The Influence of Anti-Smoking Campaign Among University Students
Abstract
Although anti-smoking campaigns were frequently carried out and various anti-smoking messages were delivered through various media but the number of smokers in Indonesia continued to increase. This study investigates how far can anti-smoking messages be effective among university students and how much do they want to quit smoking? Four variables were examined considered to influence the willingness to quit smoking: demographic factors, trust in campaign messages, attitudes, and smoking frequency. This study uses a survey method involving 502 students domiciled in the capital of Jakarta and its surrounding area. Correlation statistics, standard multiple regression, and hierarchical multiple regression were used to analyze the data. According to the findings, demographic characteristics have a substantial impact on smoking cessation message confidence and attitudes toward cessation campaigns. The three demographic characteristics surveyed are also highly connected with the frequency of smoking. Finally, all four variables had a substantial effect on smoking quitting motivation, with smoking frequency having the biggest effect (R2 = 23%), followed by self-confidence and attitude, both of which had a similar effect (R2 = 22%). Finally, demographic factors had the smallest impact.
Introduction
An important component of campaign techniques is evaluation to determine efficacy. Anti-smoking public awareness initiatives are effective if they lower the number of smokers. While the government has implemented several policies to reduce cigarette consumption, such as raising excise taxes, limiting production, advertising, and promotion, the country continues to broadcast cigarette advertisements in the mass media, despite the fact that more than 140 countries have banned all forms of cigarette advertising (Morissan, 2020; Prabandari, 2018).
Anti-smoking campaigns encourage people to change their smoking behaviour by changing their information, attitudes, and actions. Changes in behaviour can help to reduce the prevalence of smoking and ensure that individuals who do not smoke are protected from future harm (secondary prevention). Because the psychological and social processes that drive behaviour change are complex, different theories tend to focus on certain characteristics that are supposed to influence change.
One of these is the health belief model, which depicts numerous risks linked with cigarette dangers while emphasizing the benefits or good of quitting smoking (Siddiqui et al., 2016). According to Siddiqui et al., (2016):
This model is the most commonly used in anti-smoking campaigns. The health belief model outlines two elements that affect individual behavior to preserve their health: I a sense of being threatened by sickness, and (ii) a belief that the advantages of adopting health-protective behavior will outweigh the cost of doing so. (p.7)
Many countries throughout the world have implemented anti-smoking initiatives, and numerous studies have been undertaken to determine the impact of these campaigns on smoker behaviour. Smokers and non-smokers in Massachusetts were both exposed to anti-smoking efforts through the media, according to Biener et al. (2000), but the campaign message was more commonly accepted by non-smokers, smokers who had quit, and smokers who wanted to quit. This demonstrates that only smokers who wish to quit see anti-smoking advertising as a successful campaign, whereas most smokers are aware of the dangers of smoking yet refuse to modify their habits. McVey and Stapleton (2000) researched United Kingdom on the effectiveness of anti-smoking campaigns on television in motivating smokers to quit and preventing former smokers from resuming smoking. They discovered that anti-smoking advertising had a significant negative effect on smoking prevalence in the country. According to Wolburg (2001 as cited in Hong et al., 2013), smoking danger commercials are largely unsuccessful, because the messages frequently increase consumption rather than reduce it, or, to put it another way, the media is the incorrect location to push messages like "don't smoke."
According to de Meyrick (2001), anti-smoking advertising will be more effective if the target audience is younger while gender and age can affect how people react to smoking cessation ads (Chauhan & Sharma, 2017). This is because male and female viewers react differently to the same message. A study by Peters et al. (2005) tried to find a link between racial differences and exposure to smoking cessation ads, but I couldn't find a link between race and smoking cessation ads. Sly et al. (2001) discovered that an anti-tobacco campaign called "Truth" in Florida had not only a direct but also an indirect effect: the influence of the messaging topic and anti- smoking attitude. Farrelly et al. (2002) did an additional study on the Truth campaign and discovered that respondents' exposure to tobacco advertising dramatically impacted their smoking behaviour. Durkin et al., (2009) researched to determine the impact of individual characteristics on the effectiveness of anti-smoking commercials targeted at adults. The findings revealed that three factors influenced advertising messages: the gender, social, and ethnic class of people who got the message in question (Sly et al., 2001, pp. 6-7).
