e-cigarettes might influence young people to initiate e-cigarette use, and the expected positive outcomes, such as pleasure, relaxation, and social acceptance, can drive ongoing use.45

Descriptive and injunctive norms directly associated with e-cigarette use episodes

The direct associations between both descriptive and injunctive and daily e-cigarette use episodes further support that social normalization plays a vital role in sustaining high levels of use. The perceptions of how commonly peers use e-cigarettes and perceived peer approval reinforce the acceptability of frequent use among young people. The perceived normalization creates an environment where e-cigarette use is socially acceptable and endorsed, decreasing the perceived risks and stigma associated with e-cigarette use. Among college students, peer behaviors and beliefs serve as particularly powerful drivers of e-cigarette use as they are at the stage of exploring their identities and are heavily impacted by their social networks.23,24,46

Negative reinforcement associated with e-cigarette use episodes

The result showed a direct positive association between e-cigarette use and negative reinforcement outcome expectancies. Thus, when individuals anticipate that using e-cigarettes can reduce their psychological distress and improve their mood, they are more likely to use e-cigarettes several times a day. This could be that e-cigarettes might have a transient positive experience, which helps to lower an individual’s level of psychological distress. Once the active life of the nicotine is over, the individual gets to the same level of psychological distress and might need to take it more frequently to give that euphoric feeling. Thus, individuals may believe that e-cigarette use may be a way to increase positive affect and help distract them from their negative emotions to function better in the moment. This finding may be linked to the fact that many of the negative reinforcement expectancies align with the experience of withdrawal symptoms associated with nicotine dependence, such as feeling nervous, tense, or angry. Overall, both positive and negative reinforcement expectancies were associated with increased daily e-cigarette use, indicating that young adults are driven by a desire for positive experiences and a need to reduce negative emotions.32,33

Descriptive norms directly associated with negative consequences expectancies

We found that descriptive norms were positively associated with negative consequences expectancies. Thus, individuals who perceived a higher prevalence of e-cigarette use among their peers were also more likely to anticipate harmful outcomes associated with e-cigarette use. This may seem counterintuitive, as normative perceptions of greater use are often linked with increased acceptance or reduced perception of risk. However, the positive association between descriptive norms and negative consequences expectancies highlights a more nuanced dynamic. One possible explanation is that as e-cigarette use has become more widespread, individuals are increasingly exposed to public health messaging that emphasizes the harm associated with e-cigarette use, which can reinforce the expectations of negative consequences. In addition, individuals may have peers who have experienced some level of harm, such as bleeding gums or headaches from e-cigarette use, which can further influence their beliefs about the potential risks of using e-cigarettes.

Limitations

The findings must be interpreted in the light of the following limitations. Participants were students from a single large public university, who self-selected to participate in the study, with over 80% of them being white and female; the results may not generalize to other universities or more racially diverse samples. Ethnicity was not assessed, limiting the interpretation of potential disparities and the generalizability of the results. Although descriptive norms indirectly predicted daily e-cigarette use, the cross-sectional design limits causal inference. We performed an auxiliary analysis with perceived norms as mediators, and the results showed no indirect effect. In addition, outcome expectancies may evolve over time as individuals gain more personal experience with using e-cigarettes or become more informed about the potential consequences of use. Therefore, it is essential to examine these temporal changes through longitudinal research.

Although this frequency-based approach allows for conceptual alignment between perceived norms and substance use outcomes, estimating “number of times per day” may be cognitively demanding for some participants and could be subject to bias. Future research should compare frequency-based norms with more traditional prevalence- or proportion-based assessments to determine their relative validity and predictive utility. Finally, while our outcome focused on the frequency of e-cigarette use, ecological momentary assessment may accurately capture these behaviors in real time, increasing accuracy and reducing bias.

Conclusions

The current study extends the literature on the association between perceived norms and e-cigarette use among college students. The findings highlight the relevance of positive outcome expectancies in the relationship between descriptive norms and the daily frequency of e-cigarette use. Specifically, college students who perceive widespread e-cigarette use in their social network and associate e-cigarettes with pleasurable experiences may be at increased risk of frequently using e-cigarettes. In addition, individuals who have strong negative reinforcement expectancies are more likely to use e-cigarettes multiple times per day, suggesting that such individuals may be risky users who might have developed tolerance for the nicotine.6,7

These findings highlight several priority areas for college health campaigns to prevent e-cigarette use. College-based e-cigarette prevention campaigns should challenge positive beliefs about e-cigarettes and target students with negative reinforcement expectancies, who are at higher risk for dependence. Screening for tolerance and offering support for affect regulations may help reduce frequent e-cigarette use among college students. Collaborating with campus-based peer leaders and social groups, such as fraternities and sororities, may enhance the reach and effectiveness of e-cigarette prevention efforts by leveraging peer influence to deliver targeted education that corrects misconceptions about e-cigarette use prevalence and expected outcomes among college students.

Funding

This research was supported in part by grant number K01AA025994 (PI Meisel).

Footnotes

Disclosure statement

The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of the United States and received approval from the Institutional Review Board of Texas Tech University.

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