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. Author manuscript; available in PMC: 2024 Dec 1.
Published in final edited form as: J Behav Med. 2023 Aug 21;46(6):948–959. doi: 10.1007/s10865-023-00441-7

Effects of Reduced Nicotine Content Cigarette Advertising with Warning Labels and Social Media Features on Product Perceptions Among Young Adults

Andrea C Johnson 1,2, Melissa Mercincavage 1,2,6, Andy SL Tan 1,2,4,5,6, Andrea C Villanti 2,3, Cristine D Delnevo 2,3, Andrew A Strasser 1,2,4,6
PMCID: PMC10591832  NIHMSID: NIHMS1932451  PMID: 37605036

Abstract

Introduction:

This study sought to understand reactions to very low nicotine (VLN) cigarette advertising compared with conventional cigarette advertising with consideration of warning labels and social media context.

Methods:

The online experimental study recruited young adult cigarette smokers and nonsmokers (N=1,608). Participants completed a discrete choice task with a 2×2×3 mixed design: brand, (VLN, Marlboro), context (Ad only, Ad on social media), and warning (Text-only, Well-known risk pictorial, or Lesser-known risk pictorial). Participants made choices about attention, appeal, harm, buying, and quitting intentions.

Results:

Social media context increased attention and appeal. A well-known risk pictorial warning outperformed a text-only warning. Smokers had increased odds of quit intentions for VLN ads, yet nonsmokers had increased intentions to buy cigarettes on social media with a text-only warning.

Conclusions:

Results indicate differences in how young adults react to cigarette ads on social media, especially with the warnings they portray.

Keywords: Cigarettes, Addiction, Prevention, Public Health, Marketing, Tobacco Control

INTRODUCTION

Reduced nicotine content cigarettes (RNCs) have the potential to lower mortality and morbidity by reducing nicotine to nonaddictive levels (Apelberg et al., 2018; Hatsukami et al., 2013). To mitigate the burden of nicotine dependence and cigarette use (Benowitz & Henningfield, 2013), the Food and Drug Administration (FDA) has the purview to authorize Modified Risk Tobacco Products (MRTPs) expected to benefit the population as a whole (Government Printing Office, 2009). The 22nd Century Group, Inc. applied and received a MRTP marketing authorization for their very low nicotine VLN® King and VLN® Menthol King cigarettes (FDA, 2022a). This permits these products to be marketed alongside conventional cigarettes, while allowing them to make reduced risk claims.

RNC cigarettes like VLN® present the same health risks associated with smoking traditional cigarettes, but may reduce harm at the population level if they facilitate quitting among people who smoke cigarettes and impede smoking initiation (Apelberg et al., 2018; FDA, 2018b; Hatsukami et al., 2013). A systematic review of RNC marketing indicates that these products are often not well understood, and both smokers and nonsmokers exposed to advertising claims commonly perceive that RNC cigarettes confer lower cancer relative risk compared with conventional cigarettes (Johnson et al., 2019a; Johnson, Mercincavage, et al., 2021; Lochbuehler et al., 2016; Mercincavage et al., 2016, 2017; Strasser et al., 2008). This is not surprising since tobacco advertising is typically the first introduction to a product’s branding, appeal, and harm (National Cancer Institute, n.d.). Abundant evidence supports that tobacco advertising, including misleading claims, leads to tobacco use among young people (National Cancer Institute, n.d.; U.S. Department of Health and Human Services, 2012, 2014). VLN® products are authorized to be marketed with reduced exposure claims (e.g., 95% less nicotine) (FDA, 2021b, 2022b). It is important to study if advertising exposure increases misperceptions about the product, are associated with intentions to use among nonsmokers, or quitting intentions among smokers.

