E-Cigarette Marketing On Social Networking Sites
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* Joe Phua

## Effects on Attitudes, Behavioral Control, Intention to Quit, and Self-Efficacy

## ABSTRACT

This study examined exposure to three types of e-cigarette marketing—sponsored advertisements, brand pages, and user-created groups—on social networking sites and their influence on health-related outcomes. Results (*N* = 1,016) indicated that e-cigarette users who joined user-created groups had significantly more negative attitudes toward quitting and lower behavioral control, intention to quit, and self-efficacy than those who were exposed to sponsored advertisements or who followed brand pages. Exposure to two or more types of marketing had an additive effect on health-related outcomes. Social identification, attention to social comparison, and subjective norms also moderated between exposure to e-cigarette marketing and key dependent measures.

## MANAGEMENT SLANT

*   E-cigarette users who were in user-created groups had significantly more negative health-related outcomes than those who saw advertisements or followed brand pages.

*   Exposure to three types of e-cigarette marketing had a significant additive effect on health-related outcomes, compared with exposure to two or fewer types.

*   Social identification, attention to social comparison, and e-cigarette subjective norms moderated between exposure to e-cigarette marketing and health-related outcomes.

*   The results of this study also might apply to advertising of other health-related products on social networking sites, including over-the-counter pharmaceuticals and prescription drugs (*e.g.* opioids).

## INTRODUCTION

E-cigarette use is an increasingly serious health issue. The Centers for Disease Control and Prevention found that 3.5 percent of U.S. adults were current e-cigarette users in 2015, and among e-cigarette users overall, 58.8 percent were also cigarette smokers (Centers for Disease Control and Prevention, 2016). The health implications of e-cigarettes have ignited debate between harm-reduction and abstinence-only public-health professionals (Berry, Burton, and Howlett, 2017). Harm-reduction advocates cite research evidence finding e-cigarettes to be efficacious cessation aids in a step-down approach from conventional tobacco (Benowitz, Donny, and Hatsukami, 2017), but abstinence-only advocates argue that e-cigarettes, like conventional tobacco, contain cancer-causing toxins and pollutants and therefore are not safer tobacco substitutes (Huerta, Walker, Mullen, Johnson, and Ford, 2017). Researchers also found that e-cigarette use significantly predicted future conventional tobacco uptake (McCabe, Veliz, McCabe, and Boyd, 2017). Effective August 8, 2016, the U.S. Food and Drug Administration began regulating e-cigarettes, requiring health warnings on packages, banning free samples and vending-machine sales, and restricting sales to those 18 years and older (U.S. Food and Drug Administration, 2016). No regulations were announced for e-cigarette marketing, however, and, as such, e-cigarette brands continue to use social networking sites to advertise to consumers.

According to a 2016 U.S. Surgeon General's report, use of social networking sites by e-cigarette brands for marketing is increasingly prevalent, because of the ability to reach teenagers and young adults, who are most susceptible to peer and media influence (U.S. Department of Health and Human Services, 2016). In a 2016 special report on social media success metrics, the American Marketing Association (AMA) outlined three prominent types of social network-based marketing: 

*   sponsored advertisements (paid media posts advertising a brand's products);

*   brand pages (owned media posts, which let a company craft a consistent brand message, allowing users to like, follow, and comment);

*   user-created groups (earned media in which users engage in electronic word of mouth and pass along user-generated content; AMA, 2016).

The current research therefore is important, because e-cigarette marketers mainly use these three types of social networking site-based marketing to engage their target audiences (*i.e.*, sponsored advertisements, brand pages, and user-created groups). To date, no prior studies have examined whether exposure to these three types of marketing, on their own or in combination with one another, can have an impact on e-cigarette attitudes and behavioral intentions.

To address this research gap, in the current investigation the author applied the elaboration likelihood model (Petty, Cacioppo, and Schumann, 1983), the theory of planned behavior (Ajzen, 1991), and online information-seeking strategies (Ramirez, Walther, Burgoon, and Sunnafrank, 2002) to assess whether consumers' exposure to these three types of social networking site-based e-cigarette marketing would exert a significant and additive effect on attitudes and behavioral intentions toward e-cigarette use. The elaboration likelihood model and the theory of planned behavior were chosen as relevant theoretical frames for this study because of their applications to advertising-message processing and health-behavioral change, respectively.

