Addictive Behaviors 46 (2015) 77–81
Contents lists available at ScienceDirect
Addictive Behaviors
The effect of electronic cigarette advertising on intended use among college students Craig W. Trumbo ⁎, Se-Jin 'Sage' Kim Department of Journalism and Media Communication, Colorado State University, 1785 Campus Delivery, Fort Collins, CO 80523, USA
H I G H L I G H T S • • • •
We test the effect of e-cigarette video ads on college students. Attitudes and perceived social norms predict intention to use e-cigarettes. Many believe that e-cigarettes are less addictive than cigarettes. Positive reaction to e-cigarette ads may promote use.
a r t i c l e
i n f o
Available online 16 March 2015 Keywords: Electronic cigarettes Advertising College students
a b s t r a c t Introduction: . Aside from prohibiting health claims, there are presently no restrictions on electronic cigarette advertising in the U.S. Studies have shown college students have a positive view of e-cigarettes and use on campuses is increasing. The purpose of this study was to test if the appeal of e-cigarette advertisements and beliefs about the addictiveness of e-cigarettes may affect their uptake among college students. Methods: The study was framed within the Theory of Reasoned Action, which posits that behavioral intention can be understood in terms of social norms and attitudes toward a behavior. We also included variables capturing appeal of e-cigarette advertisements, belief that e-cigarettes are not as addictive as cigarettes, and tobacco use. Attitudes toward e-cigarettes, perceived norms concerning their use, beliefs that e-cigarettes are not as addictive as cigarettes, and positive appraisal of e-cigarette advertising videos were all hypothesized to be independently positively associated with intention to use an e-cigarette. Data were collected through a survey of students at a major U.S. university (participation rate 78%, N = 296). Participants were exposed to three e-cigarette video advertisements in random order. Results: In a regression analysis we found positive reaction to the ads and holding the belief that e-cigarettes are not as addictive were both independently associated with intention. Attitudes and norms were also associated but were controlled by inclusion of the other variables. Conclusions: These findings suggest that advertising may promote the uptake of e-cigarettes and may do so in addition to current smoking and alternate tobacco use status. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Aside from prohibiting health claims, there are presently no restrictions on electronic cigarette advertising in the U.S. E-cigarettes are typically marketed as a safer, tech-savvy, fashionable recreational alternative to smoking (Ayers, Ribisl, & Brownstein, 2011). Print, Web, and TV (largely cable and local) advertising of these products emphasize the idea that the smoker may not have to suffer exclusion from places prohibiting smoking (Fairchild, Bayer, & Colgrove, 2014). The themes and imagery used are very similar to those used in the earlier period of televised and print cigarette advertising (Elliott, 2012, 2013). ⁎ Corresponding author. Tel.: +1 970 491 2077. E-mail address: [email protected] (C.W. Trumbo).
http://dx.doi.org/10.1016/j.addbeh.2015.03.005 0306-4603/© 2015 Elsevier Ltd. All rights reserved.
Advertising revenue for e-cigarettes went from $3.7 million to $19.9 million between 2011 and 2012, with a fifth of that targeting television Koch, 2013). The regulatory environment for e-cigarettes is currently in flux. Recently, the U.S. Food and Drug Administration has proposed a set of regulations that largely focus on prohibiting health claims and sales to minors. No regulations have been advanced concerning advertising. The proposed regulations are in a public discussion phase and industry groups are largely supportive of the light restrictions (Gray, 2014). The purpose of the current study was to test if advertisements promoting e-cigarettes and associated beliefs about the addictiveness of e-cigarettes may potentially affect their uptake. Research has recently appeared in which e-cigarette advertising has been examined. In this work, it has been shown that e-cigarette advertising has been widely disseminated and likely targeted at youth, with youth exposure to e-
78
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
cigarette advertising having increased three-fold between 2011 and 2013 (Duke et al., 2014; Emery, Vera, Huang, & Szczypka, 2014). It has also been shown that middle-school students' interest in trying alternate tobacco products (including e-cigarettes) has been influenced by tobacco advertising (Agaku & Ayo-Yusuf, 2013). Further, studies have demonstrated that video advertisements can increase current smokers' interest in trying e-cigarettes—especially if the ads are based on differentiating e-cigarettes from tobacco cigarettes (Kim, Lee, Shafer, Nonnemaker, & Makarenko, 2013; Pepper, Emery, Ribisl, Southwell, & Brewer, 2014). College students are of interest for several reasons. Approximately half of young adults in the U.S. attend a college or university (U.S. Census, 2012). Studies have shown a high degree of social acceptability for ecigarette use by college students (Pokhrel, Little, Fagan, Muranaka, & Herzog, 2014; Trumbo & Harper, 2013). While longitudinal data on ecigarette use by college students is as yet limited, research has shown an increasing trend (Sutfin, McCoy, Morrell, Hoeppner, & Wolfson, 2013). Associated trends in students' use of cigarettes (declining) and hookah (increasing) suggest that the college population is oriented toward uptake of non-cigarette delivered nicotine (Barnett et al., 2013). This particular demographic is an attractive target for marketers of a wide variety of products, especially those that are effectively promoted using persuasive strategies couched in rebellion and peer acceptance (including tobacco) (Noble, Haytko, & Phillips, 2009; Setodji, Martino, Scharf, & Shadel, 2014). The outcome of interest in this study was the individual's expressed likelihood of using e-cigarettes in the near future. To frame this, we employed the Theory of Reasoned Action (Crano & Prislin, 2006; Eagly & Chaiken, 1993; Fishbein, 1967). The basic form of the TRA involves actions predicted by behavioral intention, which is itself predicted by attitudes and norms specific to the behavior in question. Perceived social norms are conceptualized as beliefs about the orientation of relevant others to the target behavior and the motivation to comply with such. Attitudes are conceptualized as beliefs about the behavior in question and an evaluation of such outcomes. Direct and indirect measurement schemes have been developed for both components (Ajzen, 2011). The TRA has been well supported empirically. Meta-analyses have reported significant effects, on the order of 40% of intention and 30% of behavior typically accounted for (Crano & Prislin, 2006). Use of the TRA in health research has been especially prevalent (Cooke & French, 2008; Hackman & Knowlden, 2014; Plotnikoff, Costigan, Karunamuni, & Lubans, 2013; Tyson, Covey, & Rosenthal, 2014). Augmenting the TRA is a common approach and is often used as a test of the adjunct variable's effect based on the TRA's sufficiency claim, which states that attitudes and norms should explain most if not all of the variance in behavioral intention (Conner & Armitage, 1998). In the present study, we applied the TRA in an augmented form that included the effects of e-cigarette advertising appeal, beliefs about the addictiveness of e-cigarettes, and tobacco use behaviors to predict behavioral intention to use an e-cigarette in the near future. Based on theory and previous results, we employed a model that addressed four hypotheses and two research questions. We hypothesized that (1) attitudes toward e-cigarettes and norms concerning their use will each be independently positively associated with intention to use an ecigarette in the near future. To follow this, we asked if attitudes and norms were controlled by the addition of addictiveness beliefs and ad appeal. Next we hypothesized that (2) addictiveness beliefs about ecigarettes would be independently positively associated with intention to use an e-cigarette in the near future, (3) that appeal of e-cigarette advertising videos would be independently positively associated with intention to use an e-cigarette in the near future, and (4) that tobacco use items (tried e-cigs, alternate tobacco use, ever smoked) would each be independently positively associated with intention to use an e-cigarette in the near future. Finally, we asked if attitudes, norms, addictiveness beliefs, or ad appeal are controlled by the addition of tobacco use items.