One approach to address misperceptions and accurately convey risks is to use product labeling and warnings (National Cancer Institute, n.d.; U.S. Department of Health and Human Services, 2014). Text-only warnings have been shown to be minimally effective (Green et al., 2019; Hammond, 2011). Therefore, pictorial warning labels (PWLs) are a recommended prevention strategy (Noar et al., 2016; Thrasher et al., 2021). The FDA has the authority to require PWLs for all cigarette products and they proposed a rule for conventional cigarettes that may be effective 2023 if they decide to proceed (FDA, 2020; FDA, 2022). Outlined in 2020, the rule includes required warnings depicting some of the “lesser-known” health risks of smoking (FDA, 2021a). PWLs have been delayed in part because greater evidence was needed by the courts to justify their graphic nature when communicating “well-known” risks originally proposed by the FDA in a rule from 2011 (Public Health Law Center, n.d.). If the FDA pursues PWLs for all combustible cigarettes (i.e., including VLNs authorized as MRTPs), it is unclear what type of warning is most suitable. It may be that communicating well-known risks, portrayed by PWLs in the 2011 rule, is optimal to capitalize on existing heuristics of smoking harm (FDA, 2011). Alternatively, communicating lesser-known risks, portrayed by PWLs in the 2020 rule, could garner more attention and thinking about risks (FDA, 2022). Research should also include both smokers and nonsmokers as the public health standard for FDA regulations of tobacco products requires considering possible effects to the population as a whole for tobacco users (e.g., cessation) and nonusers (e.g., uptake) (FDA, 2023).

A review of social media tobacco brand profiles indicated only 36% of tobacco products on Instagram had a legally mandated text-only warning displayed (O’Brien et al., 2020). Social media platforms such as Instagram are commonly used among young adults (Auxier & Anderson, 2021; Smith & Anderson, 2018) and are a source of exposure to tobacco promotion (Clendennen et al., 2020; Donaldson et al., 2017; Lee et al., 2021). Social media exposes young adults to an informational environment that predominately communicates a protobacco narrative coupled with social reinforcement (e.g., likes) and appealing imagery (Bandura, 2018; Fishbein & Ajzen, 2010; Flay, 1999). A FDA order authorizes VLN® products to be marketed on social media (FDA, 2022b). Pictorial warnings have shown to garner more attention than text-only warnings for cigarette products broadly (Francis et al., 2019; Hammond, 2011; Noar et al., 2016), but the optimal content of the warnings for VLNs is unclear. Communicating well-known risks when encountering a novel product may be most effective to anchor a reference point or prime mental shortcuts (Chaiken, 1980; Petty & Brinol, 2014; Tversky & Kahneman, 1981). This is theorized to expedite message processing using tools such as increased congruency of stimuli content (Lochbuehler et al., 2017, 2019) or leveraging existing beliefs about smoking (Bohner & Dickel, 2011; Crano & Prislin, 2008; Fishbein & Ajzen, 2010; Slovic, 1987). This may be particularly salient in a social media context characterized by a high volume of content with increased demands on attention. Communicating lesser-known risks when encountering a novel product could be deemed unique and garner attention to encourage central processing (Petty & Brinol, 2014). Although it could lead to greater ambiguity, leaving individuals without a clear perception of the product and potentially more easily persuaded by advertisement content.

The tobacco industry has a history of using marketing to showcase new products as lower risk (e.g., “light” cigarettes) (FDA, 2018a). It is important to understand a text-only warning on a new product such as VLNs relative to conventional cigarettes in light of pictorial warnings to disentangle marketing, labeling, and product interactions. This study seeks to understand how young adult cigarette smokers and nonsmokers perceive VLN advertising relative to conventional cigarette advertising depending on various risk communication (e.g., warning labels) or contextual (e.g., social media) features.

METHODS

Procedures and Sample

We recruited U.S. young adult current cigarette smokers and nonsmokers for an experiment using the online crowdsourcing platform Amazon Mechanical Turk (MTurk) from November 2021 to February 2022 (Jeong et al., 2019; Kraemer et al., 2017). Mturk users reviewed a brief description of the study. If interested, individuals proceeded to complete self-report screening questions determining their eligibility. Eligible participants were aged 18–30 years, registered on Mturk with a unique WorkerID verified in the US, and a current cigarette smoker or nonsmoker. Eligible participants completed all procedures online starting with informed consent followed by a survey. We examined reactions to advertisements using a discrete choice experiment (DCE).