Some research looked at the association between nicotine addiction and age (Li et al., 2015; Park et al., 2012). According to recent study, the likelihood of stopping smoking varies depending on age and nicotine dependence, which is one of the most significant barriers to quitting smoking altogether. A Chinese study found that middle-aged smokers had a higher risk of cigarette dependence than younger or older smokers (Li et al., 2015). The results of a cross-sectional study with 596 Chinese smokers revealed an inverse U association between smoking (nicotine) dependence and age. However, more research is needed to corroborate this finding (Li et al., 2015). Another study shows that demographic variables including age, education, and income have a significant influence on the trust in the contents of campaign messages, and attitudes towards anti-smoking campaigns (Morissan, 2020).
This study tries to back up previous research that found a link between smoking frequency and age, income, and length of university study. Furthermore, it is critical to determine which of the three groups of student smokers, based on age, wealth, and length of study, were the most willing to quit smoking after being exposed to anti-smoking ads.
Research Hypothesis
This study looked at the effects of anti-smoking campaigns on the beliefs and attitudes of university student smokers who had been exposed to them. Anti-smoking themes such as "smoking cause cancer," "smoking causes a heart attack," "smoking kills you," and others were presented in each campaign and through various media. This raises the question of message content belief: to what extent can student smokers trust this anti-smoking message? As a result, this section assesses student smokers' cognitive perceptions of the dangers of smoking.
Smoking cessation campaigns were conducted through a variety of channels, including mass media advertising, posters, placards, and writing on cigarette packets. These were all text, audio, and video formats that were positively captured by the human senses. Or a negative attitude. The purpose of this study is to see how college smokers react to advertising messages. This section assesses smokers' feelings about smoking cessation campaigns.
This study also sought to determine whether students' demographic characteristics have an impact on their smoking habits. Is it true that those who are older, have completed more education (bachelor's, master's, Ph.D.), and have a higher salary are more likely to smoke? Finally, the goal of this study was to see how strong student smokers' willingness to quit was. What are the effects of the previously stated variables on smokers' willingness to quit smoking?
The following issues in this study are based on many of these reasons (Morissan, 2020): (1) How do demographic variables affect beliefs in the content of smoking cessation messages? (2) How do demographic variables affect attitudes towards smoking cessation campaigns? (3) How do demographic variables affect smoking frequency? (4) How do demographic variables, beliefs, attitudes, and smoking frequency together affect smoking cessation? Based on the problem formulation, several hypotheses have been made, as shown below, and a concise model of the relationships between the variables in this study is shown in Figure 1.
H1: Demographic variables significantly influence belief in the contents of anti-smoking messages.
H2: Demographic variables significantly influence attitudes in anti-smoking advertisements.
H3: Demographic variables significantly influence the frequency of smoking.
H4: Beliefs and attitudes significantly influence on smoking frequency.
H5: Demographic variables, beliefs, attitudes, and smoking frequency significantly influence the willingness to quit smoking.
Research Methods
In this study, a survey method was used to distribute questionnaires to collect primary data from smokers of all ages at the university. The survey has open questions and private questions. With a population of over 28 million, the Greater Jakarta region (Jabodetabek region) is Indonesia's most densely populated, fastest growing region and one of the most reported in the media. As a result, smoking cessation campaigns can easily expose residents of the area. Participants were asked if they had been exposed to a smoking cessation campaign before completing the online survey. Only exposed people could fill out the questionnaire. A total of 584 participants participated in this study.
The first part of the survey collected demographic information such as age, gender, income, and study period. Student income is divided into 10 categories (from less than 1 million rupiah per month to over 15 million rupiah per month), and the research period at the university is divided into three categories: bachelor's degree, master's degree and doctoral degree. In this part, smoking frequency (number of cigarettes smoked per day), smoking duration (monthly), and whether respondents learned about the dangers of smoking through the media, and from which media respondents quit smoking campaigns. It also collects information about what you have learned about.