2. Methods 2.1. Participants and data collection The study was conducted at a Southwestern public university (undergraduate enrollment approximately 22,000). Subsequent to approval from the university's institutional review board, a survey was presented as an extra credit activity in a freshman-sophomore level large lecture class that served a broad cross-section of students. The survey was completed outside of class at a place of the student's choosing over the course of 1 week. Unique student identification numbers were required so to prevent multiple completions. Of the 398 students enrolled in the course, 309 completed the survey (78%) in October 2013. After removal of incompletes, 296 cases were available for analysis. There were no missing data. In the online survey, participants were exposed to e-cigarette advertisements by being shown three different e-cigarette video advertisements taken from YouTube for the products Mistic, blu, and Njoy (blu, 2014; Mistic, 2014; Njoy, 2014). All participants saw each ad, and the order of the ads was randomized. These specific ads were selected because at the time two brands (blu, Njoy) were the top market leaders (Esterl, 2013). The ads featured actors of opposite sex making emotion-based appeals. The third ad for Mistic was selected (7th in sales) because it featured an unknown actor making a more rationally based appeal. As a group, the ads represented a good diversity of the current marketing at the time. 2.2. Measures Appeal is a positive evaluation of the set of ads. This was measured by replicating the approach of Kelly, Slater, and Karan (2002). The product brand and appeal of the ad itself were assessed: “In your opinion, is this advertisement (or product brand) enjoyable, likable, appealing?” Responses were on a scale from 1 = not at all to 7 = very. This yielded six items for each of the three ads, 18 items overall, which were summed for an index measure which presented α = .95. Addictiveness indicated that the participant believed that “e-cigarettes are less addictive than cigarettes,” with a 5-point response strongly disagree to strongly agree (Choi & Forster, 2013). Ever smoked is having smoked 100 cigarettes in lifetime (1 = yes, 0 = no), from the Behavioral Risk Factor Surveillance System (CDC, 2009). Current use was also assessed (now smoking every day, some days, or not at all). Alt. Tobacco was a score for how many other tobacco products had ever been used. This was measured by an inventory of five products: hookah, pipe, cigars, snuff, and chewing tobacco. Responses were on a 1–4 scale, including Never, Tried once or twice, Some days, Every day. A summed index was computed, which presented α = .74. Tried E-cigs indicated that the participant had tried an e-cigarette (1 = yes, 0 = no). The three TRA items were measured using best practice recommendations (Ajzen, 2011). Intention is the likelihood of using an e-cigarette in the future. We asked “How likely do you think it is that you would use an e-cigarette in the not-too distant future, say in the next six months?” Responses ran a 7-point scale from absolutely not, very unlikely, unlikely, maybe yes maybe no, likely, very likely and to absolutely yes. Attitude was a positive appraisal of the e-cigarette as a new way of smoking. It included three items with 5-point responses (strongly disagree to strongly agree): “Use of e-cigarettes should be legal for adults; Ecigarettes are a big step forward; E-cigarettes are a more modern way of using tobacco.” This measure was summed across the three items for an index that presented α = .62. Norms was a positive perception that significant others would be approving of e-cigarette use. The was measured with three pairs of items, all with 5-point responses (strongly disagree to strongly agree): “It would be acceptable to my closest friends (most people I know, closest family members) if I used ecigarettes; when it comes to things like e-cigarettes it is important for
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
me to follow the wishes of my closest friends (most people I know, closest family members).” This measure was computed as the sum of the products of the paired statements for an index that presented α = .84. 2.3. Data analysis
Table 2 Hierarchical regression on Behavioral Intention (N = 296). Block
IVs
B
SE
β
t
p
1
Attitudes Norms Attitudes Norms Addictiveness Appeal Attitudes Norms Addictiveness Appeal Ever Smoked Alt. Tobacco Tried E-cigs
.07 .01 .03 .01 .16 .01 .03 .01 .13 .01 1.21 .08 1.13
.04 .01 .04 .01 .08 .01 .03 .01 .06 .01 .25 .03 .17
.13 .16 .04 .11 .13 .20 .06 .02 .10 .14 .24 .14 .36
2.11 2.47 0.67 1.69 2.14 3.32 1.10 0.36 2.09 2.91 4.82 2.80 6.65
.035 .014 .510 .091 .033 .001 .273 .715 .038 .004 .000 .005 .000 Adj. R2
2
All measures were coded so as to be consistent such that higher values indicate more positive orientations toward e-cigarettes and the likelihood of their use. Descriptive analyses used appropriate measures of central tendency and dispersion. Cronbach's alpha was used to assess index reliability. Paired t-tests were used to compare ad appeal scores. Pearson's r and eta were used for associations with interval and nominal variables, respectively. Hypothesis testing was accomplished through a hierarchical multiple OLS regression. All analyses were conducted using Stata 13.
79
3
ΔR2
F(df)
p
.061
9.5 (2,293)
.000
.051
8.4 (2, 291)
.000
.311 .410
51.8 (2,288) 30.2 (7,288)
.000 .000
3. Results Descriptive statistics are reported in Table 1. The sample was 38% male. Only 7.5% of the respondents reported having smoked 100 cigarettes in their lifetimes, with 5.8% reporting daily or regular smoking and 1.7% having quit. The mean score on Alt. Tobacco was 7.39 (range 5–18) with low values indicating less use. Further examination showed that most respondents had at some time tried at least one of the five: 70% had tried hookah at least once, 47% cigar, and about 15–18% pipe, snuff or chew. On Tried E-cigs, 22% reported having had tried them and 4% were regular users. The mean score on Addictiveness was 3.22 (range 1–5). Further examination showed that 19% believed or strongly believed that e-cigarettes were as addictive as cigarettes, 49% were uncertain, and 32% believed or strongly believed that e-cigarettes were not as addictive as cigarettes. Prior to computing the overall Appeal score, the three ads were compared. Paired t-tests showed that the ad for blu was significantly more appealing than the other two: blu M(SD) = 20.0 (8.1), Mystic 15.4 (6.7), Njoy 15.9 (8.4), blu-Mystic t(295) = 12.8 p b .01, blu-Njoy t(295) = 8.4 p b .01. Correlations among the three appeal scores ranged from .48 to .67 (p b .01). Each appeal score was similarly correlated with Intention, ranging from .20 to .24 (p b .01). The overall similarities supported combining the items to the single measure Appeal. Associations among the variables are also reported in Table 1. Of note, Intention had significant correlations with all of the other variables, with the tobacco use items presenting the strongest coefficients. Associations among the independent variables were also consistent. Tobacco use variables were all significantly associated. Norms and Attitude were significantly associated, and Appeal was significantly associated with Attitude and the tobacco use variables, except Ever smoked. The regression analysis was configured in three steps to first test the TRA variables, then Appeal and Addictiveness, and finally the tobacco use items. Results are presented in Table 2. In the first block Attitude and Norms were each independently positively associated with Intention (hypothesis 1). In the second block Addictiveness and Appeal were each
independently positively associated with Intention (hypotheses 2 and 3), while the effect of Attitude and Norms were both controlled (research question 1). In the third block, each of the tobacco use variables was independently positively associated with Intention (hypothesis 4), while Addictiveness and Appeal remained significant (research question 2). 4. Discussion The first hypothesis was partially supported. Attitude and Norms were both independently associated with Intention, but only in the first block of the analysis model. Neither of the variables presented strong effect sizes and the R2 for the block was modest. This finding suggests that attitudes and norms may play a role in the use of e-cigarettes but are likely not as central as in other behaviors in which studies have found greater variance explained (Crano & Prislin, 2006). The first research question was positively affirmed as Attitude and Norms were both controlled in the second block when Addictiveness and Appeal were added. Despite the partial support, it is worth noting that one of the practical features of the TRA is its demonstration of potential leverage points that might be used in persuasive messaging. The associations among Appeal, Attitudes and Norms suggest that marketing messages may have taken this approach. Formal content analyses of e-cigarette marketing and anecdotal commentaries have highlighted the observation that these advertisements do include messages promoting attitudes of product superiority (over cigarettes) and defiance of social norms against smoking (Cobb, Brookover, & Cobb, 2015; Elliott, 2012, 2013; Grana & Ling, 2014; Huang, Kornfield, Szczypka, & Emery, 2014; Kurutz, 2013; Paek, Kim, Hove, & Huh, 2014; Richardson, Ganz, Stalgaitis, Abrams, & Vallone, 2014). These leverage points may also be effective in developing counter-marketing strategies. The second and third hypotheses were both supported. Addictiveness and Appeal were both positively associated with Intention. Also, the second research question was negative, as the effects of Addictiveness and Appeal were not controlled by the tobacco use variables. Note that
Table 1 Descriptives: Reliabilities, range, means, standard deviations, correlations (N = 296).