The popularity of social networking sites in the United States increasingly has led marketers to use these sites to engage their target customers. According to a 2017 Pew Research Center report, 69 percent of all American adults 18 and older use social networking sites, with the highest usage among those 18–29 years old (86 percent) and 30–49 years old (80 percent; Pew Research Center, 2017). The most popular social networking sites include Facebook (68 percent), Instagram (28 percent), Pinterest (26 percent), LinkedIn (25 percent), and Twitter (21 percent; Pew Research Center, 2017).

Social networking sites allow marketers to purchase sponsored advertisements—paid media that users see on their news feeds. Consumer interaction with social networking site-based advertisements has been found to influence brand preferences (Gensler, Völckner, Liu-Thompkins, and Wiertz, 2013). Marketers also use social networking site brand pages (owned media) to engage consumers through brand-related posts. These posts facilitate liking, sharing, and commenting on messages, which is earned media (Taylor, Lewin, and Strutton, 2011). Members of user-created brand communities also share information and experiences based on consumption of particular brands, often in the form of user-generated content and electronic word of mouth. This is also earned media, and it results in strong brand loyalty (Kim, Sung, and Kang, 2014).

The efficacy of social networking site-based brand promotion has led e-cigarette brands to expand such marketing efforts (Richardson, Ganz, and Vallone, 2015). As of March 2017, because of a lack of U.S. Food and Drug Administration guidelines, social networking sites self-regulate e-cigarette-related content: 

*   Instagram maintains lists of banned hashtags, but none of these refer to tobacco-related content.

*   Facebook prohibits promotion of tobacco-related products, except for blogs and groups affiliated with tobacco products, which are permitted if their activity does not lead directly to tobacco sales.

*   Twitter prohibits tobacco and e-cigarette promotion but allows news and information about tobacco products.

Because marketers use three major types of social networking site-based marketing to engage their target audiences—sponsored advertisements, brand pages, and user-created groups (AMA, 2016)—it therefore is important to assess empirically each type's effect on attitudes and behavioral intentions toward e-cigarettes and e-cigarette brands.

## LITERATURE REVIEW

The elaboration likelihood model (Petty et al., 1983), a frequently used theoretical framework in traditional advertising research, proposes two major routes by which attitude change occurs. The central route requires individuals to process messages cognitively, which leads to high message elaboration. The peripheral route involves individuals processing messages through inferential cues in advertisements, which results in less enduring attitude change.

In research applying the elaboration likelihood model to social networking site-based marketing, advertising cues such as brand-post popularity (Chang, Yu, and Lu, 2015), celebrity-spokesperson characteristics (Jin and Phua, 2014), and message personalization (De Keyzer, Dens, and De Pelsmacker, 2015) were found to influence brand-related outcomes. Studies also found online-advertisement avoidance to be a major factor inhibiting user engagement with digital advertisements (Baek and Morimoto, 2012; Edwards, Li, and Lee, 2002; Fransen, Verlegh, Kirmani, and Smit, 2015; Yeu, Yoon, Taylor, and Lee, 2013). In research on Internet-based antitobacco advertising, persuasive cues in online advertisements also strongly influenced attitudes and behavioral intentions toward smoking (Pechmann, Delucchi, Lakon, and Prochaska, 2015; Vallone et al., 2016).

An additional study tested the elaboration likelihood model's relevance in the online environment by conducting a replication of the original (Petty et al., 1983) study (Kerr, Schultz, Kitchen, Mulhern, and Beede, 2015). Despite some results diverging from the premise of the model, the second group concluded that their study did offer support for learning and persuasion through subconscious processing of advertising exposure in online environments. They called for advertising researchers to further explicate traditional advertising theories, such as the elaboration likelihood model, in online contexts such as social networking sites to better reflect online consumers.

On the basis of those recommendations (Kerr et al., 2015), the current study operationalized advertising-message processing on the basis of three levels of elaboration and agency: low, medium, and high. These three levels corresponded to the three types of social networking site-based e-cigarette brand marketing examined in this article: sponsored advertisements, brand pages, and user-created brand groups, respectively. When consumers are exposed only to social networking site-based sponsored advertisements, they passively process advertising messages, with a low level of elaboration and agency involved.

The next two types of e-cigarette marketing messages (e-cigarette brand pages and user-created groups) require greater message elaboration and agency. People following e-cigarette brand pages like, comment on, and share branded posts but might not participate actively in creating posts. They therefore have a medium level of elaboration and agency. Those who join user-created e-cigarette brand groups, in contrast, more likely will participate actively in community-based activities, including information sharing, user-generated content, and electronic word of mouth. These activities require a high level of message elaboration and agency.