The second section of the survey was used to gauge respondents' trust in the anti-smoking campaign message's content. Smokers' beliefs are gauged by asking them to rate their agreement with four anti-smoking statements on a scale of one to ten: "Smoking can cause cancer," "Smoking can cause a heart attack," "Smoking can cause impotence," and "Smoking can kill you." For the four items, Cronbach's alphas were .90.
The final section of the survey examined respondents' attitudes toward anti-smoking campaigns using the following two statements: "Anti-smoking campaigns exaggerate the dangers of smoking to one's health" and "Anti-smoking advertisements exaggerate the dangers of smoking to others." Respondents who smoked were asked to rate their level of agreement on a scale of 1 (strongly disagree) to 10 (strongly agree) (strongly agree). The Cronbach's alphas for the two items were and respectively .83.
The third section of the survey is intended to determine how serious the respondents are about quitting smoking. "I want to lessen my smoking habits" or "I want to reduce the number of cigarettes I consume every day"; and "I want to stop smoking" were among the four statements presented. Respondents were asked to rate their willingness to quit smoking on a scale of 1 to 10 (strongly disagree to strongly agree). The four objects have Cronbach's alphas of .80.
Data Analysis
To see if demographic characteristics had a significant impact on the content of anti-smoking messages, a statistical study using a standard multiple regression was conducted (smoking causes cancer, heart attacks, kills you, and so on). The same regression test was used to see if demographic variables had a significant impact on attitudes, as well as to see if the smoking frequency was influenced by demographic variables, and to see if the smoking frequency was also driven by belief in content and attitude variables.
In the final stages, this study uses hierarchical multiple regression to determine the impact of beliefs, attitudes, and smoking frequency variables that are directly related to demographic characteristics and willingness to quit smoking. In this case, the willingness to quit smoking is the dependent variable and the participant demographics are the independent variable. Beliefs were classified in block 1, attitudes were classified in block 3, and smoking frequency was classified in block 4. The purpose of this experiment was to determine whether beliefs, attitudes, and smoking frequency could increase or decrease the association between demographics and willingness to quit smoking. For this reason, we recommend that you use the hierarchical multiple regression test to analyze the following models.
Willingness to quit smoking = age, education, income (Block 1)
+ Belief in smoking cessation campaign messages (Block 2)
+ Attitudes towards smoking cessation campaigns (Block 3)
+ Smoking frequency (Block 4)
In this study, all statistical analyzes were performed using IBM Statistical Package for Social Sciences (SPSS) or IBM SPSS Statistics version 25. All reported probabilities (P-values) were two-tailed, and that probabilities of less than 0.05 were considered significant statistically.
Findings
This study employs a sample survey using a structured questionnaire, with active university student smokers as the target respondents. The goal of this study is to see how the numerous elements mentioned above affect the decision to quit smoking. The data collection method yielded a total of 584 respondents of diverse ages, smoking duration, daily cigarette consumption, as well as education and income levels (See Table 1). There were 548 men (94%) and 35 women (6%) among the 584 people in the sample. Female smokers had previously been estimated in very tiny numbers. This is due to Indonesian culture, which still regards smoking as a masculine practice, whereas female smokers are not.
The survey included a sample of 520 respondents after excluding 28 respondents (5.1%) who said they had never been exposed to a smoking cessation program. From TV to billboards, 156 respondents (30%) knew about smoking cessation campaigns from just one media source, while 208 respondents (40%) from one or two media. I knew the campaign. The remaining respondents said they knew about smoking cessation campaigns in more than two media outlets.
