Lesson 7

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INTRODuCTION

E­cigarette use is an increasingly serious health issue. The Centers for Disease Control and Preven- tion found that 3.5 percent of U.S. adults were cur- rent e­cigarette users in 2015, and among e­cigarette users overall, 58.8 percent were also cigarette smokers (Centers for Disease Control and Preven- tion, 2016). The health implications of e­cigarettes have ignited debate between harm-reduction and abstinence-only public-health professionals (Berry, Burton, and Howlett, 2017). Harm­reduction advo- cates cite research evidence finding e­cigarettes to be efficacious cessation aids in a step­down approach from conventional tobacco (Benowitz,

Donny, and Hatsukami, 2017), but abstinence-only advocates argue that e­cigarettes, like conventional tobacco, contain cancer-causing toxins and pollut- ants and therefore are not safer tobacco substitutes (Huerta, Walker, Mullen, Johnson, and Ford, 2017). Researchers also found that e­cigarette use signifi- cantly predicted future conventional tobacco uptake (McCabe, Veliz, McCabe, and Boyd, 2017). Effective August 8, 2016, the U.S. Food and Drug Adminis- tration began regulating e­cigarettes, requiring health warnings on packages, banning free sam- ples and vending-machine sales, and restricting sales to those 18 years and older (U.S. Food and Drug Administration, 2016). No regulations were

E-Cigarette Marketing

On Social Networking Sites Effects on Attitudes, Behavioral Control,

Intention to Quit, and Self-Efficacy

JOE PHuA

university of Georgia

[email protected]

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.

• 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).

submitted November 1, 2016;

revised July 7, 2017;

accepted august 3, 2017;

published online may 14, 2018.

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announced for e-cigarette marketing, however, and, as such, e­cigarette brands continue to use social networking sites to advertise to consumers.

According to a 2016 U.S. Surgeon Gen- eral’s report, use of social networking sites by e-cigarette brands for market- ing 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 Asso- ciation (AMA) outlined three prominent types of social network­based 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 impor- tant, because e­cigarette 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 combina- tion 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 the- ory of planned behavior (Ajzen, 1991), and online information-seeking strate- gies (Ramirez, Walther, Burgoon, and

Sunnafrank, 2002) to assess whether con- sumers’ exposure to these three types of social networking site­based e­cigarette marketing would exert a significant and additive effect on attitudes and behavioral intentions toward e­cigarette use. The elab- oration 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), Pin- terest (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 facili- tate liking, sharing, and commenting on messages, which is earned media (Tay- lor, 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 con- tent 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 e­cigarette 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 guide- lines, social networking sites self-regulate e­cigarette­related 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 e­cigarette promotion but allows news and infor- mation about tobacco products.

Because marketers use three major types of social networking site-based market- ing 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 behav- ioral intentions toward e­cigarettes and e­cigarette brands.

LITERATuRE REVIEW

The elaboration likelihood model (Petty et al., 1983), a frequently used theoreti- cal framework in traditional advertising research, proposes two major routes by which attitude change occurs. The cen- tral 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.

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In research applying the elaboration like- lihood model to social networking site- based marketing, advertising cues such as brand-post popularity (Chang, Yu, and Lu, 2015), celebrity-spokesperson charac- teristics (Jin and Phua, 2014), and message personalization (De Keyzer, Dens, and De Pelsmacker, 2015) were found to influ- ence brand-related outcomes. Studies also found online-advertisement avoidance to be a major factor inhibiting user engage- ment 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 elabora- tion likelihood model’s relevance in the online environment by conducting a rep- lication 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 pro- cessing of advertising exposure in online environments. They called for advertising researchers to further explicate traditional advertising theories, such as the elabora- tion 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 opera- tionalized 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 site­based e­cigarette brand marketing examined in this article: sponsored advertisements, brand pages,