This study proposed that these three types of e-cigarette marketing correspond to increasing elaboration of persuasive messages: low elaboration–low agency (sponsored advertisements), medium elaboration–medium agency (brand pages), and high elaboration–high agency (user-created groups). The study proposed that user-created groups (high elaboration–high agency) would have the most significant impact on attitude and behavioral intentions toward e-cigarettes, compared with sponsored advertisements (low elaboration–low agency) and brand pages (medium elaboration–medium agency).

Another relevant theoretical framework for the current investigation is online information-seeking strategies (Ramirez et al., 2002). On the basis of previous research on online communication in computer-mediated settings, the author proposed three strategies brand consumers use to extract social and brand information online: passive, active, and interactive. Consumers using passive strategies to acquire information from social networking sites merely observe persuasive messages, such as advertisements, from the sidelines without personal participation in creating or passing along the messages. In the current context, exposure to e-cigarette-related sponsored advertisements on social networking sites entails a passive information-seeking strategy, because consumers see advertisements on their news feeds without actually creating or passing along the advertisements.

Active information-seeking strategies involve indirect information gathering (*e.g.*, following conversations of others familiar with the topic at hand) and may include a low degree of interpersonal communication. Following e-cigarette brand pages and liking, commenting on, and sharing brand posts therefore constitutes an active information-seeking strategy, because these activities require greater consumer involvement in propagating brand-related messages (Araujo, Neijens, and Vliegenthart, 2015; Van Noort, Antheunis, and Velergh, 2014). Interactive information-seeking strategies involve direct communication between consumers and brands, whereby greater depth of discussion, self-disclosure, reciprocity, and alteration of behavior on the basis of feedback are possible. Joining user-created e-cigarette groups on social networking sites, sharing user-generated content, and propagating electronic word of mouth entail an interactive information-seeking strategy, because users become both consumers and creators of brand-related content (Levy and Gvili, 2015). Applying these online information-seeking strategies (Ramirez et al., 2002), the current author proposed that interactive (user-created group) information-seeking strategies would have the most significant impact on e-cigarette attitudes and behavioral intentions, compared with active strategies (brand pages) and passive strategies (sponsored advertisements).

A key variable in the theory of planned behavior is behavioral control, which refers to individuals' perceived ease or difficulty with performing particular health-related activities (Ajzen, 1991). Previous research found behavioral control to exert a strong influence on attitudes and intentions toward smoking (Namkoong, Nah, Record, and Vanstee, 2016). Self-efficacy is a tenet of social-cognitive theory that refers to the extent to which individuals believe they have the ability to accomplish particular tasks (Bandura, 2001). Prior research found participation in social networking site-based support groups to predict smoking cessation self-efficacy significantly (Phua, 2013).

Applying the elaboration likelihood model and online information-seeking strategies to social networking site-based brand communications, the current author proposed that consumers who joined user-created social networking site e-cigarette groups would have significantly more negative attitudes toward quitting e-cigarettes, lower behavioral control, lower intention to quit, and lower smoking cessation self-efficacy, compared with consumers who were exposed to sponsored advertisements or who followed brand pages. This prediction was based on the fact that user-created groups require the highest level of message elaboration and agency and are the most interactive online information-seeking strategy.

*   H1: E-cigarette users who are members of user-created e-cigarette brand groups on social networking sites will have significantly (a) more negative attitudes toward quitting e-cigarettes, (b) lower perceived behavioral control, (c) lower intention to quit, and (d) lower smoking cessation self-efficacy compared with those who saw sponsored advertisements or who follow e-cigarette brand pages on social networking sites.

In addition to exposure, online advertising frequency is another major factor influencing consumer brand engagement (Cheong, De Gregorio, and Kim, 2010; Schmidt and Eisend, 2015). Previous research found that when consumers are exposed multiple times to advertising, they have higher brand recall and purchase intention (Brettel, Reich, Gavilanes, and Flatten, 2015; Lee, Ahn, and Parks, 2015). Too many exposures, however, also leads to less effectiveness because of the advertising wear-out effect (Campbell and Keller, 2003). For familiar and well-liked brands that consumers already follow, the advertising wear-out effect can be delayed, because consumers continue to become more engaged even at higher frequencies of exposure (Schmidt and Eisend, 2015).

On social networking sites, consumers are exposed to sponsored advertisements on the basis of profile information they entered themselves. When they like brand pages or join user-created groups, they voluntarily allow themselves to be exposed to brand-related posts on their news feeds. This study therefore proposed that when e-cigarette users are exposed to two or more types of social networking site-based e-cigarette marketing, they more likely will continue to have positive brand evaluations, because of the high interactivity and high message elaboration and agency associated with following brand pages and joining user-created groups.