Currently there is no standard for determining the minimum sample size for discrete choice experiments. We estimated that a sample between 1,500–2,000 participants would be suitable based on previous work (Mays et al., 2022). One approach recommends a “rule-of-thumb” calculation which indicates our study sample met minimum sample size requirements (Orme, 2010). Participants who met eligibility, consent, and data quality procedures were approved for payment on an ongoing basis. We closed data collection when incoming responses slowed and we reached our target range. Participants completed all procedures online starting with informed consent followed by a survey hosted by Qualtrics and paying $2 for a completed survey (around 10 minutes), equating to $12 per hour. All procedures were approved as exempt by the host institution’s Institutional Review Board.

After completing measures assessing eligibility, demographics, and other tobacco use, participants completed the DCE task (Regmi et al., 2018). The experimental task tested a 2×2×3 mixed design testing: brand (VLN, Marlboro), context (1. Advertisement exposure only or 2. Advertisement exposure simulated on social media) and warning (1. Text-only warning, 2. Well-known risk pictorial warning, or 3. Lesser-known risk pictorial warning). Participants were first stratified by either VLN or Marlboro brand (between-subjects) and then randomly shown 15 sets of stimuli with varying context and warning attributes (within-subjects). Participants reviewed two ads and indicated their choices (e.g., to buy) for each set. The order of the ads was randomized. A sample choice set and the full list of choice set conditions are shown in the Supplemental Materials.

VLN stimuli were drawn from the 22nd Century Group’s MRTP application materials to the FDA and the Marlboro stimuli were drawn from the Trinkets and Trash database (Rutgers Center for Tobacco Studies, 2022). The VLN ad showed VLN® King and VLN® Menthol King packages. The Marlboro ad showed Regular and Menthol Special Blend packages. Stimuli were edited using Photoshop software. The social media components were designed with branding, likes, and a tagline consistent across stimuli (Johnson & Mays, 2020). The text-only warning we used was, “WARNING: This product contains nicotine. Nicotine is an addictive chemical.” The pictorial warnings included an image of diseased lungs with text, “Cigarettes cause fatal lung disease” (well-known) or an image of head and neck cancer with text, “Smoking causes head and neck cancer” (lesser-known) (Noar et al., 2016; Pepper et al., 2020). Warnings covered the top 20% of the advertisement (FDA, 2020). Stimuli are shown in the Supplemental Materials.

Measures

Demographics.

Demographics included age, sex, race, ethnicity, education and income (Chowdhury et al., 2010). Instagram use frequency was collected where 1=Not at all, 2=Less often, 3=About once a day, and 4=Several times a day. Greater values indicate greater use frequency on average (Auxier & Anderson, 2021; Johnson & Mays, 2020).

Tobacco Use.

Cigarette and other tobacco use measures were collected (Mays et al., 2021). Current cigarette smokers were eligible if they reported lifetime use of at least 100 cigarettes and smoked currently (some days or every day). Nonsmokers were eligible if they reported not having smoked at least 100 cigarettes in their lifetime (NIDA & FDA, n.d.). Smokers indicated which cigarette brand they use most often in the past 30 days (Centers for Disease Control and Prevention, 2021). Other tobacco product use was measured among all participants by past 30 day use of electronic cigarettes, heated tobacco products, smokeless tobacco, cigarillos, traditional cigars, and hookah tobacco (Mays et al., 2021).

DCE Outcomes.

Participants were asked five questions: 1. Which ad attracts your attention? (attention) 2. Which ad is most appealing to you? (appeal) 3. Which ad contains cigarettes that are the most harmful to your health? (harm) 4. Which product would you be most likely to buy? (buy), and for smokers only, 5. Which ad makes you want to quit smoking? (quit) (Johnson, Luta, et al., 2021).

