Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Jan 1.
Published in final edited form as: Addict Behav. 2012 Aug 28;38(1):1493–1498. doi: 10.1016/j.addbeh.2012.08.005

The Role of Cognitive Attributions for Smoking in Subsequent Smoking Progression and Regression among Adolescents in China

Qian Guo a,*, Jennifer B Unger b, Paula H Palmer c, Chih-Ping Chou b, C Anderson Johnson c
PMCID: PMC3493682  NIHMSID: NIHMS404055  PMID: 23017586

Abstract

Previous studies have documented that cognitive attributions are correlated with adolescent smoking. The present study further explored whether cognitive attributions for smoking influenced adolescents’ future smoking behaviors, especially transitions to more advanced stages of smoking.

Participants were 12,382 middle and high school students (48.5% males and 51.5% females) in seven large cities in China. They completed two waves of surveys one year apart. Cognitive attributions for smoking and three smoking behavior outcomes (lifetime smoking, past 30-day smoking, and daily smoking) were assessed. Changes in smoking, including progression from lower stages to higher stages and regression from higher stages to lower stages, over a one-year period, were defined longitudinally. Polychotomous logistic regression was used to examine associations between cognitive attributions for smoking and changes in smoking status over one year, adjusting for demographic characteristics and other plausible confounders.

Seven out of eight cognitive attributions for smoking were associated with subsequent smoking behaviors (p<0.05). Curiosity, autonomy, social image, social belonging, and coping influenced earlier stages of smoking, whereas mental enhancement and engagement influenced later stages of smoking. Curiosity, autonomy, social image, and mental enhancement preceded smoking progression; social belonging prevented smoking regression; and coping and engagement both preceded smoking progression and prevented smoking regression.

This study demonstrates that different cognitive attributions influence smoking at different stages in different ways. These findings could inform smoking prevention and cessation programs targeting Chinese adolescents.

Keywords: Attributions, Smoking, Attribution Theory, Adolescents, China

1. Introduction

Attribution theories describe how people explain the causes of behavior (Heider, 1958; Jones & Davis, 1965; Kelley, 1967). People’s explanations of behavior, called “attributions”, can be classified into categories based on three dimensions: locus of control (internal or external), stability (stable or unstable), and controllability (controllable or uncontrollable) (Weiner, 1986). Cognitive process of inference may lead to biased attributions (Ross, 1977; Ross, Greene, & House, 1977). For example, people are more likely to attribute their own behaviors to situational (external) factors and attribute the behavior of others to personal (internal) factors (Jones & Nisbett, 1971; Monson & Snyder, 1977). However, attribution theories posit that cognitive attributions are important, because they influence people’s self-esteem, expectations, and emotions (Weiner, 1986), which may partially determine their future actions.

Attribution is different from other related constructs such as personal beliefs and outcome expectancies. For example, an individual might have a belief or expectancy that cigarette smoking can help make friends. However, while acknowledging that smoking could possibly help make friends, this individual might perceive that he/she is smoking primarily for other reasons, such as coping with stress. Therefore, a health education message that attempts to dispel the myth that smoking has social benefits would not resonate with this smoker, who believes that he/she smokes to relieve stress.

Attribution research has been conducted to address cigarette smoking, a public health problem worldwide, among adolescents (Allbutt, Amos, & Cunningham-Burley, 1995; Aloise-Young, Hennigan, & Graham, 1996; Barton, Chassin, Presson, & Sherman, 1982; Cronan, Conway, & Kaszas, 1991; Kleinke, Staneski, & Meeker, 1983; McGee & Stanton, 1993; Rugkasa et al., 2001; Sarason, Mankowski, Peterson, & Dinh, 1992; Stanton, Mahalski, McGee, & Silva, 1993; Treacy et al., 2007). However, although previous attribution studies have identified self-reported cognitive attributions for smoking, they did not report whether those attributions were truly correlated with actual smoking behaviors, nor did they examine whether the attributions influenced future smoking behaviors.

We conducted a series of studies to investigate cognitive attributions for smoking among adolescents in China, where one third of smokers in the world reside (Yang et al., 1999). This is the third study in a program of research on attributions and smoking among Chinese adolescents. The first study investigated cognitive attributions for Chinese adolescent smoking, their relative importance, and their associations with actual smoking behaviors (Guo et al., 2010). It used cross-sectional data. The second study investigated whether cognitive attributions for smoking predicted subsequent smoking development and examined hypothesized mediators and moderators (Guo, Unger, Azen, MacKinnon, & Johnson, 2012). This study used two waves of longitudinal data collected one year apart from the same respondents and focused on smoking initiation and progression overall.