The author thus proposed that exposure to all three types of e-cigarette marketing—sponsored advertisements, brand pages, and user-created groups—would result in significantly more negative attitudes toward quitting e-cigarettes, lower behavioral control, lower intention to quit, and lower smoking cessation self-efficacy, compared with exposure to two or fewer types of e-cigarette marketing. Thus: 

*   H2: E-cigarette users who are exposed to all three types of e-cigarette marketing messages on social networking sites (*i.e.*, saw sponsored ads, joined e-cigarette brand pages, and joined user-created e-cigarette groups) will have significantly (a) more negative attitudes toward quitting e-cigarettes, (b) lower perceived behavioral control, (c) lower intention to quit, and (d) lower smoking-cessation self-efficacy compared with those who are exposed to two or fewer types of e-cigarette marketing messages.

“Social identity” refers to the part of a person's self-concept deriving from membership in social groups (Tajfel and Turner, 1986). When individuals' social identity is salient, they see themselves as interchangeable exemplars of the larger social group. This deindividuation effect results in socially identified individuals incorporating behavioral expectations of the group into the self, which guides ![Figure 1](http://www.journalofadvertisingresearch.com/https://www.journalofadvertisingresearch.com/content/jadvertres/59/2/242/F1.medium.gif)

[Figure 1](http://www.journalofadvertisingresearch.com/content/59/2/242/F1)

Figure 1 
Conceptual Model Illustrating Hypotheses Tested In the Study (*N* = 1,016)

 identity-relevant behaviors. Strong smoker identification has been found to lead greater resistance toward cessation messages and smoking escalation (

Hertel and Mermelstein, 2012).

“Attention to social comparison” refers to the extent to which individuals are dispositionally susceptible to reference-group influence (Lennox and Wolfe, 1984). Social comparison has been found to moderate significantly between perceived norms and health behavior (Novak and Crawford, 2001). “Subjective norms” (Ajzen, 1991) refers to individuals' perceptions of normative behavior in social groups, influenced by the judgment of others. In previous research, subjective norms have been found to affect tobacco uptake and maintenance significantly (Rise, Kovac, Kraft, and Moan, 2008).

On the basis of the findings of prior research, the author hypothesized that social identification as an e-cigarette user, attention to social comparison, and e-cigarette subjective norms would interact with exposure to social networking service-based e-cigarette marketing to affect attitude toward quitting e-behavioral control, intention to quit, and self-efficacy (See Figure 1).

*   H3: Social identification, attention to social comparison, and e-cigarette subjective norms will moderate the relationship between exposure to social networking site-based e-cigarette marketing messages and (a) attitude toward quitting e-cigarettes, (b) perceived behavioral control, (c) intention to quit, and (d) self-efficacy.

## METHODOLOGY

### Participants

A total of 1,016 participants took part in this study. Mean age was 41.6 years (*SD* = 13.43). (See Table 1 for participant demographics.)

### Procedure

Data for the study were collected with Qualtrics Panel, an online research-participant recruitment service. The service posted an online questionnaire to its participants to recruit a nationally representative sample from across the United States. Only participants who satisfied the two screening criteria—active social networking site users and current e-cigarette users—were recruited by Qualtrics Panel for the study. A total of 1,016 participants completed the online questionnaire and received e-points from Qualtrics as an incentive.

Participants were asked to identify the one social networking site that they most frequently used and answer all subsequent questions on the basis of their use of this particular site (See Table 2). The questionnaire also included an item asking participants whether they had seen e-cigarette-brand sponsored advertisements, followed brand pages, or been members of user-created groups within the past month.

### Measures

All measures in this study were drawn from previously used scales that have been validated empirically in published research. Attitude toward quitting e-cigarettes was assessed with four items (modified from Rise et al., 2008) on 7-point semantic-differential scales. Participants were asked whether quitting e-cigarettes in the next six months was “good,” “useful,” “pleasant,” or “comfortable” (Cronbach's α = .88).

Behavioral control toward quitting e-cigarettes was assessed with three items (modified from Rise et al., 2008) on 7-point Likert scales, ranging from “strongly disagree” to “strongly agree.” Items included “During the next six months, I can easily quit e-cigarettes if I want to” and “How much control do you have over quitting e-cigarettes during the next six months?” (Cronbach's α = .91). Intention to quit e-cigarettes was assessed with four items (modified from Rise et al., 2008) on 7-point Likert scales. Items included “During the next six months, I intend to quit smoking e-cigarettes” and “During the next six months, I will quit smoking e-cigarettes” (Cronbach's α = .98).