1. Intention 2. Sex 3. Attitude 4. Norms 5. Ever smoked 6. Alt. tobacco 7. Tried E−cigs 8. Addictiveness 9. Appeal
α
Range
M(SD)
1.
– – .62 .84 – .74 – – .95
1–7 1=M 3–15 3–75 1 = Yes 5–18 1 = Yes 1–5 7–126
1.9 (1.32) .38 (.49) 6.9 (2.27) 29.93(15.37) .075 (.26) 7.39 (2.36) .22 (.42) 3.22 (1.01) 51.34 (19.33)
– .16⁎⁎ .20⁎⁎ .22⁎⁎ .46⁎⁎ .37⁎⁎ .55⁎⁎ .21⁎⁎ .27⁎⁎
Reporting η for binary variables. ⁎ p b .05. ⁎⁎ p b .01.
2. – −.05 −.08 .10 .47⁎⁎ .19⁎⁎ −.08 .02
3.
4.
5.
6.
7.
8.
9.
– −.10 .12⁎
– .19⁎⁎
–
–
.44⁎⁎ −.07 −.03 .14⁎ .27⁎⁎ .39⁎⁎
– .15⁎ .18⁎⁎ .23⁎⁎ .31⁎⁎ .25⁎⁎
–
.24⁎⁎ .44⁎⁎ −.10 .09
–
.39⁎⁎ −.09 .18⁎⁎
80
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
Appeal and Addictiveness are themselves modestly correlated (r = .19 p b .01). Taken together, these findings suggest a potentially important aspect of the marketing information environment surrounding ecigarettes. While directional effects could not be assessed in this study design, there was a set of mutually reinforcing associations among ecigarette behavior, the perceived quality of marketing messages, and a key belief about addictiveness. This finding suggests the potentially central role of addictiveness beliefs in the set of mechanisms influencing the attractiveness of e-cigarettes. These findings have relevance in the context of other work that has examined motivations for e-cigarette initiation and associated perceptions of low potential harm (Choi & Forster, 2013; Goniewicz, Lingas, & Hajek, 2012; Kim et al., 2013; Pepper et al., 2014). Finally, the third hypothesis was supported. Each of the tobacco use measures was independently positively associated with Intention, and they collectively added about three-quarters of total variance explained. The three are themselves correlated, and other studies have found that e-cigarette use as well as use of alternate tobacco products is highest among cigarette smokers (Dawkins, Turner, Roberts, & Soar, 2013; Etter, 2010; King, Alam, Promoff, Arrazola, & Dube, 2013). These represent the constellation of nicotine addictive behaviors, with e-cigarettes the newest addition. The manner in which e-cigarettes are finding a place in this behavioral set is of considerable interest in public health. The potential for the e-cigarette to become a normalized alternate tobacco use behavior on college campuses is of special interest because of the now widespread presence of hookah bars (Barnett et al., 2013). 4.1. Limitations This pilot study relied on a limited convenience sample of students from a single college campus, limiting generalizability. Prior exposure to e-cigarette advertisements in general or these specific ads was not assessed, which presented a potential confound. Because the study was cross-sectional causal directionality could not be determined, making it possible that previous experiences with e-cigarettes precipitated the appeal of the ads. Because the study was retrospective, it relied on recall and did not actually test behavioral outcomes. In terms of measurement, the effect of Attitude was limited, which may have been due to the low reliability of the scale. And participants completed the questionnaire in an uncontrolled environment that might have been subject to unknown biasing factors. 4.2. Conclusions This is among the first published studies to examine the effect of ecigarette advertising. The findings support the argument that e-cigarette advertising is potentially promoting the uptake of e-cigarettes and is doing so in addition to current smoking and alternate tobacco use. This has been show to be true for the marketing of cigarettes, and advertising of those products is now regulated (Biener & Siegel, 2000). These findings should inform the ongoing discussion concerning the regulation of ecigarette advertising. Finally, this study examined college students. As described above, this is a component of society that is strongly desired by marketers. The e-cigarette may be especially problematic for this population in conjunction with the rapid establishment and general acceptance of hookah lounges. This new path to nicotine exposure and dependence may also have the potential to fuel a resurgence of cigarette smoking. Health professionals on college campuses should be aware of the uptake of these devices among students and see the e-cigarette as yet another dimension in the web of avenues to nicotine exposure and addiction. The ability of campus health workers to effectively reach students is significant and provides an opportunity to better understand motivations for the use of e-cigarettes and to test strategies to help emerging adults avoid this pathway to nicotine addiction.
Role of funding sources None.
Contributors Craig W. Trumbo and Se-Jin Kim have both contributed significantly to, and approve of this final manuscript. Both authors contributed to the conceptualization of the current manuscript. Kim supervised the data collection. Both authors collaborated on all analyses. Trumbo drafted the Introduction and Methods. Both authors contributed to drafting the Results and Discussion, and both contributed to revisions.
Conflict of Interest Both authors declare that they have no conflicts of interest.
References Agaku, I. T., & Ayo-Yusuf, O. A. (2013). The effect of exposure to pro-tobacco advertising on experimentation with emerging tobacco products among U.S. adolescents. Health Education & Behavior, 41, 275–280. http://dx.doi.org/10.1177/ 1090198113511817. Ajzen, I. (2011). Constructing a TpB questionnaire: Conceptual and methodological considerations. Amherst, MA: University of Massachusetts (Available at http://www-unix.oit. umass.edu/~aizen/tpb.html (Accessed 10/012014)). Ayers, J. W., Ribisl, K. M., & Brownstein, J. S. (2011). Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. American Journal of Preventive Medicine, 40, 448–453. http://dx.doi.org/10. 1016/j.amepre.2010.12.007. Barnett, T. E., Smith, T., He, Y., Soule, E. K., Curbow, B. A., Tomar, S. L., et al. (2013). Evidence of emerging hookah use among university students: A cross-sectional comparison between hookah and cigarette use. BMC Public Health, 13, 302. http://dx.doi.org/ 10.1186/1471-2458-13-302. Biener, L., & Siegel, M. (2000). Tobacco marketing and adolescent smoking: More support for a causal inference. American Journal of Public Health, 90(3), 407–411. http://dx.doi. org/10.2105/AJPH.90.3.407. blu (2014). Stephen Dorff's rise from the ashes—Brought to you by blu cigs. Available at https://www.youtube.com/watch?v=VZishwAt_RM (Accessed 10/01/2014). CDC (2009). Behavioral risk factor surveillance system survey questionnaire. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention (Available at http://www.cdc.gov/brfss (Accessed 10/01/2014)). Census, U. S. (2012). CPS data on school enrollment. Washington, DC: US Department of Commerce (Available at http://www.census.gov/hhes/school/data/cps/2012/ tables. html (Accessed 10/01/2014)). Choi, K., & Forster, J. (2013). Characteristics associated with awareness, perceptions, and use of electronic nicotine delivery systems among young US Midwestern adults. American Journal of Public Health, 103, 556–561. http://dx.doi.org/10.2105/AJPH. 2012.300947. Cobb, N. K., Brookover, J., & Cobb, C. O. (2015). Forensic analysis of online marketing for electronic nicotine delivery systems. Tobacco Control, 24(2), 128–131 (http://10.1136/ tobaccocontrol-2013-051185). Conner, M., & Armitage, C. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1429–1464. http://dx.doi.org/10.1111/j.1559-1816.1998.tb01685.x. Cooke, R., & French, D. P. (2008). How well do the theory of reasoned action and theory of planned behaviour predict intentions and attendance at screening programmes? A meta-analysis. Psychology & Health, 23, 745–765. http://dx.doi.org/10.1080/ 08870440701544437. Crano, W., & Prislin, R. (2006). Attitudes and persuasion. Annual Review of Psychology, 57, 345–375. http://dx.doi.org/10.1146/annurev.psych.57.102904.190034. Dawkins, L., Turner, J., Roberts, A., & Soar, K. (2013). ‘Vaping’ profiles and preferences: An online survey of electronic cigarette users. Addiction, 108(6), 1115–1125. http://dx. doi.org/10.1111/add.12150. Duke, J. C., Lee, Y. O., Kim, A. E., Watson, K. A., Arnold, K. Y., Nonnemaker, J. M., et al. (2014). Exposure to Eeectronic cigarette television advertisements among youth and young adults. Pediatrics. http://dx.doi.org/10.1542/peds. 2014-0269. Eagly, A., & Chaiken, S. (1993). The psychology of attitudes. Ft. Worth: Harcourt Brace Jovanovich. Elliott, S. (2012, Dec. 6). Campaigns for e-cigarettes borrow from tobacco's heyday. The New York Times, B1. Elliott, S. (2013, Aug. 30). E-cigarette makers' ads echo tobacco's heyday. The New York Times, B1. Emery, S. L., Vera, L., Huang, J., & Szczypka, G. (2014). Wanna know about vaping? Patterns of message exposure, seeking and sharing information about e-cigarettes across media platforms. Tobacco Control, 23, iii17–iii25. http://dx.doi.org/10.1136/ tobaccocontrol-2014-051648. Esterl, M. (2013). E-Cigarettes fire up investors, regulators. The Wall Street Journal, 261(134), pA1–A2 (Available at http://online.wsj.com/articles/SB10001424127887324904004 578535362153026902 (Accessed 10/01/2014)). Etter, J. F. (2010). Electronic cigarettes: A survey of users. BMC Public Health, 10, 231 (doi: 1471-2458-10-231). Fairchild, A. L., Bayer, R., & Colgrove, J. (2014). The renormalization of smoking? Ecigarettes and the tobacco "endgame". New England Journal of Medicine, 370(4), 293–295. http://dx.doi.org/10.1056/NEJMp131394. Fishbein, I. (1967). Readings in attitude theory and measurement. New York: John Wiley and Sons.