The most important factor to judge among respondents is confidence in the health message provided by various media. The belief in smoking cessation messages is determined by responding to five statements regarding threatening smoking cessation messages such as three health hazards (impotence, heart attack, fetal abnormalities) such as "smoking can cause cancer". And "smoking" will kill you. "The highest total score for the belief level variable was 50, the lowest total score was 5, and the average was 35.4 (SD = 10.2). Krombacher alpha for each of the five confidence levels. Krombacher with five points. The alpha of is 0.90, which shows the result of the sample belief level in the threat message of the campaign. Table 2 believes that more than half or the majority of respondents believe that cigarettes can cause these five fatal illnesses, but the majority believe that cigarettes can be fatal. Indicates that it is not.
The goal of this study was to see if demographic factors such as age, education, and income influenced views toward the anti-smoking campaign, which was the dependent variable. To gauge this mindset, two statements were made: "Anti-smoking campaign exaggerates the harms of smoking to others" and "Anti-smoking campaign overstates the dangers of smoking to others." Table 3 shows the findings of the sample's feedback. (M = 17.1, SD = 5.3) The lowest total score was 2 and the highest was 20.
Smokers' willingness to quit smoking is judged by five statements: "Smoking disturbed my health"; "Smoking affected my economy"; "I want to cut down my smoking habits"; "I want to cut down the number of cigarettes I suck every day"; "I want to quit smoking" (See Table 4). The average total score for willingness to quit smoking was 32 (SD = 10.6), with the greatest total score being 50 and the lowest being 5.
Belief in Campaign Contents
Prior to playing out a relapse investigation, it is important to play out some supposition testing as needed by the relapse test. The supposition test considers the negligible example size and information ordinariness in both numerous and progressive relapse tests. As far as test size, Green (1991) states that the base example size for testing a general model is 50 + 8k, where k is the quantity of indicator factors. Six elements can be named indicators in this review, bringing about an example size of 50 + 8 (6) = 98. This review had 520 members, demonstrating that the example size was proper.
Dependent samples should be distributed on a regular basis, and the method used to check for outliers is to remove 11 outliers from the remaining 502 respondents to create a boxplot. .. Since the independent variable is not a mixture of other independent variables, the singularity assumption is also satisfied. Finally, the correlation test shows that there are no highly correlated independent variables, indicating that the multicollinearity condition has been met.
The relationship model was significant in the regression calculations, although the three predictors (age, education, and wealth) combined only contribute 2.4 percent to the belief level variance (R2 = 0.024, F(3, 650) = 5,622,<0.05). The most significant predictor was education, which contributed 12.3% to the trust level variance (β = 0.123,<0.05), while the other two variables weakened the relationship: age did not contribute significantly (β = -0.054, = 0.167), and income level did not contribute significantly (β = 0.025, = 0.547).
Because age and profits elements have been now no longer statistically tremendous (for the regression version equation are expecting believe in anti-smoking message Y1 = β0 + β1X1 + … + βpXp + ε) most effective training became used within side the regression version equation Y1 = 29 + 1.forty four to are expecting religion in anti-smoking messages (training). This shows that for each diploma of training gained, believe in anti-smoking messages will growth with the aid of using 1.forty four. Understandably, training degree had a reasonably sturdy and tremendous effect on believe on this dating version due to the fact people with better training might have extra possibilities to get right of entry to records, mainly via mass media in which anti-smoking campaigns and different records have been displayed.
Attitudes towards Campaigns
The disposition toward against smoking projects, the second reliant variable, had a solid connection with the three segment factors. Albeit the three indicator factors (age, training, and abundance) all in all contribute simply 6.2 percent to the mentality variation (R2 = 0.062, F(3, 650) = 14,995, p<0.05), relapse estimations uncovered that this affiliation model was critical with a relapse coefficient R = 0.25. The main indicator variable is age, which represents 24.1 percent of the demeanour fluctuation (β = 0.241, p<0.05), though the other two factors will quite often debilitate the relationship, with schooling not contributing essentially (β = - 0.023, p = 0.58), and incoordination likewise not contributing altogether (β = - 0.023, p = 0.58), so does the pay level (β = - 0.042, p = 0.31). The relapse estimation uncovered the accompanying condition Y2 = 21.6 – 0.16 for the relapse model condition to foresee disposition toward crusade Y2 = β0 + β1X1 + … + βpXp + ε, and as training and pay factors were not genuinely huge, just schooling was gone into the situation (age). This proposes that for each unit (year) old enough acquired, perspectives toward hostile to smoking efforts will decay by 0.16.