View this table:
[TABLE 1](http://www.journalofadvertisingresearch.com/content/59/2/242/T1)

TABLE 1 
Demographic Characteristics of Study Participants (*N* = 1,016)

Self-efficacy toward quitting e-cigarettes was assessed with a 19-item self-efficacy scale (Etter, Bergman, Humair, and Perneger, 2000). Questions asked whether participants could refrain from smoking e-cigarettes in different situations, including “when nervous” and “when angry” (Cronbach's α = .96). Social identification as an e-cigarette user was assessed with eight items (modified from Cameron, 2004) on 7-point Likert scales. Items included “I have a lot in common with e-cigarette users” and “I feel strong ties with e-cigarette users” (Cronbach's α = .85).

Attention to social comparison was assessed with 13 items (modified from Lennox and Wolfe, 1984) on 7-point Likert scales. Items included “I pay attention to others' reactions in order to avoid being out of place” and “It is important for me to fit into the group I'm with” (Cronbach's α = .92). Subjective norms toward e-cigarettes were assessed with six items (modified from Rise et al., 2008) on 7-point Likert scales. Items included “People who mean a lot to me think smoking e-cigarettes is unacceptable” and “Most of my close friends do not currently smoke e-cigarettes” (Cronbach's α = .94).

The author assessed convergent and discriminant validity by calculating the average variance extracted (AVE) for each measure in the study. On the basis of previous recommendations (Fornell and Larcker, 1981), convergent validity is established when AVE for each measure exceeds 0.50, whereas discriminant validity is established when AVE for each measure is greater than the squared correlation between each pair of measures in the study. AVE values obtained ranged from 0.61 to 0.75, thereby establishing convergent validity. Additionally, for each pair of measures in the study, the largest squared correlation was 0.36, whereas the lowest AVE previously obtained was 0.61, thereby establishing discriminant validity.

The author then assessed common method bias using Harman's single-factor test. All items on the measures were loaded into an exploratory factor analysis, with the number of factors extracted set at 1, and the unrotated factor solution was examined (Harman, 1976). Results indicated that a single factor did not account for a majority of variance in the measures, and when all items were loaded onto a single factor, only 25.23 percent of variance was accounted for. Common method bias therefore was not an issue.

## RESULTS

### Attitude toward Quitting, Behavioral Control, Intention to Quit, And Self-Efficacy

The author conducted a one-way multivariate analysis of variance to examine attitude toward quitting e-cigarettes, behavioral control, intention to quit, and self-efficacy, on the basis of exposure to social networking site-based e-cigarette marketing. Exposure categories were as follows (See Table 2): 

*   was not exposed;

*   saw sponsored advertisements;

*   followed brand pages;

*   followed user-created pages;

*   saw advertisements and followed brand pages;

*   saw advertisements and followed user-created pages;

*   followed brand pages and user-created pages;

*   was exposed to all three.

Results revealed a significant multivariate main effect by exposure (Wilks's λ=.560), *F*(18, 3625) = 157.91, *p* < .001 (partial η2 = .051, power = 1.00). Given the significance of the overall test, the author examined univariate analysis of variance results with *p* value set at <.0125 to control for Type I error. Significant univariate main effects by exposure to e-cigarette marketing were obtained for: 

*   attitude toward quitting, *F*(7, 1008) = 64.41, *p* < .001 (partial η2 = .082, power = 1.00);

*   behavioral control, *F*(7, 1008) = 38.78, *p* < .001 (partial η2 = .073, power = 1.00);

*   intention to quit, *F*(7, 1008) = 48.41, *p* < .001 (partial η2 = .077, power = 1.00);

*   self-efficacy to quit, *F*(7, 1008) = 65.38, *p* < .001 (partial η2 = .081, power = 1.00).

View this table:
[TABLE 2](http://www.journalofadvertisingresearch.com/content/59/2/242/T2)

TABLE 2 
Study Participants' (*N* = 1,016) Social Networking Site Use And Exposure to E-Cigarette Marketing

 Hypotheses 1 and 2 therefore were supported.