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81 Goniewicz, M. L., Lingas, E. O., & Hajek, P. (2012). Patterns of electronic cigarette use and user beliefs about their safety and benefits: An Internet survey. Drug and Alcohol Review, 32(2), 133–140. http://dx.doi.org/10.1111/j.1465-3362.2012.00512.x. Grana, R. A., & Ling, P. M. (2014). “Smoking revolution”: A content analysis of electronic cigarette retail websites. American Journal of Preventive Medicine, 46, 395–403. http://dx.doi.org/10.1016/j.amepre. 2013.12.010. Gray, E. (2014). Industry is winning the e-cig regulation battle. Time Magazine (Available at http://time.com/2857718/industry-is-winning-the-e-cig-regulation-battle (Accessed 10/01/2014)). Hackman, C. L., & Knowlden, A. P. (2014). Theory of reasoned action and theory of planned behavior-based dietary interventions in adolescents and young adults: A systematic review. Adolescent Health, Medicine and Therapeutics, 5, 101–114. http://dx.doi.org/ 10.2147/AHMT.S56207. Huang, J., Kornfield, R., Szczypka, G., & Emery, S. L. (2014). A cross-sectional examination of marketing of electronic cigarettes on Twitter. Tobacco Control, 23, 26–30. http://dx. doi.org/10.1136/tobaccocontrol-2014-051551. Kelly, K. J., Slater, M. D., & Karan, D. (2002). Image advertisements' influence on adolescents' perceptions of the desirability of beer and cigarettes. Journal of Public Policy & Marketing, 21, 295–304 (doi: http://dx.doi.org/10.1509/jppm.21.2.295.17585). Kim, A. E., Lee, Y. O., Shafer, P., Nonnemaker, J., & Makarenko, O. (2013). Adult smokers' receptivity to a television advert for electronic nicotine delivery systems. Tobacco Control. http://dx.doi.org/10.1136/tobaccocontrol-2013-051130. King, B. A., Alam, S., Promoff, G., Arrazola, R., & Dube, S. R. (2013). Awareness and ever-use of electronic cigarettes among U.S. adults, 2010–2011. Nicotine & Tobacco Research, 15(9), 1623–1627. http://dx.doi.org/10.1093/ntr/ntt013. Koch, W. (2013, Sept. 3). Big tobacco back in TV viewers' faces as FDA cuts in. USA TODAY, 1A (Available at http://www.usatoday.com/story/news/nation/ 2013/09/03/bigtobacco-tv-ecigarette-ads/2608427 (Accessed 10/012014)). Kurutz, S. (2013, Sept. 8). Smoking is back, without the stigma. The New York Times, E1. Mistic (2014). Mistic electronic cigarettes: Best taste, best value. Available at https://www. youtube.com/watch?v=bTXY9ntYJvo (Accessed 10/01/2014) Njoy (2014). Relax … It's Courtney Love! Available at https://www.youtube.com/watch ? v=_SFs761eEFk (Accessed 1/01/2014)
81
Noble, S. M., Haytko, D. L., & Phillips, J. (2009). What drives college-age Generation Y consumers? Journal of Business Research, 62, 617–628. http://dx.doi.org/10.1016/j. jbusres.2008.01.020. Paek, H. -J., Kim, S., Hove, T., & Huh, J. Y. (2014). Reduced harm or another gateway to smoking? Source, message, and information characteristics of e-cigarette videos on YouTube. Journal of Health Communication, 19, 545–560. http://dx.doi.org/10.1080/ 10810730.2013.821560. Pepper, J. K., Emery, S. L., Ribisl, K. M., Southwell, B. G., & Brewer, N. T. (2014). Effects of advertisements on smokers' interest in trying e-cigarettes: The roles of product comparison and visual cues. Tobacco Control, 23, iii31–iii36. http://dx.doi.org/10.1136/ tobaccocontrol-2014-051718. Plotnikoff, R. C., Costigan, S. A., Karunamuni, N., & Lubans, D. R. (2013). Social cognitive theories used to explain physical activity behavior in adolescents: A systematic review and meta-analysis. Preventive Medicine, 56, 245–253. http://dx.doi.org/10. 1016/j.ypmed.2013.01.013. Pokhrel, P., Little, M. A., Fagan, P., Muranaka, N., & Herzog, T. A. (2014). Electronic cigarette use outcome expectancies among college students. Addictive Behaviors, 39, 1062–1065. http://dx.doi.org/10.1016/j.addbeh.2014.02.014. Richardson, A., Ganz, O., Stalgaitis, C., Abrams, D., & Vallone, D. (2014). Noncombustible tobacco product advertising: How companies are selling the new face of tobacco. Nicotine & Tobacco Research, 16, 606–614. http://dx.doi.org/10.1093/ntr/ntt200. Setodji, C. M., Martino, S. C., Scharf, D. M., & Shadel, W. G. (2014). Quantifying the persistence of pro-smoking media effects on college students' smoking risk. Journal of Adolescent Health, 54, 474–480. http://dx.doi.org/10.1016/j.jadohealth.2013.09.011. Sutfin, E. L., McCoy, T. P., Morrell, H. E., Hoeppner, B. B., & Wolfson, M. (2013). Electronic cigarette use by college students. Drug and Alcohol Dependence, 131, 214–221. http://dx.doi.org/10.1016/j.drugalcdep.2013.05.001. Trumbo, C. W., & Harper, R. (2013). Use and perception of electronic cigarettes among college students. Journal of American College of Health, 61, 149–155. http://dx.doi.org/10. 1080/07448481.2013.776052. Tyson, M., Covey, J., & Rosenthal, H. E. S. (2014). Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis. Health Psychology. http://dx.doi. org/10.1037/hea0000047.