Statistical Analysis

Among validated responses that completed a Captcha verification, we screened 4,335 individuals for eligibility, 2,686 participants (61%) were ineligible due to age (n=1,210, 45%), smoking status (n=601, 22%), or both (n=789, 29%). Data quality procedures (Auer et al., 2021; Heffner et al., 2021) were implemented as done in previous work (Johnson et al., 2019b; Johnson & Mays, 2020; Mays et al., 2016, 2022) by including a Captcha verification, prohibited duplicate responses and we further removed automated responses (n=41, 2.5% consented) flagged by Qualtrics features (Qualtrics XM, 2022), leaving an analytic sample of N=1,608. We reviewed descriptive statistics for the sample and bivariate associations between smokers and nonsmokers. The groups differed on characteristics shown in Table 1, therefore we examined them separately.

Table 1.

Sample Characteristics

Full Sample (N=1608) Smokers (n=900) Nonsmokers (n=708) p value
Age 27.1 (2.6) 27.2 (2.5) 26.9 (2.8) 0.07
Sex 0.37
 Female 547 (34.0%) 315 (35.0%) 232 (32.9%)
 Male 1059 (66.0%) 585 (65.0%) 474 (67.1%)
Race <.001
 Asian 43 (2.7%) 16 (1.8%) 27 (3.8%)
 American Indian/Alaska Native 15 (0.9%) 8 (0.9%) 7 (1.0%)
 Black or African American 206 (13.0%) 91 (10.1%) 115 (16.2%)
 Native Hawaiian/Pacific Islander 2 (0.1%) 0 (0.0%) 2 (0.3%)
 White 1364 (82.6%) 781 (86.8%) 546 (77.1%)
 More than one race 12 (0.8%) 2 (0.2%) 10 (1.4%)
 Other race 3 (0.2%) 2 (0.2%) 1 (0.1%)
Ethnicity 0.16
 Hispanic 419 (26.1%) 224 (25.0%) 195 (28.1%)
 Non-Hispanic 1171 (72.8%) 672 (75.0%) 499 (71.9%)
Education 0.21
 Less than high school 3 (0.2%) 2 (0.2%) 1 (0.1%)
 Some high school 5 (0.3%) 3 (0.3%) 2 (0.3%)
 High school graduate or GED 29 (1.8%) 12 (1.3%) 17 (2.4%)
 Some college 170 (10.6%) 93 (10.3%) 77 (10.9%)
 College degree 903 (56.2%) 491 (54.6%) 412 (58.3%)
 Graduate degree 496 (30.9%) 298 (33.2%) 198 (28.0%)
Income 0.05
 Less than $20,000 77 (5.0%) 37 (4.1%) 40 (5.7%)
 $20,000 - $35,000 259 (16.1%) 136 (15.2%) 123 (17.4%)
 $35,001 - $50,000 522 (32.5%) 304 (33.9%) 218 (30.8%)
 $50,001 - $75,000 555 (34.5%) 326 (36.3%) 229 (32.4%)
 Greater than $75,000 191 (11.9%) 94 (10.5%) 97 (13.7%)
Lifetime Cigarette Use <.001
 Yes 900 (56.0%) 900 (100%) 0 (0.0%)
 No 708 (44.0%) 0 (0.0%) 708 (94.2%)
Current Cigarette Use
 Every Day 668 (40.4%) 668 (74.2%) 0 (0.0%)
 Some Days 232 (14.0%) 232 (25.8%) 0 (0.0%)
Past 30 Day Other Tobacco Use
 Electronic Cigarettes 761 (47.5%) 581 (64.8%) 180 (25.5%) <.001
 Heated/IQOS 646 (40.2%) 487 (54.1%) 159 (22.5%) <.001
 Smokeless Tobacco 622 (38.9%) 477 (53.4%) 145 (20.6%) <.001
 Cigarillo 822 (51.3%) 641 (71.5%) 181 (25.6%) <.001
 Traditional Cigars 808 (50.5%) 630 (70.3%) 178 (25.3%) <.001
 Waterpipe/Hookah 811 (50.8%) 638 (71.6%) 173 (24.5%) <.001
Instagram Use Frequency 3.1 (0.9) 3.2 (0.9) 3.1 (1.0) 0.07
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Note: Sporadic missing data <5%. Instagram Use Frequency range 1=Not at all, 2=Less often, 3=About once a day, and 4=Several times a day. Values were averaged so greater values indicate greater use frequency.