and user-created brand groups, respec- tively. 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 e­cigarette mar- keting messages (e­cigarette 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 creat- ing posts. They therefore have a medium level of elaboration and agency. Those who join user­created e­cigarette brand groups, in contrast, more likely will par- ticipate 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 e­cigarette 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 elabora- tion–high agency) would have the most significant impact on attitude and behav- ioral intentions toward e­cigarettes, com- pared 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 (Ram- irez et al., 2002). On the basis of previ- ous research on online communication in computer­mediated settings, the author proposed three strategies brand consum- ers use to extract social and brand informa- tion online: passive, active, and interactive. Consumers using passive strategies to acquire information from social network- ing sites merely observe persuasive mes- sages, such as advertisements, from the sidelines without personal participation in creating or passing along the mes- sages. In the current context, exposure to e­cigarette­related sponsored advertise- ments 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 com- munication. Following e­cigarette 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, Nei- jens, and Vliegenthart, 2015; Van Noort, Antheunis, and Velergh, 2014). Interactive information-seeking strategies involve direct communication between consum- ers and brands, whereby greater depth of discussion, self-disclosure, reciprocity, and alteration of behavior on the basis of feedback are possible. Joining user-created

This study proposed that these three types of

e-cigarette marketing correspond to increasing

elaboration of persuasive messages.

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groups require the highest level of message elaboration and agency and are the most interactive online information-seeking strategy.

H1: E­cigarette users who are mem- bers of user­created e­cigarette brand groups on social network- ing sites will have significantly (a) more negative attitudes toward quitting e-cigarettes, (b) lower perceived behavio- ral control, (c) lower intention to quit, and (d) lower smoking cessation self­efficacy compared with those who saw sponsored advertisements or who follow e­cigarette brand pages on social networking sites.

In addition to exposure, online adver- tising 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 pur- chase 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 wear­out effect (Campbell and Keller, 2003). For familiar and well-liked brands that consumers already follow, the advertising wear­out 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

e­cigarette 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 con- sumers and creators of brand-related con- tent (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 e­cigarette attitudes and behavioral intentions, compared with active strate- gies (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 dif- ficulty with performing particular health­ related activities (Ajzen, 1991). Previous research found behavioral control to exert a strong influence on attitudes and inten- tions toward smoking (Namkoong, Nah, Record, and Vanstee, 2016). Self­efficacy is a tenet of social-cognitive theory that refers to the extent to which individuals believe they have the ability to accomplish partic- ular tasks (Bandura, 2001). Prior research found participation in social networking site-based support groups to predict smok- ing 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 e­cigarette groups would have significantly more neg- ative attitudes toward quitting e­cigarettes, 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 predic- tion was based on the fact that user-created

e­cigarette users are exposed to two or more types of social networking site-based e­cigarette marketing, they more likely will continue to have positive brand evalu- ations, 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 e­cigarette market- ing—sponsored advertisements, brand pages, and user-created groups—would result in significantly more negative atti- tudes toward quitting e­cigarettes, lower behavioral control, lower intention to quit, and lower smoking cessation self­efficacy, compared with exposure to two or fewer types of e­cigarette marketing. Thus:

H2: E­cigarette users who are exposed to all three types of e­cigarette 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 e­cigarettes, (b) lower perceived behavioral con- trol, (c) lower intention to quit, and (d) lower smoking-cessation self-efficacy compared with those who are exposed to two or fewer types of e­cigarette mar- keting 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

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behavioral control, intention to quit, and self­efficacy (See Figure 1).

H3: Social identification, atten- tion to social comparison, and e-cigarette subjective norms will moderate the relationship between exposure to social net- working site-based e-cigarette marketing messages and (a) atti- tude toward quitting e­cigarettes, (b) perceived behavioral control, (c) intention to quit, and (d) self­efficacy.

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 ser- vice posted an online questionnaire to its participants to recruit a nationally

identity-relevant behaviors. Strong smoker identification has been found to lead to greater resistance toward cessation mes- sages and smoking escalation (Hertel and Mermelstein, 2012).

“Attention to social comparison” refers to the extent to which individuals are dis- positionally 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 Craw- ford, 2001). “Subjective norms” (Ajzen, 1991) refers to individuals’ perceptions of normative behavior in social groups, influ- enced by the judgment of others. In previ- ous research, subjective norms have been found to affect tobacco uptake and mainte- nance 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 e­cigarette subjective norms would inter- act with exposure to social networking service-based e-cigarette marketing to affect attitude toward quitting e­cigarettes,

representative sample from across the United States. Only participants who sat- isfied the two screening criteria—active social networking site users and cur- rent e­cigarette 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 subse- quent questions on the basis of their use of this particular site (See Table 2). The questionnaire also included an item ask- ing participants whether they had seen e-cigarette-brand sponsored advertise- ments, 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 e­cigarettes was assessed with four items (modified from Rise et al., 2008) on 7-point semantic­differential scales. Participants were asked whether quitting e­cigarettes in the next six months was “good,” “use- ful,” “pleasant,” or “comfortable” (Cron- bach’s α = .88).