Contents lists available at ScienceDirect
Addictive Behaviors
The effect of electronic cigarette advertising on intended use among college students Craig W. Trumbo ⁎, Se-Jin 'Sage' Kim Department of Journalism and Media Communication, Colorado State University, 1785 Campus Delivery, Fort Collins, CO 80523, USA
H I G H L I G H T S • • • •
We test the effect of e-cigarette video ads on college students. Attitudes and perceived social norms predict intention to use e-cigarettes. Many believe that e-cigarettes are less addictive than cigarettes. Positive reaction to e-cigarette ads may promote use.
a r t i c l e
i n f o
Available online 16 March 2015 Keywords: Electronic cigarettes Advertising College students
a b s t r a c t Introduction: . Aside from prohibiting health claims, there are presently no restrictions on electronic cigarette advertising in the U.S. Studies have shown college students have a positive view of e-cigarettes and use on campuses is increasing. The purpose of this study was to test if the appeal of e-cigarette advertisements and beliefs about the addictiveness of e-cigarettes may affect their uptake among college students. Methods: The study was framed within the Theory of Reasoned Action, which posits that behavioral intention can be understood in terms of social norms and attitudes toward a behavior. We also included variables capturing appeal of e-cigarette advertisements, belief that e-cigarettes are not as addictive as cigarettes, and tobacco use. Attitudes toward e-cigarettes, perceived norms concerning their use, beliefs that e-cigarettes are not as addictive as cigarettes, and positive appraisal of e-cigarette advertising videos were all hypothesized to be independently positively associated with intention to use an e-cigarette. Data were collected through a survey of students at a major U.S. university (participation rate 78%, N = 296). Participants were exposed to three e-cigarette video advertisements in random order. Results: In a regression analysis we found positive reaction to the ads and holding the belief that e-cigarettes are not as addictive were both independently associated with intention. Attitudes and norms were also associated but were controlled by inclusion of the other variables. Conclusions: These findings suggest that advertising may promote the uptake of e-cigarettes and may do so in addition to current smoking and alternate tobacco use status. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Aside from prohibiting health claims, there are presently no restrictions on electronic cigarette advertising in the U.S. E-cigarettes are typically marketed as a safer, tech-savvy, fashionable recreational alternative to smoking (Ayers, Ribisl, & Brownstein, 2011). Print, Web, and TV (largely cable and local) advertising of these products emphasize the idea that the smoker may not have to suffer exclusion from places prohibiting smoking (Fairchild, Bayer, & Colgrove, 2014). The themes and imagery used are very similar to those used in the earlier period of televised and print cigarette advertising (Elliott, 2012, 2013). ⁎ Corresponding author. Tel.: +1 970 491 2077. E-mail address: [email protected] (C.W. Trumbo).
http://dx.doi.org/10.1016/j.addbeh.2015.03.005 0306-4603/© 2015 Elsevier Ltd. All rights reserved.
Advertising revenue for e-cigarettes went from $3.7 million to $19.9 million between 2011 and 2012, with a fifth of that targeting television Koch, 2013). The regulatory environment for e-cigarettes is currently in flux. Recently, the U.S. Food and Drug Administration has proposed a set of regulations that largely focus on prohibiting health claims and sales to minors. No regulations have been advanced concerning advertising. The proposed regulations are in a public discussion phase and industry groups are largely supportive of the light restrictions (Gray, 2014). The purpose of the current study was to test if advertisements promoting e-cigarettes and associated beliefs about the addictiveness of e-cigarettes may potentially affect their uptake. Research has recently appeared in which e-cigarette advertising has been examined. In this work, it has been shown that e-cigarette advertising has been widely disseminated and likely targeted at youth, with youth exposure to e-
78
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
cigarette advertising having increased three-fold between 2011 and 2013 (Duke et al., 2014; Emery, Vera, Huang, & Szczypka, 2014). It has also been shown that middle-school students' interest in trying alternate tobacco products (including e-cigarettes) has been influenced by tobacco advertising (Agaku & Ayo-Yusuf, 2013). Further, studies have demonstrated that video advertisements can increase current smokers' interest in trying e-cigarettes—especially if the ads are based on differentiating e-cigarettes from tobacco cigarettes (Kim, Lee, Shafer, Nonnemaker, & Makarenko, 2013; Pepper, Emery, Ribisl, Southwell, & Brewer, 2014). College students are of interest for several reasons. Approximately half of young adults in the U.S. attend a college or university (U.S. Census, 2012). Studies have shown a high degree of social acceptability for ecigarette use by college students (Pokhrel, Little, Fagan, Muranaka, & Herzog, 2014; Trumbo & Harper, 2013). While longitudinal data on ecigarette use by college students is as yet limited, research has shown an increasing trend (Sutfin, McCoy, Morrell, Hoeppner, & Wolfson, 2013). Associated trends in students' use of cigarettes (declining) and hookah (increasing) suggest that the college population is oriented toward uptake of non-cigarette delivered nicotine (Barnett et al., 2013). This particular demographic is an attractive target for marketers of a wide variety of products, especially those that are effectively promoted using persuasive strategies couched in rebellion and peer acceptance (including tobacco) (Noble, Haytko, & Phillips, 2009; Setodji, Martino, Scharf, & Shadel, 2014). The outcome of interest in this study was the individual's expressed likelihood of using e-cigarettes in the near future. To frame this, we employed the Theory of Reasoned Action (Crano & Prislin, 2006; Eagly & Chaiken, 1993; Fishbein, 1967). The basic form of the TRA involves actions predicted by behavioral intention, which is itself predicted by attitudes and norms specific to the behavior in question. Perceived social norms are conceptualized as beliefs about the orientation of relevant others to the target behavior and the motivation to comply with such. Attitudes are conceptualized as beliefs about the behavior in question and an evaluation of such outcomes. Direct and indirect measurement schemes have been developed for both components (Ajzen, 2011). The TRA has been well supported empirically. Meta-analyses have reported significant effects, on the order of 40% of intention and 30% of behavior typically accounted for (Crano & Prislin, 2006). Use of the TRA in health research has been especially prevalent (Cooke & French, 2008; Hackman & Knowlden, 2014; Plotnikoff, Costigan, Karunamuni, & Lubans, 2013; Tyson, Covey, & Rosenthal, 2014). Augmenting the TRA is a common approach and is often used as a test of the adjunct variable's effect based on the TRA's sufficiency claim, which states that attitudes and norms should explain most if not all of the variance in behavioral intention (Conner & Armitage, 1998). In the present study, we applied the TRA in an augmented form that included the effects of e-cigarette advertising appeal, beliefs about the addictiveness of e-cigarettes, and tobacco use behaviors to predict behavioral intention to use an e-cigarette in the near future. Based on theory and previous results, we employed a model that addressed four hypotheses and two research questions. We hypothesized that (1) attitudes toward e-cigarettes and norms concerning their use will each be independently positively associated with intention to use an ecigarette in the near future. To follow this, we asked if attitudes and norms were controlled by the addition of addictiveness beliefs and ad appeal. Next we hypothesized that (2) addictiveness beliefs about ecigarettes would be independently positively associated with intention to use an e-cigarette in the near future, (3) that appeal of e-cigarette advertising videos would be independently positively associated with intention to use an e-cigarette in the near future, and (4) that tobacco use items (tried e-cigs, alternate tobacco use, ever smoked) would each be independently positively associated with intention to use an e-cigarette in the near future. Finally, we asked if attitudes, norms, addictiveness beliefs, or ad appeal are controlled by the addition of tobacco use items.