In the present analysis, we move further to examine the detailed roles that cognitive attributions for smoking played at different points along the smoking trajectory. We investigate which cognitive attributions preceded changes in stage of smoking, including both progression from lower stages to higher stages and regression from higher stages to lower stages. The detailed pathways are demonstrated in Figure 1. This study addresses some questions raised in the above two studies and provides specific information about cognitive processes relevant to different points on smoking trajectories that may be useful for smoking prevention and cessation programs.

Figure 1.

Figure 1

Open in a new tab

Paths for changes in smoking status from baseline to one-year follow-up (Inline graphic: smoking progressed; Inline graphic: smoking maintained; Inline graphic : smoking regressed)

2. Methods

2.1. Participants

The samples in this study were middle and high school students recruited from seven large cities in different regions of China. All schools in the metropolitan area of each city were stratified by median income in the district (high, medium, low) and by school academic performance (high, medium, low), resulting in nine clusters of schools. One middle school and one high school were randomly selected from each of the nine clusters. One classroom from the 7th and 8th grades in the selected middle schools and one classroom from the 10th and 11th grades in the selected high schools were recruited. In addition, one professional high school was selected from each district, major courses of study within each professional school were randomly selected, and students in these majors were recruited from the 10th and 11th grades. As a result, 14,434 students (93.0% of those who were invited) participated in the study. One year later, 12,382 students (85.8% of the total at baseline) completed a follow-up survey.

2.2. Procedures

Self-administered surveys were conducted twice in 2002 and 2003 respectively among recruited students. Prior to data collection, the informed consent and data collection procedures were reviewed and approved by both the University of Southern California (USC) and Chinese Institutional Review Boards. During the data collection, the procedures were monitored through a three-level quality control system, which included faculty and staff from local universities in the seven Chinese cities, Chinese Centers for Disease Control and Prevention, and USC. More details about the methodology of this study have been reported elsewhere (Johnson et al., 2006).

2.3. Measures

Demographic measures included age, gender, ethnicity, and geographic region. Three measures of smoking behaviors were assessed: lifetime smoking (“Have you ever tried cigarette smoking, even a few puffs?”), past 30-day smoking (“During the past 30 days, on how many days did you smoke cigarettes?” and “During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day?”), and daily smoking (“Have you ever smoked cigarettes daily, that is, at least one cigarette every day for 30 days?”).

Cognitive attributions for smoking were assessed with one multiple-choice question: “I smoke, (or might smoke), because: (circle all that apply)”, with seventeen possible reasons for smoking as response options (e.g., “It makes me look good” and “It helps me forget my problems”). At the analytical stage, these response options were classified into eight categories through exploratory factor analysis, representing eight cognitive attributions: curiosity, coping, social image, social belonging, engagement, autonomy, mental enhancement, and weight control. More details about how the measures for cognitive attributions for smoking have been established and utilized by previous studies, including the detailed procedures of the exploratory factor analysis, have been reported elsewhere (Guo et al., 2010; Guo et al., 2012).

2.4. Statistical Analyses

Frequencies were calculated to describe the demographic characteristics of the sample, both overall and stratified by gender. Chi-square tests were conducted to examine gender differences for each of the variables.