Smoking Frequency
To see if age, education level, and wealth have a significant impact on smoking frequency, researchers performed multiple regression analyses. The regression results uncovered that the three segment factors have a critical impact when joined, notwithstanding, the three indicators just record for 6.4 percent of the variety in smoking recurrence (R2 = 0.064, F (3, 650) = 15.408, p<0.01).
The Pearson test uncovered that age has the most grounded affiliation and is measurably huge (r = 0.231, p<0.01); pay likewise has a critical relationship yet with a powerless connection (r = 0.076, p<0.05); and instruction has no impact on smoking recurrence (r = 0.041, p = 0.289). Smoking recurrence could be anticipated utilizing the condition Y3 = 3.552 + 0.20 (age) + 0.23 (instruction) when non-huge factors (schooling) were taken out from the situation (pay). This implies that assuming any remaining variables stay steady, the quantity of cigarettes smoked each year increments by 0.2, and smoking recurrence increments by 0.23 for each extended period old enough increment.
Belief, attitude, and smoking frequency are intervening variables in this study, which are positioned between demographic variables and a desire to quit smoking. Multiple regression tests were used to determine the direct effect of demographic variables on beliefs and attitudes, as well as the direct relationship of demographic variables to smoking frequency, and all of these relationships were significant but at a weak correlation level. The next step was to determine how the relationship of trust and attitude with smoking frequency was determined.
Indeed, even in a powerless affiliation, the factors of confidence in enemy of smoking messages and disposition toward hostile to smoking efforts substantially affected smoking recurrence. Independently, one's degree of trust extensively affects the quantity of cigarettes smoked day by day. A Pearson connection investigation uncovered a generous positive connection among trust and smoking recurrence, with r (651) = 0.25 and p0.01 separately (2-followed). Likewise, there was a considerable yet bad association between smoking recurrence and perspectives toward hostile to smoking efforts, r (651) = - 0.11, p<0.01 (2-followed). Utilizing various relapses, the trust and mentality factors both had a critical association with smoking recurrence, contributing 7.5 percent to the smoking recurrence variety. A critical aggregate impact between trust, mentality, and smoking recurrence was determined utilizing different straight relapse (F (2, 651) = 27.67, p<0.001, R2 = 0.075). Every indicator variable was researched further, and the proposed model uncovered that trust (t = 6.83, p0.01) and mentalities (t = - 3.0, p<0.05) were critical indicators.
The willingness to Quit Smoking
This review expects to find which job conviction, disposition, and smoking recurrence play in the connection between segment attributes and readiness to stop smoking utilizing various progressive relapse tests. The reliant variable was an ability to stop smoking, and the segment variable was the autonomous variable in block 1, trailed by the trust variable in block 2, the mentality variable in block 3, and the smoking recurrence in block 4. (See Table 5,6). The objective of the review was to check whether trust, disposition, and smoking recurrence reinforced or debilitated the relationship between segment attributes and the craving to stop smoking. The relationship model, as introduced in the outline model box, yielded the accompanying ends dependent on the aftereffects of a few various levelled relapse tests. In block 1, segment factors contributed essentially to the relapse model, F (3, 650) = 2.987, p<0.05), with relapse coefficient esteems R = 0.114 and R2 = 0.013, inferring that segment factors represent 1.3 percent of the fluctuation in the craving to stop smoking.
In block 2, putting accept (believe) moved forward the affiliation by 19.6%, with a relapse coefficient of R = 0.46 and R2 = 0.219, inferring that the believe variable accounts for around 22% of the smoking recurrence variety. F (4, 649) = 44,854, p<0.001). The believe variable had a considerable affect on the relapse demonstrate. The affiliation quality expanded by 0.2 percent when the state of mind variable was included within the demonstrate within the third step. With a relapse coefficient esteem of R = 0.46 and R2 = 0.221, the state of mind variable contributed essentially to the relapse demonstrate, F (5, 648) = 36,171 p<0.001, suggesting that attitude accounted for 22.1 percent of the change within the crave to stopped smoking. The connect fortified 0.7 percent when the smoking recurrence variable was included, with a relapse esteem of R = 0.47 and R2 = 0.228, indicating that smoking recurrence accounts for over 23 percent of the fluctuation within the want to stopped smoking.