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

Figure 1 Conceptual model illustrating Hypotheses tested in the study (N = 1,016)

Types:

Exposure to E-Cigarette Marketing

a. Sponsored Ads

b. Brand Pages c. User-Created

Groups

H1a/H2a

H1b/H2b

H1c/H2c

H1d/H2d

H3a H3b

H3c H3d

H3a

H3b

H3c

H3d

H3a

H3b

H3c

H3d

Attitude toward Quitting E-Cigarettes

Perceived Behavioral Control

Intention to Quit E-Cigarettes

Smoking Cessation Self-Efficacy

Attention to Social Comparison

E-Cigarette Subjective Norms

Social Identification

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participants could refrain from smoking e­cigarettes in different situations, includ- ing “when nervous” and “when angry” (Cronbach’s α = .96). Social identifica- tion as an e­cigarette user was assessed with eight items (modified from Cam- eron, 2004) on 7-point Likert scales. Items

e­cigarettes” and “During the next six months, I will quit smoking e­cigarettes” (Cronbach’s α = .98).

Self­efficacy toward quitting e­cigarettes was assessed with a 19­item self­efficacy scale (Etter, Bergman, Humair, and Per- neger, 2000). Questions asked whether

included “I have a lot in common with e­cigarette users” and “I feel strong ties with e­cigarette users” (Cronbach’s α = .85).

Attention to social comparison was assessed with 13 items (modified from Lennox and Wolfe, 1984) on 7-point Lik- ert scales. Items included “I pay atten- tion 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 e­cigarettes were assessed with six items (modified from Rise et al., 2008) on 7-point Likert scales. Items included “Peo- ple who mean a lot to me think smoking e­cigarettes is unacceptable” and “Most of my close friends do not currently smoke e­cigarettes” (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 meas- ure exceeds 0.50, whereas discriminant validity is established when AVE for each measure is greater than the squared correlation between each pair of meas- ures in the study. AVE values obtained ranged from 0.61 to 0.75, thereby estab- lishing 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 indi- cated that a single factor did not account for a majority of variance in the measures,

TABLE 1 demographic Characteristics of study Participants (N = 1,016) Demographic N (%)

Gender

male 461 (45.4%)

Female 555 (54.6%)

Ethnicity

Caucasian/White 833 (82.0%)

asian/asian american 41 (4.0%)

Hispanic/latino american 47 (4.6%)

Black/african american 57 (5.6%)

Native american 12 (1.2%)

mixed ethnicity 20 (2.0%)

other ethnicity 6 (0.6%)

Highest Educational Level

did not complete high school 14 (1.4%)

Completed high school 176 (17.3%)

technical/vocational school 56 (5.5%)

attended some college 242 (23.8%)

associate’s degree 128 (12.6%)

Bachelor’s degree 255 (25.1%)

master’s degree 104 (10.2%)

doctoral/professional degree 41 (4.0%)

Annual Household Income

$20,000 or less 126 (12.4%)

$20,001 to $40,000 232 (22.8%)

$40,001 to $60,000 202 (19.9%)

$60,001 to $80,000 181 (17.8%)

$80,001 to $100,000 144 (14.2%)

over $100,000 131 (12.9%)

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• self­efficacy to quit, F(7, 1008) = 65.38, p < .001 (partial η2 = .081, power = 1.00).

Hypotheses 1 and 2 therefore were supported.

Levene’s tests of equality of error vari- ances were insignificant for all depend- ent 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 adver- tising (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

and when all items were loaded onto a single factor, only 25.23 percent of vari- ance 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 multi- variate analysis of variance to examine attitude toward quitting e-cigarettes, behavioral control, intention to quit, and self­efficacy, on the basis of exposure to social networking site­based e­cigarette 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 multivari- ate main effect by exposure (Wilks’s λ= .560), F(18, 3625) = 157.91, p < .001 (par- tial 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 con- trol for Type I error. Significant univari- ate main effects by exposure to e­cigarette 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);

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

TABLE 2 study Participants’ (N = 1,016) social Networking site use and Exposure to E-Cigarette marketing Variable N (%)

Social Networking Site Most Frequently used

Facebook 854 (84.1%)

twitter 60 (5.9%)

instagram 36 (3.5%)