2. Methods 2.1. Participants and data collection The study was conducted at a Southwestern public university (undergraduate enrollment approximately 22,000). Subsequent to approval from the university's institutional review board, a survey was presented as an extra credit activity in a freshman-sophomore level large lecture class that served a broad cross-section of students. The survey was completed outside of class at a place of the student's choosing over the course of 1 week. Unique student identification numbers were required so to prevent multiple completions. Of the 398 students enrolled in the course, 309 completed the survey (78%) in October 2013. After removal of incompletes, 296 cases were available for analysis. There were no missing data. In the online survey, participants were exposed to e-cigarette advertisements by being shown three different e-cigarette video advertisements taken from YouTube for the products Mistic, blu, and Njoy (blu, 2014; Mistic, 2014; Njoy, 2014). All participants saw each ad, and the order of the ads was randomized. These specific ads were selected because at the time two brands (blu, Njoy) were the top market leaders (Esterl, 2013). The ads featured actors of opposite sex making emotion-based appeals. The third ad for Mistic was selected (7th in sales) because it featured an unknown actor making a more rationally based appeal. As a group, the ads represented a good diversity of the current marketing at the time. 2.2. Measures Appeal is a positive evaluation of the set of ads. This was measured by replicating the approach of Kelly, Slater, and Karan (2002). The product brand and appeal of the ad itself were assessed: “In your opinion, is this advertisement (or product brand) enjoyable, likable, appealing?” Responses were on a scale from 1 = not at all to 7 = very. This yielded six items for each of the three ads, 18 items overall, which were summed for an index measure which presented α = .95. Addictiveness indicated that the participant believed that “e-cigarettes are less addictive than cigarettes,” with a 5-point response strongly disagree to strongly agree (Choi & Forster, 2013). Ever smoked is having smoked 100 cigarettes in lifetime (1 = yes, 0 = no), from the Behavioral Risk Factor Surveillance System (CDC, 2009). Current use was also assessed (now smoking every day, some days, or not at all). Alt. Tobacco was a score for how many other tobacco products had ever been used. This was measured by an inventory of five products: hookah, pipe, cigars, snuff, and chewing tobacco. Responses were on a 1–4 scale, including Never, Tried once or twice, Some days, Every day. A summed index was computed, which presented α = .74. Tried E-cigs indicated that the participant had tried an e-cigarette (1 = yes, 0 = no). The three TRA items were measured using best practice recommendations (Ajzen, 2011). Intention is the likelihood of using an e-cigarette in the future. We asked “How likely do you think it is that you would use an e-cigarette in the not-too distant future, say in the next six months?” Responses ran a 7-point scale from absolutely not, very unlikely, unlikely, maybe yes maybe no, likely, very likely and to absolutely yes. Attitude was a positive appraisal of the e-cigarette as a new way of smoking. It included three items with 5-point responses (strongly disagree to strongly agree): “Use of e-cigarettes should be legal for adults; Ecigarettes are a big step forward; E-cigarettes are a more modern way of using tobacco.” This measure was summed across the three items for an index that presented α = .62. Norms was a positive perception that significant others would be approving of e-cigarette use. The was measured with three pairs of items, all with 5-point responses (strongly disagree to strongly agree): “It would be acceptable to my closest friends (most people I know, closest family members) if I used ecigarettes; when it comes to things like e-cigarettes it is important for
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
me to follow the wishes of my closest friends (most people I know, closest family members).” This measure was computed as the sum of the products of the paired statements for an index that presented α = .84. 2.3. Data analysis
Table 2 Hierarchical regression on Behavioral Intention (N = 296). Block
IVs
B
SE
β
t
p
1
Attitudes Norms Attitudes Norms Addictiveness Appeal Attitudes Norms Addictiveness Appeal Ever Smoked Alt. Tobacco Tried E-cigs
.07 .01 .03 .01 .16 .01 .03 .01 .13 .01 1.21 .08 1.13
.04 .01 .04 .01 .08 .01 .03 .01 .06 .01 .25 .03 .17
.13 .16 .04 .11 .13 .20 .06 .02 .10 .14 .24 .14 .36
2.11 2.47 0.67 1.69 2.14 3.32 1.10 0.36 2.09 2.91 4.82 2.80 6.65
.035 .014 .510 .091 .033 .001 .273 .715 .038 .004 .000 .005 .000 Adj. R2
2
All measures were coded so as to be consistent such that higher values indicate more positive orientations toward e-cigarettes and the likelihood of their use. Descriptive analyses used appropriate measures of central tendency and dispersion. Cronbach's alpha was used to assess index reliability. Paired t-tests were used to compare ad appeal scores. Pearson's r and eta were used for associations with interval and nominal variables, respectively. Hypothesis testing was accomplished through a hierarchical multiple OLS regression. All analyses were conducted using Stata 13.
79
3
ΔR2
F(df)
p
.061
9.5 (2,293)
.000
.051
8.4 (2, 291)
.000
.311 .410
51.8 (2,288) 30.2 (7,288)
.000 .000
3. Results Descriptive statistics are reported in Table 1. The sample was 38% male. Only 7.5% of the respondents reported having smoked 100 cigarettes in their lifetimes, with 5.8% reporting daily or regular smoking and 1.7% having quit. The mean score on Alt. Tobacco was 7.39 (range 5–18) with low values indicating less use. Further examination showed that most respondents had at some time tried at least one of the five: 70% had tried hookah at least once, 47% cigar, and about 15–18% pipe, snuff or chew. On Tried E-cigs, 22% reported having had tried them and 4% were regular users. The mean score on Addictiveness was 3.22 (range 1–5). Further examination showed that 19% believed or strongly believed that e-cigarettes were as addictive as cigarettes, 49% were uncertain, and 32% believed or strongly believed that e-cigarettes were not as addictive as cigarettes. Prior to computing the overall Appeal score, the three ads were compared. Paired t-tests showed that the ad for blu was significantly more appealing than the other two: blu M(SD) = 20.0 (8.1), Mystic 15.4 (6.7), Njoy 15.9 (8.4), blu-Mystic t(295) = 12.8 p b .01, blu-Njoy t(295) = 8.4 p b .01. Correlations among the three appeal scores ranged from .48 to .67 (p b .01). Each appeal score was similarly correlated with Intention, ranging from .20 to .24 (p b .01). The overall similarities supported combining the items to the single measure Appeal. Associations among the variables are also reported in Table 1. Of note, Intention had significant correlations with all of the other variables, with the tobacco use items presenting the strongest coefficients. Associations among the independent variables were also consistent. Tobacco use variables were all significantly associated. Norms and Attitude were significantly associated, and Appeal was significantly associated with Attitude and the tobacco use variables, except Ever smoked. The regression analysis was configured in three steps to first test the TRA variables, then Appeal and Addictiveness, and finally the tobacco use items. Results are presented in Table 2. In the first block Attitude and Norms were each independently positively associated with Intention (hypothesis 1). In the second block Addictiveness and Appeal were each
independently positively associated with Intention (hypotheses 2 and 3), while the effect of Attitude and Norms were both controlled (research question 1). In the third block, each of the tobacco use variables was independently positively associated with Intention (hypothesis 4), while Addictiveness and Appeal remained significant (research question 2). 4. Discussion The first hypothesis was partially supported. Attitude and Norms were both independently associated with Intention, but only in the first block of the analysis model. Neither of the variables presented strong effect sizes and the R2 for the block was modest. This finding suggests that attitudes and norms may play a role in the use of e-cigarettes but are likely not as central as in other behaviors in which studies have found greater variance explained (Crano & Prislin, 2006). The first research question was positively affirmed as Attitude and Norms were both controlled in the second block when Addictiveness and Appeal were added. Despite the partial support, it is worth noting that one of the practical features of the TRA is its demonstration of potential leverage points that might be used in persuasive messaging. The associations among Appeal, Attitudes and Norms suggest that marketing messages may have taken this approach. Formal content analyses of e-cigarette marketing and anecdotal commentaries have highlighted the observation that these advertisements do include messages promoting attitudes of product superiority (over cigarettes) and defiance of social norms against smoking (Cobb, Brookover, & Cobb, 2015; Elliott, 2012, 2013; Grana & Ling, 2014; Huang, Kornfield, Szczypka, & Emery, 2014; Kurutz, 2013; Paek, Kim, Hove, & Huh, 2014; Richardson, Ganz, Stalgaitis, Abrams, & Vallone, 2014). These leverage points may also be effective in developing counter-marketing strategies. The second and third hypotheses were both supported. Addictiveness and Appeal were both positively associated with Intention. Also, the second research question was negative, as the effects of Addictiveness and Appeal were not controlled by the tobacco use variables. Note that
Table 1 Descriptives: Reliabilities, range, means, standard deviations, correlations (N = 296).