In DCE analyses, we modeled the experimental design using within-subjects multivariable logistic regression models with generalized estimating equations and an exchangeable working correlation structure (Johnson, Luta, et al., 2021; Mays et al., 2022; Regmi et al., 2018). Modeling included the following reference groups: context (advertisement exposure only), warning (text-only), and brand (Marlboro). Design-related covariates included the order of choice sets and ad choices. Demographic covariates included: age, sex, race, education, Instagram use, and brand of cigarettes used most often.

We report global Chi-Square score statistics for Type 3 GEE analysis parameters. For statistically significant parameters we report exponentiated least squared means output with a Tukey-Kramer adjustment for multiple testing using Odds Ratios (OR) with 95% confidence intervals (CI) with less focus on main effects where there were significant interactions. All analyses used SAS version 9.4.

RESULTS

Sample Characteristics

The sample included N=1,608 participants with n=900 current cigarette smokers and n=708 nonsmokers (Table 1). Overall, individuals had an average age of 27.1 (SD=2.6) and were majority male (n=1059, 66%), white race (n=1364, 82.6%), non-Hispanic (n=1171, 72.8%), with above average education and income levels. Other tobacco product use included cigarillos (51.3%) and cigars (50.5%), hookah (50.8%), and electronic cigarettes (47.5%). The sample had a mean value of 3.1 (SD=0.9) for Instagram use frequency, indicating most participants use Instagram about once a day. Current smokers were more likely to be white, less likely to have income greater than $75,000, and more likely to have greater past 30 day use of all other tobacco products (p’s <.05). Roughly a third of smokers (n=336, 37%) indicated Marlboro was the cigarette brand they use most often.

DCE Outcomes

DCE outcomes of attention, appeal, harm, buy, and quit are grouped by smoker (Table 2) and nonsmoker (Table 3) status. Interaction effects are shown in Figures 1, 2, and 3.

Table 2.

DCE Outcomes (Smokers)

Attention Appeal Harm Buy Quit
Main Effects χ2 p χ2 p χ2 p χ2 p χ2 p
Context 25.57 <.001 9.16 0.003 0.11 0.74 1.37 0.24 0.00 0.99
Warning 7.65 0.02 20.90 <.001 49.43 <.001 30.61 <.001 47.55 <.001
Brand 0.02 0.89 1.52 0.22 0.01 0.90 0.88 0.35 5.00 0.03
Interactions
Context × Warning 0.81 0.67 8.00 0.02 2.28 0.32 1.63 0.44 0.64 0.73
Context × Brand 0.13 0.72 0.16 0.69 0.04 0.84 0.79 0.38 0.08 0.78
Warning × Brand 7.70 0.02 1.80 0.41 0.45 0.80 0.95 0.62 0.77 0.68
Context × Warning × Brand 0.32 0.85 0.89 0.64 0.00 1.00 0.34 0.84 2.27 0.32
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Note: Bold indicates statistically significant p<.05. Covariates include: age, sex, race, education, Instagram use, brand of cigarettes used most often, the order of DCE choice sets, and the order of the DCE ad choices within each set.

Table 3.

DCE Outcomes (Nonsmokers)

Attention Appeal Harm Buy
Main Effects χ2 p χ2 p χ2 p χ2 p
Context 54.80 <.001 16.28 <.001 0.05 0.48 19.74 <.001
Warning 27.05 <.001 24.71 <.001 125.34 <.001 83.58 <.001
Brand 0.24 0.62 2.31 0.13 0.01 0.91 0.28 0.59
Interactions
Context × Warning 8.77 0.01 2.59 0.27 0.90 0.64 1.90 0.39
Context × Brand 1.04 0.31 0.07 0.79 0.01 0.93 0.79 0.37
Warning × Brand 8.98 0.01 3.79 0.15 1.72 0.42 5.10 0.08
Context × Warning × Brand 2.35 0.31 4.13 0.13 0.43 0.81 6.36 0.04
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Note: Bold indicates statistically significant p<.05. Covariates include: age, sex, race, education, Instagram use, the order of DCE choice sets, and the order of the DCE ad choices within each set.