Pinterest 18 (1.8%)

Google+ 14 (1.4%)

snapchat 11 (1.1%)

linkedin 11 (1.1%)

tumblr 7 (0.7%)

other social networking site 5 (0.5%)

Exposure To E-Cigarette Marketing In Last Month

Not exposed 181 (17.8%)

advertisements only 129 (12.7%)

Brand pages only 47 (4.6%)

user-created pages only 7 (0.7%)

advertisements and brand pages 259 (25.4%)

advertisements and user-created groups 23 (2.3%)

Brand pages and user-created groups 9 (0.9%)

advertisements, brand pages, and user-created groups 361 (35.4%)

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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 self­efficacy was highest for those not exposed (M = 5.80, SD = 0.75), followed by those who

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).

Mean intention to quit was highest for those not exposed (M = 4.54, SD = 0.70), fol- lowed by those who saw advertisements (M = 3.54, SD = 0.79), those who followed

saw advertisements (M = 5.27, SD = 0.79), those who followed brand pages (M = 4.38, SD = 0.80), those who saw advertise- ments 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).

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

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networking site­based e­cigarette market- ing—sponsored advertisements, brand pages, and user-created groups—can exert a significant effect on health­related outcomes, on the basis of the elabora- tion likelihood model (Petty et al., 1983), the theory of planned behavior (Ajzen, 1991), and online information-seeking

Social Identification, Attention to Social

Comparison, and Subjective Norms

The author conducted hierarchical regres- sion analyses to test the moderation relationships proposed in Hypothesis 3. Each variable was centered, with interac- tion terms created between exposure to e­cigarette marketing and potential moder- ators, and entered into Model 2 of each set of regressions. For each potentially signifi- cant 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 inter- acted with exposure to e­cigarette market- ing messages to influence

• behavioral control (ΔR2 = .009), ΔF(1, 1012) = 13.20, p < .001 (β= .490, 95 percent CI [.001, .207]), t(1012) = 3.40, p < .001;

• intention to quit (ΔR2 = .004), ΔF(1, 1012) = 6.62, p < .01 (β= .246, 95 percent CI [.111, .381]), t(1012) = 3.58, p < .001;

• self­efficacy (ΔR2 = .003), ΔF(1, 1012) = 4.60, p < .05 (β= .284, 95 percent CI [.130, .437]), t(1012) = 3.63, p < .001.

Attention to social comparison sig- nificantly interacted with exposure to e­cigarette marketing messages to influ- ence intention to quit (ΔR2 = .004), ΔF(1, 1012) = 6.51, p < .01 (β=.202, 95 percent CI [.093,.311]), t(1012) = 3.64, p < .001. Subjec- tive norms significantly interacted with exposure to e­cigarette marketing mes- sages to influence behavioral control (ΔR2 = .005), ΔF(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 knowl- edge of effects of social networking site­based e­cigarette marketing in sev- eral ways. First, the results suggest that exposure to three different types of social

strategies (Ramirez et al., 2002). Current e­cigarette users who were members of social networking site user-created groups significantly more likely had more nega- tive attitudes toward quitting e­cigarettes, lower behavioral control, lower inten- tion to quit, and lower smoking cessation self­efficacy, compared with those who

Figure 3 Plots of significant interactions between Exposure to E-Cigarette marketing messages and moderators on key dependent measures (N = 1,016)

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health­related outcomes. This finding sug- gests that e­cigarette users with the most points of contact with e­cigarette brands on social networking sites (i.e., exposed to all three types of e­cigarette marketing mes- sages in the past month) had the most nega- tive attitudes toward quitting e­cigarettes, 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 exist- ing literature by finding several mod- erators between exposure to social networking site­based e­cigarette market- ing and health-related outcomes. First, social identification as an e­cigarette user significantly moderated between expo- sure to e­cigarette marketing and behav- ioral control. When participants had been exposed to e­cigarette marketing, higher identification resulted in higher behav- ioral control, whereas if participants had not been exposed to e­cigarette market- ing, higher identification resulted in lower behavioral control.

Second, identification also signifi- cantly moderated between exposure and intention to quit. Higher identifica- tion resulted in greater intention to quit when participants had been exposed to

followed e­cigarette brand pages or saw sponsored advertisements. Because of the increased cognitive agency required to join and participate in user­created e­cigarette groups, these e­cigarette 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 e­cigarettes.