1. Intention 2. Sex 3. Attitude 4. Norms 5. Ever smoked 6. Alt. tobacco 7. Tried E−cigs 8. Addictiveness 9. Appeal
α
Range
M(SD)
1.
– – .62 .84 – .74 – – .95
1–7 1=M 3–15 3–75 1 = Yes 5–18 1 = Yes 1–5 7–126
1.9 (1.32) .38 (.49) 6.9 (2.27) 29.93(15.37) .075 (.26) 7.39 (2.36) .22 (.42) 3.22 (1.01) 51.34 (19.33)
– .16⁎⁎ .20⁎⁎ .22⁎⁎ .46⁎⁎ .37⁎⁎ .55⁎⁎ .21⁎⁎ .27⁎⁎
Reporting η for binary variables. ⁎ p b .05. ⁎⁎ p b .01.
2. – −.05 −.08 .10 .47⁎⁎ .19⁎⁎ −.08 .02
3.
4.
5.
6.
7.
8.
9.
– −.10 .12⁎
– .19⁎⁎
–
–
.44⁎⁎ −.07 −.03 .14⁎ .27⁎⁎ .39⁎⁎
– .15⁎ .18⁎⁎ .23⁎⁎ .31⁎⁎ .25⁎⁎
–
.24⁎⁎ .44⁎⁎ −.10 .09
–
.39⁎⁎ −.09 .18⁎⁎
80
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81
Appeal and Addictiveness are themselves modestly correlated (r = .19 p b .01). Taken together, these findings suggest a potentially important aspect of the marketing information environment surrounding ecigarettes. While directional effects could not be assessed in this study design, there was a set of mutually reinforcing associations among ecigarette behavior, the perceived quality of marketing messages, and a key belief about addictiveness. This finding suggests the potentially central role of addictiveness beliefs in the set of mechanisms influencing the attractiveness of e-cigarettes. These findings have relevance in the context of other work that has examined motivations for e-cigarette initiation and associated perceptions of low potential harm (Choi & Forster, 2013; Goniewicz, Lingas, & Hajek, 2012; Kim et al., 2013; Pepper et al., 2014). Finally, the third hypothesis was supported. Each of the tobacco use measures was independently positively associated with Intention, and they collectively added about three-quarters of total variance explained. The three are themselves correlated, and other studies have found that e-cigarette use as well as use of alternate tobacco products is highest among cigarette smokers (Dawkins, Turner, Roberts, & Soar, 2013; Etter, 2010; King, Alam, Promoff, Arrazola, & Dube, 2013). These represent the constellation of nicotine addictive behaviors, with e-cigarettes the newest addition. The manner in which e-cigarettes are finding a place in this behavioral set is of considerable interest in public health. The potential for the e-cigarette to become a normalized alternate tobacco use behavior on college campuses is of special interest because of the now widespread presence of hookah bars (Barnett et al., 2013). 4.1. Limitations This pilot study relied on a limited convenience sample of students from a single college campus, limiting generalizability. Prior exposure to e-cigarette advertisements in general or these specific ads was not assessed, which presented a potential confound. Because the study was cross-sectional causal directionality could not be determined, making it possible that previous experiences with e-cigarettes precipitated the appeal of the ads. Because the study was retrospective, it relied on recall and did not actually test behavioral outcomes. In terms of measurement, the effect of Attitude was limited, which may have been due to the low reliability of the scale. And participants completed the questionnaire in an uncontrolled environment that might have been subject to unknown biasing factors. 4.2. Conclusions This is among the first published studies to examine the effect of ecigarette advertising. The findings support the argument that e-cigarette advertising is potentially promoting the uptake of e-cigarettes and is doing so in addition to current smoking and alternate tobacco use. This has been show to be true for the marketing of cigarettes, and advertising of those products is now regulated (Biener & Siegel, 2000). These findings should inform the ongoing discussion concerning the regulation of ecigarette advertising. Finally, this study examined college students. As described above, this is a component of society that is strongly desired by marketers. The e-cigarette may be especially problematic for this population in conjunction with the rapid establishment and general acceptance of hookah lounges. This new path to nicotine exposure and dependence may also have the potential to fuel a resurgence of cigarette smoking. Health professionals on college campuses should be aware of the uptake of these devices among students and see the e-cigarette as yet another dimension in the web of avenues to nicotine exposure and addiction. The ability of campus health workers to effectively reach students is significant and provides an opportunity to better understand motivations for the use of e-cigarettes and to test strategies to help emerging adults avoid this pathway to nicotine addiction.
Role of funding sources None.
Contributors Craig W. Trumbo and Se-Jin Kim have both contributed significantly to, and approve of this final manuscript. Both authors contributed to the conceptualization of the current manuscript. Kim supervised the data collection. Both authors collaborated on all analyses. Trumbo drafted the Introduction and Methods. Both authors contributed to drafting the Results and Discussion, and both contributed to revisions.
Conflict of Interest Both authors declare that they have no conflicts of interest.
References Agaku, I. T., & Ayo-Yusuf, O. A. (2013). The effect of exposure to pro-tobacco advertising on experimentation with emerging tobacco products among U.S. adolescents. Health Education & Behavior, 41, 275–280. http://dx.doi.org/10.1177/ 1090198113511817. Ajzen, I. (2011). Constructing a TpB questionnaire: Conceptual and methodological considerations. Amherst, MA: University of Massachusetts (Available at http://www-unix.oit. umass.edu/~aizen/tpb.html (Accessed 10/012014)). Ayers, J. W., Ribisl, K. M., & Brownstein, J. S. (2011). Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. American Journal of Preventive Medicine, 40, 448–453. http://dx.doi.org/10. 1016/j.amepre.2010.12.007. Barnett, T. E., Smith, T., He, Y., Soule, E. K., Curbow, B. A., Tomar, S. L., et al. (2013). Evidence of emerging hookah use among university students: A cross-sectional comparison between hookah and cigarette use. BMC Public Health, 13, 302. http://dx.doi.org/ 10.1186/1471-2458-13-302. Biener, L., & Siegel, M. (2000). Tobacco marketing and adolescent smoking: More support for a causal inference. American Journal of Public Health, 90(3), 407–411. http://dx.doi. org/10.2105/AJPH.90.3.407. blu (2014). Stephen Dorff's rise from the ashes—Brought to you by blu cigs. Available at https://www.youtube.com/watch?v=VZishwAt_RM (Accessed 10/01/2014). CDC (2009). Behavioral risk factor surveillance system survey questionnaire. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention (Available at http://www.cdc.gov/brfss (Accessed 10/01/2014)). Census, U. S. (2012). CPS data on school enrollment. Washington, DC: US Department of Commerce (Available at http://www.census.gov/hhes/school/data/cps/2012/ tables. html (Accessed 10/01/2014)). Choi, K., & Forster, J. (2013). Characteristics associated with awareness, perceptions, and use of electronic nicotine delivery systems among young US Midwestern adults. American Journal of Public Health, 103, 556–561. http://dx.doi.org/10.2105/AJPH. 2012.300947. Cobb, N. K., Brookover, J., & Cobb, C. O. (2015). Forensic analysis of online marketing for electronic nicotine delivery systems. Tobacco Control, 24(2), 128–131 (http://10.1136/ tobaccocontrol-2013-051185). Conner, M., & Armitage, C. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1429–1464. http://dx.doi.org/10.1111/j.1559-1816.1998.tb01685.x. Cooke, R., & French, D. P. (2008). How well do the theory of reasoned action and theory of planned behaviour predict intentions and attendance at screening programmes? A meta-analysis. Psychology & Health, 23, 745–765. http://dx.doi.org/10.1080/ 08870440701544437. Crano, W., & Prislin, R. (2006). Attitudes and persuasion. Annual Review of Psychology, 57, 345–375. http://dx.doi.org/10.1146/annurev.psych.57.102904.190034. Dawkins, L., Turner, J., Roberts, A., & Soar, K. (2013). ‘Vaping’ profiles and preferences: An online survey of electronic cigarette users. Addiction, 108(6), 1115–1125. http://dx. doi.org/10.1111/add.12150. Duke, J. C., Lee, Y. O., Kim, A. E., Watson, K. A., Arnold, K. Y., Nonnemaker, J. M., et al. (2014). Exposure to Eeectronic cigarette television advertisements among youth and young adults. Pediatrics. http://dx.doi.org/10.1542/peds. 2014-0269. Eagly, A., & Chaiken, S. (1993). The psychology of attitudes. Ft. Worth: Harcourt Brace Jovanovich. Elliott, S. (2012, Dec. 6). Campaigns for e-cigarettes borrow from tobacco's heyday. The New York Times, B1. Elliott, S. (2013, Aug. 30). E-cigarette makers' ads echo tobacco's heyday. The New York Times, B1. Emery, S. L., Vera, L., Huang, J., & Szczypka, G. (2014). Wanna know about vaping? Patterns of message exposure, seeking and sharing information about e-cigarettes across media platforms. Tobacco Control, 23, iii17–iii25. http://dx.doi.org/10.1136/ tobaccocontrol-2014-051648. Esterl, M. (2013). E-Cigarettes fire up investors, regulators. The Wall Street Journal, 261(134), pA1–A2 (Available at http://online.wsj.com/articles/SB10001424127887324904004 578535362153026902 (Accessed 10/01/2014)). Etter, J. F. (2010). Electronic cigarettes: A survey of users. BMC Public Health, 10, 231 (doi: 1471-2458-10-231). Fairchild, A. L., Bayer, R., & Colgrove, J. (2014). The renormalization of smoking? Ecigarettes and the tobacco "endgame". New England Journal of Medicine, 370(4), 293–295. http://dx.doi.org/10.1056/NEJMp131394. Fishbein, I. (1967). Readings in attitude theory and measurement. New York: John Wiley and Sons.