Levene's tests of equality of error variances were insignificant for all dependent measures, so the author used Scheffe post hoc tests to compare pairwise group means. Mean attitude toward quitting was highest for those not exposed to advertising (*M* = 5.18, *SD* = 0.77), followed by those who saw advertisements (*M* = 4.94, *SD* = 0.74), those who followed brand pages (*M* = 4.34, *SD* = 0.67), those who saw advertisements and followed brand pages (*M* = 4.02, *SD* = 1.07), those who joined user-created pages (*M* = 2.17, *SD* = 0.94), those who saw advertisements and joined user-created groups (*M* = 2.14, *SD* = 1.07), those who followed brand pages and joined user-created groups (*M* = 1.33, *SD* = 0.71), and those who were exposed to all three types of advertising (*M* = 1.31, *SD* = 0.46). Mean behavioral control was highest for those not exposed (*M* = 5.18, *SD* = 1.42), followed by those who saw advertisements (*M* = 4.63, *SD* = 1.36), those who followed brand pages (*M* = 3.82, *SD* = 0.87), those who saw advertisements and followed brand pages (*M* = 3.51, *SD* = 0.72), those who joined user-created groups (*M* = 1.86, *SD* = 0.69), those who saw advertisements and joined user-created groups (*M* = 1.57, *SD* = 0.66), those who followed brand pages and joined user-created groups (*M* = 1.44, *SD* = 0.53), and those who were exposed to all three types (*M* = 1.37, *SD* = 0.48).

![Figure 2](http://www.journalofadvertisingresearch.com/https://www.journalofadvertisingresearch.com/content/jadvertres/59/2/242/F2.medium.gif)

[Figure 2](http://www.journalofadvertisingresearch.com/content/59/2/242/F2)

Figure 2 
Study Participants' (*N* = 1,016) Attitudes toward Quitting E-Cigarettes, Perceived Behavioral Control, Intention to Quit, and Self-Efficacy by Exposure to E-Cigarette Marketing Types

Mean intention to quit was highest for those not exposed (*M* = 4.54, *SD* = 0.70), followed by those who saw advertisements (*M* = 3.54, *SD* = 0.79), those who followed brand pages (*M* = 3.34, *SD* = 0.84), those who saw advertisements and followed brand pages (*M* = 3.01, *SD* = 0.69), those who joined user-created groups (*M* = 2.57, *SD* = 1.27), those who saw advertisements and joined user-created groups (*M* = 2.04, *SD* = 0.71), those who followed brand pages and joined user-created groups (*M* = 1.44, *SD* = 0.53), and those who were exposed to all three types of advertising (*M* = 1.33, *SD* = 0.49). Mean self-efficacy was highest for those not exposed (*M* = 5.80, *SD* = 0.75), followed by those who saw advertisements (*M* = 5.27, *SD* = 0.79), those who followed brand pages (*M* = 4.38, *SD* = 0.80), those who saw advertisements and followed brand pages (*M* = 4.18, *SD* = 0.98), those who joined user-created groups (*M* = 2.71, *SD* = 1.49), those who saw advertisements and joined user-created groups (*M* = 2.22, *SD* = 0.67), those who followed brand pages and joined user-created groups (*M* = 1.89, *SD* = 0.60), and those who were exposed to all three types of advertising (*M* = 1.77, *SD* = 0.51; See Figure 2).

### Social Identification, Attention to Social Comparison, and Subjective Norms

The author conducted hierarchical regression analyses to test the moderation relationships proposed in Hypothesis 3. Each variable was centered, with interaction terms created between exposure to e-cigarette marketing and potential moderators, and entered into Model 2 of each set of regressions. For each potentially significant moderation effect, the author ran the PROCESS macro for SPSS software (Hayes, 2013) on the centered terms to examine the effect across 1,000 bootstrap samples.

Social identification significantly interacted with exposure to e-cigarette marketing messages to influence 

*   behavioral control (Δ*R*2 = .009), Δ*F*(1, 1012) = 13.20, *p* < .001 (β= .490, 95 percent CI [.001, .207]), *t*(1012) = 3.40, *p* < .001;

*   intention to quit (Δ*R*2 = .004), Δ*F*(1, 1012) = 6.62, *p* < .01 (β= .246, 95 percent CI [.111, .381]), *t*(1012) = 3.58, *p* < .001;

*   self-efficacy (Δ*R*2 = .003), Δ*F*(1, 1012) = 4.60, *p* < .05 (β= .284, 95 percent CI [.130, .437]), *t*(1012) = 3.63, *p* < .001.

Attention to social comparison significantly interacted with exposure to e-cigarette marketing messages to influence intention to quit (Δ*R*2 = .004), Δ*F*(1, 1012) = 6.51, *p* < .01 (β=.202, 95 percent CI [.093,.311]), *t*(1012) = 3.64, *p* < .001. Subjective norms significantly interacted with exposure to e-cigarette marketing messages to influence behavioral control (Δ*R*2 = .005), Δ*F*(1, 1012) = 8.02, *p* < .01 (β = −.200, 95 percent CI [−.369, −.030]), *t*(1012) = −2.32, *p* < .05 (See Figure 3).