Discussion and Conclusion
Agreeing to measurable investigation, statistic characteristics affected believe in anti-smoking message campaigns. The relapse investigation uncovered that this affiliation show was critical, be that as it may, the three indicator variables (age, instruction, and riches) together as it were contributed 2.4 percent to the believe change (R2 = 0.024, F (3, 650) = 5,622, p<0.05). The three statistic factors were moreover found to have a considerable connect with anti-smoking campaign opinions. The relapse calculations uncovered that this affiliation demonstrate is noteworthy, with a relapse coefficient R = 0.25, in any case all three indicator factors (age, instruction, and riches) together as it were contribute 6.2 percent to demeanour fluctuation (R2 = 0.062, F (3, 650) = 14,995, p<0.05). Moreover, smoking recurrence or the number of cigarettes smoked each day encompasses a solid relationship with the three statistic characteristics explored. The comes about of the calculations uncovered that the three statistic factors.
Although there was a weak association, the smoking frequency was influenced by trust in anti-smoking messages and attitudes toward anti-smoking campaigns. The results of the multiple linear regression calculations revealed that trust, attitude, and smoking frequency all had a significant collective influence (F (2, 651) = 27.67, p<0.001, R2 = 0.075). The trust and attitude factors were found to have a substantial connection with smoking frequency, each accounting for 7.5 percent of the variation in smoking frequency. Finally, the impact of the four variables evaluated in this study: demography, beliefs, attitudes, and smoking frequency on the desire to quit smoking revealed that the four variables had a significant impact on the desire to quit smoking.
A few highlights of this study about take after those of Chauhan and Sharma (2017) and Li et al., (2015), all of which shown that statistic factors such as sex and age impacted the audience's reaction to anti-smoking promoting. There was too a relationship between nicotine enslavement and age in a few thinks about (Li et al., 2015; Morissan, 2020). Indeed in spite of the fact that the current ponder did not look at sex impact on message substance, convictions, or states of mind toward anti-smoking campaigns due to the generally little number of female smokers in Indonesia due to social judgments that seen adversely ladies who smoke, it was reliable with the three past considers.
Similarly, Durkin et al. (2009) discovered that three individual characteristics, namely the gender, social, and ethnic class of people who received the message in question, influenced the effectiveness of message content in anti-smoking commercials. Even though contemporary study focuses on the concept of 'income' rather than 'social class,' income is a determining factor in identifying one's social class. The idea of "social class" explored by Durkin et al., (2009) was substituted in the current study with income variables that were demonstrated to affect the effectiveness (trust and attitude) of anti-smoking advertising messages.
Based on the five hypotheses given before, the following conclusions can be drawn: (1) Demographic variables (age, education, and income) have a significant impact on trust in anti-smoking message content; (2) Demographic variables have a significant impact on attitudes toward anti-smoking campaigns; (3) Demographic variables have a significant impact on smoking frequency; (4) There is a significant effect of trust and attitudes on smoking frequency.
The four factors examined specifically demography, convictions, states of mind, and smoking recurrence, all had a critical effect on the want to stopped smoking, with smoking recurrence having the most prominent impact (R2 = 22%), taken after by convictions and demeanours, which had about rise to commitments (R2 = 21%), and at last, the statistic variable, which had the littlest affect (R2 = 19%).
Finally, the value of this study is that it assesses tobacco risk perceptions and identifies socioeconomic variables among a new demographic, as no other study has done so with the Indonesian community. This contribution is highlighted in the current manuscript.