Figure 1.

Figure 1.

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Attention Interaction Effects

Figure 2.

Figure 2.

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Appeal Interaction Effects

Figure 3.

Figure 3.

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Buy Interaction Effects

Attention

In both smokers (Table 2) and nonsmokers (Table 3), there were main effects of Context and Warning on attention, but no effect of Brand.

There was a significant Warning × Brand interaction for attention among smokers (χ2=7.70, p=0.02, Figure 1a). Smokers had higher odds of attention for the VLN ad with a well-known warning (OR=1.11, 95% CI=1.03, 1.20) and lower odds of attention for the VLN ad with a text-only warning (OR=0.89, 95% CI=0.82, 0.96). In comparison, attention for those viewing the Marlboro ad did not differ by warning type. All other interactions were non-significant.

There was a significant Warning × Brand interaction for attention among nonsmokers (χ2=8.98, p=0.01, Figure 1b). Nonsmokers had higher odds of attention for the VLN ad with a lesser-known warning (OR=1.17, 95% CI=1.09, 1.26) and the Marlboro ad with a well-known warning (OR=1.16, 95% CI=1.06, 1.26). Nonsmokers had lower odds of attention for those viewing the VLN (OR=0.82, 95% CI=0.75, 0.91) or the Marlboro (OR=0.85, 95% CI=0.78, 0.94) ad with a text-only warning. There was a significant Context × Warning interaction for attention among nonsmokers (χ2=8.77, p=0.01, Figure 1c). Nonsmokers had higher odds of attention for ads on social media with lesser-known (OR=1.24, 95% CI=1.15, 1.34) and well-known (OR=1.21, 95% CI=1.11, 1.31) warnings. Nonsmokers had lower odds of attention for the ad-only condition with a text-only warning (OR=0.69, 95% CI=0.63, 0.76). All other interactions were non-significant.

Appeal

In both smokers (Table 2) and nonsmokers (Table 3), there were main effects of Context and Warning on appeal, but no effect of Brand.

There was a significant Context × Warning interaction for appeal among smokers (χ2=8.00, p=0.02, Figure 2). Smokers had higher odds of appeal for ads on social media with lesser-known (OR=1.10, 95% CI=1.03, 1.17) and text-only (OR=1.17, 95% CI=1.09, 1.25) warnings. Smokers had lower odds of appeal for a well-known warning both with (OR=0.90, 95% CI=0.84, 0.96) and without (OR=0.91, 95% CI=0.85, 0.97) social media context. All other interactions were non-significant.

There were significant Context (χ2=16.28, p<.001) and Warning (χ2=24.71, p<.001) main effects for appeal among nonsmokers. Nonsmokers had higher odds of appeal for ads on social media (OR=1.07, 95% CI=1.03, 1.11) and a text-only warning (OR=1.15, 95% CI=1.08, 1.22). Nonsmokers had lower odds of appeal for ads with a well-known warning (OR=0.86, 95% CI=0.81, 0.92). The odds of appeal for ads with a lesser-known warning was not statistically significant (OR=0.99, 95% CI=0.95, 1.04).

Harm

In both smokers (Table 2) and nonsmokers (Table 3), there was a main effect of Warning on harm, but not Context or Brand.

There was a significant Warning main effect for harm among smokers (χ2=49.43, p=<.001). There were higher odds of harm for ads with lesser-known (OR=1.05, 95% CI=1.01, 1.10) and well-known (OR=1.13, 95% CI=1.08, 1.18) warnings among smokers. Conversely, there were lower odds of harm for ads with a text-only warning (OR=0.84, 95% CI=0.80, 0.88).

A parallel pattern emerged for nonsmokers where there was a Warning main effect for harm (χ2=125.34, p=<.001). There were higher odds of harm for ads with a well-known warning (OR=1.37, 95% CI=1.29, 1.45) and lower odds of harm for ads with a text-only warning (OR=0.71, 95% CI=0.67, 0.76). The lesser-known warning estimate was positive, though not statistically significant (OR=1.05, 95% CI=1.00, 1.10).