## DISCUSSION

The current study contributes to knowledge of effects of social networking site-based e-cigarette marketing in several ways. First, the results suggest that exposure to three different types of social networking site-based e-cigarette marketing—sponsored advertisements, brand pages, and user-created groups—can exert a significant effect on health-related outcomes, on the basis of the elaboration likelihood model (Petty et al., 1983), the theory of planned behavior (Ajzen, 1991), and online information-seeking strategies (Ramirez et al., 2002). Current e-cigarette users who were members of social networking site user-created groups significantly more likely had more negative attitudes toward quitting e-cigarettes, lower behavioral control, lower intention to quit, and lower smoking cessation self-efficacy, compared with those who followed e-cigarette brand pages or saw sponsored advertisements. Because of the increased cognitive agency required to join and participate in user-created e-cigarette groups, these e-cigarette users engaged in the highest level of message elaboration, according to the elaboration likelihood model (Petty et al., 1983), which led to most enduring attitude and behavioral change toward e-cigarettes.

![Figure 3](http://www.journalofadvertisingresearch.com/https://www.journalofadvertisingresearch.com/content/jadvertres/59/2/242/F3.medium.gif)

[Figure 3](http://www.journalofadvertisingresearch.com/content/59/2/242/F3)

Figure 3 
Plots of Significant Interactions between Exposure To E-Cigarette Marketing Messages and Moderators on Key Dependent Measures (*N* = 1,016)

On the basis of online information-seeking strategies (Ramirez et al., 2002), members of user-created groups applied the most interactive strategies while participating in group activities, creating user-generated content, and spreading electronic word of mouth. Active participation in user-created groups resulted in consumers developing stronger relationships with e-cigarette brands. Those participants therefore had the most negative attitudes toward quitting e-cigarettes, had the lowest behavioral control and intention to quit, and were the least likely to refrain from using e-cigarettes in social situations, compared with consumers who were exposed to sponsored advertisements or followed e-cigarette brand pages.

Another important finding is that exposure to all three types of social networking site-based e-cigarette marketing had a significant additive effect on dependent measures. E-cigarette users—who in the past month, had been exposed to e-cigarette brand sponsored advertisements, followed e-cigarette brand pages, and were members of user-created social networking site e-cigarette groups—were significantly more likely (than those who were exposed to two or fewer of the three types of e-cigarette marketing) to have more negative health-related outcomes. This finding suggests that e-cigarette users with the most points of contact with e-cigarette brands on social networking sites (*i.e.*, exposed to all three types of e-cigarette marketing messages in the past month) had the most negative attitudes toward quitting e-cigarettes, lowest behavioral control, lowest intention to quit, and lowest smoking cessation self-efficacy. This result parallels studies that found that smokers who exhibited high brand loyalty toward particular cigarette brands had more negative attitudes toward quitting and lower intention to quit (Dawes, 2014). In the current study, multiple points of contact (sponsored advertisements, brand pages, user-created groups) with e-cigarette brands on social networking sites might have increased brand loyalty, in turn leading to more negative health-related outcomes.

This study also contributes to existing literature by finding several moderators between exposure to social networking site-based e-cigarette marketing and health-related outcomes. First, social identification as an e-cigarette user significantly moderated between exposure to e-cigarette marketing and behavioral control. When participants had been exposed to e-cigarette marketing, higher identification resulted in higher behavioral control, whereas if participants had not been exposed to e-cigarette marketing, higher identification resulted in lower behavioral control.

Second, identification also significantly moderated between exposure and intention to quit. Higher identification resulted in greater intention to quit when participants had been exposed to e-cigarette marketing and lower intention to quit when they had not been exposed to e-cigarette marketing. Third, identification also significantly moderated between exposure and self-efficacy. Among those exposed to e-cigarette marketing, higher identification increased self-efficacy to quit, whereas among those not exposed, higher identification resulted in lower self-efficacy. Identification hence exerted a strong impact on health-related outcomes due to the deindividuation effect, whereby e-cigarette users use e-cigarette behavioral expectations of the group to guide their own e-cigarette behaviors (Tajfel and Turner, 1986).

Attention to social comparison also significantly moderated between exposure to e-cigarette marketing and intention to quit e-cigarettes. For those who were exposed to e-cigarette marketing, higher attention to social comparison resulted in greater intention to quit, whereas for those not exposed, higher attention to social comparison resulted in lower intention to quit. This finding suggests that the degree to which one uses one's reference groups as models for one's own behavior (Lennox and Wolfe, 1984) can exert a strong impact on e-cigarette health-related outcomes.