C.W. Trumbo, S.-J.'S.' Kim / Addictive Behaviors 46 (2015) 77–81 Goniewicz, M. L., Lingas, E. O., & Hajek, P. (2012). Patterns of electronic cigarette use and user beliefs about their safety and benefits: An Internet survey. Drug and Alcohol Review, 32(2), 133–140. http://dx.doi.org/10.1111/j.1465-3362.2012.00512.x. Grana, R. A., & Ling, P. M. (2014). “Smoking revolution”: A content analysis of electronic cigarette retail websites. American Journal of Preventive Medicine, 46, 395–403. http://dx.doi.org/10.1016/j.amepre. 2013.12.010. Gray, E. (2014). Industry is winning the e-cig regulation battle. Time Magazine (Available at http://time.com/2857718/industry-is-winning-the-e-cig-regulation-battle (Accessed 10/01/2014)). Hackman, C. L., & Knowlden, A. P. (2014). Theory of reasoned action and theory of planned behavior-based dietary interventions in adolescents and young adults: A systematic review. Adolescent Health, Medicine and Therapeutics, 5, 101–114. http://dx.doi.org/ 10.2147/AHMT.S56207. Huang, J., Kornfield, R., Szczypka, G., & Emery, S. L. (2014). A cross-sectional examination of marketing of electronic cigarettes on Twitter. Tobacco Control, 23, 26–30. http://dx. doi.org/10.1136/tobaccocontrol-2014-051551. Kelly, K. J., Slater, M. D., & Karan, D. (2002). Image advertisements' influence on adolescents' perceptions of the desirability of beer and cigarettes. Journal of Public Policy & Marketing, 21, 295–304 (doi: http://dx.doi.org/10.1509/jppm.21.2.295.17585). Kim, A. E., Lee, Y. O., Shafer, P., Nonnemaker, J., & Makarenko, O. (2013). Adult smokers' receptivity to a television advert for electronic nicotine delivery systems. Tobacco Control. http://dx.doi.org/10.1136/tobaccocontrol-2013-051130. King, B. A., Alam, S., Promoff, G., Arrazola, R., & Dube, S. R. (2013). Awareness and ever-use of electronic cigarettes among U.S. adults, 2010–2011. Nicotine & Tobacco Research, 15(9), 1623–1627. http://dx.doi.org/10.1093/ntr/ntt013. Koch, W. (2013, Sept. 3). Big tobacco back in TV viewers' faces as FDA cuts in. USA TODAY, 1A (Available at http://www.usatoday.com/story/news/nation/ 2013/09/03/bigtobacco-tv-ecigarette-ads/2608427 (Accessed 10/012014)). Kurutz, S. (2013, Sept. 8). Smoking is back, without the stigma. The New York Times, E1. Mistic (2014). Mistic electronic cigarettes: Best taste, best value. Available at https://www. youtube.com/watch?v=bTXY9ntYJvo (Accessed 10/01/2014) Njoy (2014). Relax … It's Courtney Love! Available at https://www.youtube.com/watch ? v=_SFs761eEFk (Accessed 1/01/2014)
81
Noble, S. M., Haytko, D. L., & Phillips, J. (2009). What drives college-age Generation Y consumers? Journal of Business Research, 62, 617–628. http://dx.doi.org/10.1016/j. jbusres.2008.01.020. Paek, H. -J., Kim, S., Hove, T., & Huh, J. Y. (2014). Reduced harm or another gateway to smoking? Source, message, and information characteristics of e-cigarette videos on YouTube. Journal of Health Communication, 19, 545–560. http://dx.doi.org/10.1080/ 10810730.2013.821560. Pepper, J. K., Emery, S. L., Ribisl, K. M., Southwell, B. G., & Brewer, N. T. (2014). Effects of advertisements on smokers' interest in trying e-cigarettes: The roles of product comparison and visual cues. Tobacco Control, 23, iii31–iii36. http://dx.doi.org/10.1136/ tobaccocontrol-2014-051718. Plotnikoff, R. C., Costigan, S. A., Karunamuni, N., & Lubans, D. R. (2013). Social cognitive theories used to explain physical activity behavior in adolescents: A systematic review and meta-analysis. Preventive Medicine, 56, 245–253. http://dx.doi.org/10. 1016/j.ypmed.2013.01.013. Pokhrel, P., Little, M. A., Fagan, P., Muranaka, N., & Herzog, T. A. (2014). Electronic cigarette use outcome expectancies among college students. Addictive Behaviors, 39, 1062–1065. http://dx.doi.org/10.1016/j.addbeh.2014.02.014. Richardson, A., Ganz, O., Stalgaitis, C., Abrams, D., & Vallone, D. (2014). Noncombustible tobacco product advertising: How companies are selling the new face of tobacco. Nicotine & Tobacco Research, 16, 606–614. http://dx.doi.org/10.1093/ntr/ntt200. Setodji, C. M., Martino, S. C., Scharf, D. M., & Shadel, W. G. (2014). Quantifying the persistence of pro-smoking media effects on college students' smoking risk. Journal of Adolescent Health, 54, 474–480. http://dx.doi.org/10.1016/j.jadohealth.2013.09.011. Sutfin, E. L., McCoy, T. P., Morrell, H. E., Hoeppner, B. B., & Wolfson, M. (2013). Electronic cigarette use by college students. Drug and Alcohol Dependence, 131, 214–221. http://dx.doi.org/10.1016/j.drugalcdep.2013.05.001. Trumbo, C. W., & Harper, R. (2013). Use and perception of electronic cigarettes among college students. Journal of American College of Health, 61, 149–155. http://dx.doi.org/10. 1080/07448481.2013.776052. Tyson, M., Covey, J., & Rosenthal, H. E. S. (2014). Theory of planned behavior interventions for reducing heterosexual risk behaviors: A meta-analysis. Health Psychology. http://dx.doi. org/10.1037/hea0000047.