Buy

There were main effects of Warning on buying for smokers (Table 2) and nonsmokers (Table 3). In nonsmokers, there was a main effect of Context. There was no main effect of Brand for buying in either group.

There was a significant Warning main effect for buying among smokers (χ2=30.61, p=<.001). There were higher odds of buying for ads with a text-only warning (OR=1.09, 95% CI=1.04, 1.15) and lower odds with a well-known warning (OR=0.88, 95% CI=0.84, 0.92). The odds of buying for ads with a lesser-known warning were not statistically significant (OR=1.03, 95% CI=0.98, 1.07).

There was a significant Context × Warning interaction between those that viewed VLN and Marlboro brands for buying among nonsmokers (χ2=6.36, p=0.04, Figure 3). Nonsmokers had lower odds of buying for ads with a well-known warning across brands, with and without a social media context (Social Media Ad: VLN OR=0.86, 95% CI=0.78, 0.96; Marlboro OR=0.83, 95% CI=0.75, 0.93; Ad-Only: VLN OR=0.74, 95% CI=0.67, 0.82; Marlboro OR=0.73, 95% CI=0.65, 0.82). There were higher odds of buying for Marlboro ads with a text-only warning, both with and without a social media context (Social Media Ad: OR=1.48, 95% CI=1.31, 1.67; Ad-Only: OR=1.36 95% CI=1.21, 1.53). There were higher odds of buying for those viewing the VLN ads with a text-only warning and social media context (OR=1.50, 95% CI=1.33, 1.68). Lastly, there were lower odds of buying for those viewing the Marlboro ads with a lesser-known warning without a social media context (OR=0.81, 95% CI=0.74, 0.88). All other interactions were non-significant.

Quit Intentions

There were significant Warning (χ2=47.55, p=<.001) and Brand (χ2=5.00, p=0.03) main effects for quit intentions among smokers (Table 2). There were higher odds of quit intentions for ads with a well-known warning (OR=1.15, 95% CI=1.10, 1.21) and lower odds of quit intentions with a text-only warning (OR=0.85, 95% CI=0.81, 0.89). The odds with a lesser-known warning were positive, though not statistically significant (OR=1.04, 95% CI=1.00, 1.09). There were higher odds of quit intentions for those viewing ads with the VLN brand (OR=1.02, 95% CI=1.01, 1.03).

Discussion

The current study examined reactions to RNC advertising (VLN) compared to conventional cigarette advertising (Marlboro) considering warning labels and context among young adults. Experimental results from an online sample indicate differential effects of context, warning, and brand. Presenting an ad in a social media context resulted in greater appeal and attention in both smokers and nonsmokers and increased intention to buy VLN ads with a text-only warning in nonsmokers. For both groups, well-known pictorial warnings largely outperformed the text-only warning to increase attention and harm perceptions, reduce appeal and buying intentions, and increase quit intentions (smokers only). The VLN ad with a text-only warning garnered the least attention, though each brands’ marketing was deemed appealing. Nonsmokers were most sensitive to a social media context and text-only warnings increased intentions to buy cigarettes.

Results indicate that a pictorial warning was more effective compared to a text-only warning. This aligns with the literature on PWL effectiveness (Cho, Thrasher, Swayampakala, et al., 2018; Cho, Thrasher, Yong, et al., 2018; Hammond, 2011; Hammond et al., 2019; Levy et al., 2017; Noar et al., 2016). Yet, the well-known warning was most consistent across outcomes and outperformed the lesser-known warning when paired with the VLN brand to garner attention among smokers and reduce buying intentions among nonsmokers. Research has shown variation in PWL labels’ potency and impact (Magnan et al., 2020; Pepper et al., 2020). Work should explore if a variety of PWLs have similar patterns. Unique to this study, PWL warnings are effective alongside social media features for a novel product after a brief exposure. Importantly, text-only warnings showed lower odds of attention and higher odds of buying cigarettes. Attention and appeal patterns were not consistent, indicating a need to understand how visual attention to specific features can impact outcomes. Visual attention methodologies with high precision (e.g., eye-tracking) may better capture attentional bias effects (Strasser et al., 2012).