This study also found e-cigarette subjective norms to moderate significantly between exposure and behavioral control. That is, for e-cigarette users who were exposed to e-cigarette marketing, strong pro-e-cigarette subjective norms did not change their behavioral control, but for those not exposed to e-cigarette marketing, strong pro-e-cigarette subjective norms resulted in higher behavioral control. This finding suggests that the degree to which individuals perceive e-cigarette use to be normative among their social groups exerts a strong influence on behavioral control (Rise et al., 2008).

There are some limitations to the current study that offer implications for future research. First, the participants' most frequently used social networking site and their exposure to e-cigarette marketing were self-reported. Future research should access actual statistical data of participants' exposure to each type of social networking site-based e-cigarette marketing. Second, the study was cross-sectional. It is possible that the amount of time people are exposed to different e-cigarette marketing efforts can have a significant effect on health-related outcomes. Future studies should examine longitudinal effects of exposure to social networking site-based e-cigarette marketing.

Third, the author asked participants to self-report e-cigarette brands they had been exposed to on social networking sites and did not control for prior attitudes toward these brands, which might have had a confounding effect on dependent measures. Each social networking site platform, in addition, might present e-cigarette brand marketing in a different way. Future studies should pretest for pre-existing brand attitudes and also account for differences in platform features.

Fourth, future research should use additional data-analysis methods, such as multigroup and structural equation modeling, and study designs that include experiments to further investigate the validity of this study's results. Future studies also should examine whether the current results apply to general social networking site brand messages and whether they have wider applications outside of the e-cigarette context.

This study also offers practical implications for regulators and advertising practitioners. As the results show, exposure to all three types of social networking site-based e-cigarette marketing had a significant and additive effect on attitudes and behavioral intentions toward e-cigarettes. Individuals who saw sponsored advertisements, followed brand pages, and joined user-created e-cigarette groups more likely had more negative health-related outcomes than those who were not exposed to e-cigarette marketing and those who were exposed to two or fewer types of social networking site-based e-cigarette marketing. Regulators should therefore enact stricter guidelines for social networking site-based e-cigarette marketing, particularly brand pages and user-created groups, because the increased interactivity of such marketing can result in greater consumer engagement with e-cigarette brands.

Because exposure to more social networking site-based e-cigarette marketing types can influence consumers' attitudes and behavioral intention toward e-cigarettes, regulators also should restrict e-cigarette brands from using misleading information, such as promoting e-cigarettes as cessation aids, and glamorizing e-cigarette use in social networking site-based advertising. For advertising practitioners, it is important to work with regulators to establish guidelines for social networking site e-cigarette marketing, such as restricting access to those 18 years and older, including appropriate advertising disclosures, and self-regulating advertising message contents.

The results of the current study also might apply to advertising of other health-related products on social networking sites, including over-the-counter pharmaceuticals and prescription drugs (*e.g.*, opioids). Researchers should take this possibility into consideration when discussing the broader implications of social networking site-based health-product marketing for consumer health. In particular, researchers should examine further the interactivity of social networking site-based advertising, compared with other online and traditional media, to help practitioners and regulators come up with appropriate marketing plans for health-related products such as e-cigarettes.

## LIMITATIONS AND FUTURE RESEARCH

Overall, the current study contributes to ongoing investigations of how social networking site-based e-cigarette marketing can influence consumers' perceptions of e-cigarette brands. Results indicate that social networking site e-cigarette marketing exerts a significant negative impact on health-related outcomes, depending on the type of marketing message (sponsored advertisements, brand pages, user-created groups) and its attendant influence on consumer agency, message elaboration (low, medium, high), and interactivity (passive, active, interactive). Future research should continue to explicate social networking site e-cigarette advertising with potential negative effects on consumer health, to guide federal and state regulations regarding e-cigarette marketing. The long-term goal of these efforts is curbing and preventing e-cigarette use and uptake among vulnerable populations, such as teenagers and young adults, the main target audience of e-cigarette brands.

## ABOUT THE AUTHOR

**Joe Phua** is an associate professor in the Department of Advertising and Public Relations at the University of Georgia's Grady College of Journalism and Mass Communication. His research examines how emerging communication technologies influence and change consumer attitudes and behaviors with regard to advertisements, brands, and health issues. Phua has been published in journals such as *Journal of Advertising* and *Journal of Health Communication*.

*   Received November 1, 2016.
*   Received (in revised form) July 7, 2017.
*   Accepted August 3, 2017.


*   Copyright© 2019 ARF. All rights reserved.

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