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

Another important finding is that expo- sure to all three types of social network- ing site­based e­cigarette marketing had a significant additive effect on dependent measures. E­cigarette users—who in the past month, had been exposed to e­cigarette brand sponsored advertisements, followed e­cigarette brand pages, and were mem- bers of user-created social networking site e­cigarette groups—were significantly more likely (than those who were exposed to two or fewer of the three types of e­cigarette marketing) to have more negative

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

Attention to social comparison also sig- nificantly moderated between exposure to e­cigarette marketing and intention to quit e­cigarettes. For those who were exposed to e­cigarette marketing, higher attention to social comparison resulted in greater intention to quit, whereas for those not exposed, higher attention to social com- parison 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 e­cigarette health­related outcomes.

This study also found e­cigarette sub- jective norms to moderate significantly between exposure and behavioral control. That is, for e­cigarette users who were exposed to e­cigarette marketing, strong pro­e­cigarette subjective norms did not change their behavioral control, but for those not exposed to e­cigarette 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 cur- rent study that offer implications for future

Exposure to all three types of social networking

site-based e-cigarette marketing had a significant

additive effect on dependent measures.

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user­created e­cigarette groups more likely had more negative health-related out- comes than those who were not exposed to e­cigarette marketing and those who were exposed to two or fewer types of social net- working site­based e­cigarette marketing. Regulators should therefore enact stricter guidelines for social networking site-based e­cigarette marketing, particularly brand pages and user-created groups, because the increased interactivity of such market- ing can result in greater consumer engage- ment with e­cigarette brands.

Because exposure to more social net- working site-based e-cigarette market- ing types can influence consumers’ attitudes and behavioral intention toward e­cigarettes, regulators also should restrict e-cigarette brands from using mislead- ing information, such as promoting e­cigarettes as cessation aids, and glamor- izing e­cigarette use in social networking site-based advertising. For advertising practitioners, it is important to work with regulators to establish guidelines for social networking site e­cigarette marketing, such as restricting access to those 18 years and older, including appropriate advertising disclosures, and self-regulating advertis- ing 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 phar- maceuticals and prescription drugs (e.g., opioids). Researchers should take this possibility into consideration when dis- cussing the broader implications of social networking site-based health-product marketing for consumer health. In par- ticular, researchers should examine fur- ther 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 e­cigarettes.

research. First, the participants’ most fre- quently used social networking site and their exposure to e­cigarette marketing were self-reported. Future research should access actual statistical data of partici- pants’ exposure to each type of social net- working site­based e­cigarette marketing. Second, the study was cross-sectional. It is possible that the amount of time people are exposed to different e­cigarette mar- keting efforts can have a significant effect on health-related outcomes. Future stud- ies should examine longitudinal effects of exposure to social networking site-based e­cigarette marketing.

Third, the author asked participants to self­report e­cigarette 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 plat- form, in addition, might present e­cigarette brand marketing in a different way. Future studies should pretest for pre-existing brand attitudes and also account for dif- ferences in platform features.

Fourth, future research should use addi- tional data-analysis methods, such as mul- tigroup and structural equation modeling, and study designs that include experi- ments to further investigate the valid- ity of this study’s results. Future studies also should examine whether the current results apply to general social network- ing site brand messages and whether they have wider applications outside of the e­cigarette context.

This study also offers practical implica- tions for regulators and advertising prac- titioners. As the results show, exposure to all three types of social networking site- based e­cigarette marketing had a signifi- cant and additive effect on attitudes and behavioral intentions toward e­cigarettes. Individuals who saw sponsored advertise- ments, followed brand pages, and joined

LIMITATIONS AND FuTuRE RESEARCH

Overall, the current study contributes to ongoing investigations of how social net- working site­based e­cigarette marketing can influence consumers’ perceptions of e­cigarette brands. Results indicate that social networking site e­cigarette market- ing 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 con- sumer agency, message elaboration (low, medium, high), and interactivity (passive, active, interactive). Future research should continue to explicate social networking site e­cigarette advertising with potential nega- tive effects on consumer health, to guide federal and state regulations regarding e­cigarette marketing. The long­term goal of these efforts is curbing and preventing e­cigarette use and uptake among vulner- able populations, such as teenagers and young adults, the main target audience of e­cigarette 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.

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