Smokers had greater odds of quit intentions with the VLN ad exposure indicating promising potential for cessation-related opportunities. This should be weighed against the effects seen among nonsmokers where a text-only warning was largely ineffective to deter this group from intending to buy VLN cigarettes on social media. This is especially relevant provided the impact of a social media context in the current study given its brief exposure with subtle differences (present or absent). Social media use is ubiquitous and its marketing has been associated with use of novel tobacco products in young people (Clendennen et al., 2020; Donaldson et al., 2017; Lee et al., 2021). Further testing of additional warnings in relation to behavioral outcomes can provide greater evidence of these effects. Furthermore, qualitative work of warnings in relation to information processing could provide greater insights on how individuals engage with the risk messaging. This could help understand if the text-only warning about nicotine was potentially confusing in relation to the VLN advertisement relaying 95% reduced nicotine or if it simply did not capture attention without pictorial imagery. It could also help tease out whether the lesser-known warning was believable or left individuals with greater ambiguity and more easily persuaded by the tobacco ad.

There are limitations to this work. The sample consisted of a well-educated, racially homogenous sample in the US limiting its external validity. Vulnerable populations are important to protect regardless of the recruitment method and there are unique concerns to consider when drawing samples from crowdsourcing sites such as MTurk. While offering convenience and rapid data collection, it is not without its pitfalls and additional work is needed to better understand the circumstances of the respondents to ensure maximum protections (Chmielewski & Kucker, 2020). Participants viewed one brand to minimize burden and although conventional cigarette brands are prohibited from paid marketing on social media due to its appeal (FDA, 2022b), the experiment showed VLN advertising had comparable levels of appeal. Nonsmokers did not have the ability to opt-out of choices. This limits the available inferences for nonsmokers. However, null findings would suggest little impact of the design features among nonsmokers and this was not the case. Nonsmokers are inherently less likely to see tobacco advertising on social media or buy RNCs, yet it is largely unclear how VLN advertising will be circulated, how underlying social media algorithms may play a role in exposure, or how comparisons with other products may encourage uptake. Further replication of this work with such sample and design considerations are warranted. Eligibility was not restricted to those with an Instagram account and stimuli exposure was simulated. Warnings included both harm and addiction themes to provide practical significance of aligning with the FDA’s approved warnings.

Conclusions

This study provides a first step in understanding how young adults react to RNC advertising. Results indicate there are differences in how smokers and nonsmokers react to cigarette ads on and off social media, especially with consideration of the warnings they portray. Additional testing of various types of warning labels and contexts is important for the advancement of public health globally. This includes consideration for how policies interact, including the introduction of a new tobacco product accompanied by warning labels or campaigns (Villanti, Byron, et al., 2019; Villanti, West, et al., 2019). Results suggest ads on social media may not be the same as conventional advertising. Future research should focus on parallels and differences for marketing of novel tobacco products in a social media environment.

Supplementary Material

Supplementary Materials

Funding

Research reported in this publication was supported by the National Cancer Institute (NCI) of the National Institutes of Health (NIH) and the U.S. Food and Drug Administration (FDA) Center for Tobacco Products under Award Number U54CA229973 (all authors), the National Institute on Drug Abuse (NIDA) Award Number R01DA051001 (Strasser and Villanti), and by NCI Award Number K07CA218366 (Mercincavage). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIDA, or the FDA.

Footnotes

Conflicts of interest/Competing interests

The authors have no conflicts of interest to declare.

Ethics Approval

The study complies with ethical standards for the protection of human subjects. All study procedures were approved by the University of Pennsylvania Institutional Review Board.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Code availability

Custom code available upon request.

Availability of data and material

Data and materials available upon request.

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Supplementary Materials

Supplementary Materials
NIHMS1932451-supplement-Supplementary_Materials.docx (2.5MB, docx)

Data Availability Statement

Data and materials available upon request.

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