References
Biener, L., McCallum-Keeler, G., & Nyman, A. L. (2000). Adults’ response to Massachusetts anti-tobacco television advertisements: Impact of viewer and advertisement characteristics. Tobacco Control, 9(4), 401–407. DOI:
Chauhan, A., & Sharma, R. (2017). Impact of Anti Smoking Campaigns on Youth. Procedia Computer Science, 122, 941–948. DOI:
de Meyrick, J. (2001). Forget the “blood and gore”: An alternative message strategy to help adolescents avoid cigarette smoking. In Health Education (Vol. 101, Issue 3, pp. 99–108). DOI:
Durkin, S. J., Biener, L., & Wakefield, M. A. (2009). Effects of different types of antismoking ads on reducing disparities in smoking cessation among socioeconomic subgroups. American Journal of Public Health, 99(12), 2217–2223. DOI:
Farrelly, M. C., Healton, C. G., Davis, K. C., Messeri, P., Hersey, J. C., & Haviland, M. L. (2002). Getting to the truth: Evaluating national tobacco countermarketing campaigns. American Journal of Public Health, 92(6), 901–907. DOI:
Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate behavioral research, 26(3), 499-510.
Hong, Y. H., Soh, C. H., Khan, N., Abdullah, M. M. Bin, & Teh, B. H. (2013). Effectiveness of Anti-Smoking Advertising: The Roles of Message and Media. International Journal of Business and Management, 8(19). DOI:
Li, H., Zhou, Y., Li, S., Wang, Q., Pan, L., Yang, X., Zhang, N., Jiang, F., Han, M., & Jia, C. (2015). The Relationship between Nicotine Dependence and Age among Current Smokers. Iranian Journal of Public Health, 44(4), 495–500.
McVey, D., & Stapleton, J. (2000). Can anti-smoking television advertising affect smoking behaviour? Controlled trial of the health education authority for England’s anti-smoking TV campaign. Tobacco Control, 9(3), 273–282. DOI:
Morissan, M. (2020). The influence of anti smoking campaign on desire to quit smoking in Indonesia. Profetik: Jurnal Komunikasi, 13(1), 5. DOI:
Park, S., Lee, J. Y., Song, T. M., & Cho, S. I. (2012). Age-associated changes in nicotine dependence. Public Health, 126(6), 482–489. DOI:
Peters, R. J., Kelder, S. H., Prokhorov, A., Amos, C., Yacoubian, G. S., Agurcia, C. A., Murray, N., & Shegog, R. (2005). The relationship between perceived youth exposure to anti-smoking advertisements: How perceptions differ by race. Journal of Drug Education, 35(1), 47–58. DOI:
Prabandari, Y. S. (2018). Pengendalian Rokok di Indonesia Masih Lemah | Universitas Gadjah Mada. https://ugm.ac.id/id/berita/15713-pengendalian-rokok-di-indonesia-masih-lemah
Siddiqui, T. R., Ghazal, S., Bibi, S., Ahmed, W., & Sajjad, S. F. (2016). Use of the Health Belief Model for the Assessment of Public Knowledge and Household Preventive Practices in Karachi, Pakistan, a Dengue-Endemic City. PLoS Neglected Tropical Diseases, 10(11). DOI:
Sly, D. F., Hopkins, R. S., Trapido, E., & Ray, S. (2001). Influence of a counteradvertising media campaign on initiation of smoking: The Florida ‘truth’ campaign. American Journal of Public Health, 91(2), 233–238. DOI:
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
About this article
Publication Date
31 January 2022
Article Doi
eBook ISBN
978-1-80296-122-5
Publisher
European Publisher
Volume
123
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-494
Subjects
Communication, Media, Disruptive Era, Digital Era, Media Technology
Cite this article as:
Morissan, M., Saragih, N., Fasta, F., & Cahyowati, T. D. (2022). The Influence of Anti-Smoking Campaign Among University Students. In J. A. Wahab, H. Mustafa, & N. Ismail (Eds.), Rethinking Communication and Media Studies in the Disruptive Era, vol 123. European Proceedings of Social and Behavioural Sciences (pp. 401-413). European Publisher. https://doi.org/10.15405/epsbs.2022.01.02.34