Cross-sectional data were used to define four stages of smoking at baseline and at one-year follow-up, respectively: 1) never smoker (never smoked, even a few puffs), 2) lifetime smoker (smoked, but not during the past 30 days), 3) past 30-day smoker (smoked during the past 30 days, but not daily), and 4) daily smoker (smoked daily during the past 30 days). Longitudinal data were used to create new variables representing dynamic changes in smoking status over a one-year period. The new variables were named and coded based on three factors: smoking status at baseline, change in smoking status over a year, and level of change in smoking status. Based on smoking status at baseline, the new variables were named as: “Never” (for never smokers at baseline), “Lifetime” (for lifetime smokers at baseline), “Month” (for past 30-day smokers at baseline), and “Daily” (for daily smokers at baseline). Based on the changes in smoking status during one-year follow-up, the new variables were coded as: “+” (for students whose smoking had initiated or progressed to a higher stage), “0” (for students whose smoking status had remained at the same stage), and “−” (for students whose smoking had regressed to a lower stage). Lastly, the new variables were coded as: “1” (for students whose smoking had either progressed or regressed to the next higher or lower stage), and “2” (for students whose smoking had progressed or regressed to the adjacent two higher or lower stages). Therefore, for example, if a student was a past 30-day smoker at baseline and became a daily smoker one year later, the new variable representing the change of smoking status was named as “Month” and coded as “+1”. Otherwise, if a student was a daily smoker at baseline but became a lifetime smoker one year later, the new variable was named as “Daily” and coded as “−2”. As a result, four new variables were created, which included Never (with values 0, +1, +2, and +3), Lifetime (with values 0, +1, and +2), Month (with values −1, 0, +1), and Daily (with values −2, −1, and 0). The first letters of these new variables and their corresponding values are presented as the path names in Figure 1 (e.g., n0, l1, m−1, d−2) to facilitate better understanding about the meanings.

After the above four new variables were created, polychotomous logistic regression was used to examine associations between cognitive attributions for smoking and changes in smoking status over one year. In each of the polychotomous logistic regression models, one of the four new variables was the dependent variable, eight smoking attributions were independent variables, and gender, age, ethnicity, geographic region, district economy rank, and school academic rank were covariates.

3. Results

3.1. Demographic Characteristics of the Sample

The sample contained 6,354 females (51.5% of the total) and 5,988 males (48.5% of the total). The distributions of ethnicity (96.1% Han vs. 3.9% others) and geographic region (from 12.0% to 15.8% for seven cities) were not significantly different between genders (p>0.05), but the distribution of age groups was significantly different (p<0.0001). A detailed frequency table describing the demographic characteristics of the sample at baseline and gender differences has been presented elsewhere (Guo et al., 2012).

3.2. Changes in Smoking Status over One Year

As shown in Table 1, the prevalence of lifetime smoking, past 30-day smoking, and daily smoking all increased during the one-year follow-up and among both genders. The percentages of students whose smoking status progressed, maintained, and regressed from baseline to one-year follow-up were 17.0%, 76.3%, and 6.7% respectively. However, among students at higher stages of smoking (such as past 30-day smokers and daily smokers), the percentages of those students whose smoking regressed to lower stages (e.g., 55.4% and 53.8% respectively) were higher than others whose smoking progressed to higher stages (e.g., 11.5%) or remained at the same stages (e.g., 33.1% and 46.2%). In this study, respondents who had ever smoked at baseline could not regress all the way back to never-smoking, and respondents who were already daily smokers at baseline could regress but could not progress further.

Table 1.

Smoking status of the sample and changes in smoking status over one year

All Male Female Gender
n (%) n (%) n (%) Difference
Smoking status at baseline
Never smoker 7801 (63.5) 3182 (53.6) 4596 (72.9) p<0.0001
Lifetime smoker 2976 (24.2) 1680 (28.3) 1283 (20.3)
Past 30-day smoker 1207 (9.8) 829 (14.0) 377 (6.0)
Daily smoker 294 (2.5) 243 (4.1) 51 (0.8)
Smoking status at one-year follow-up
Never smoker 6379 (51.8) 2425 (40.8) 3934 (62.1) p<0.0001
Lifetime smoker 4181 (33.9) 2281 (38.4) 1885 (29.8)
Past 30-day smoker 1310 (10.6) 857 (14.4) 450 (7.1)
Daily smoker 451 (3.7) 383 (6.4) 66 (1.0)
Changes in smoking status over a year
Smoking regressed 824 (6.7) 504 (8.6) 320 (5.1) p<0.0001
Smoking maintained 9319 (76.3) 4168 (70.7) 5121 (81.4)
Smoking progressed 2076 (17.0) 1222 (20.7) 847 (13.5)
Changes in smoking status over a year among baseline never smokers
Path n0 (→ never smoker)1 6314 (81.4) 2396 (76.0) 3899 (85.2) p<0.0001
Path n1 (→ lifetime smoker)1 907 (11.7) 469 (14.9) 435 (9.5)
Path n2 (→ past 30-day smoker)1 451 (5.8) 226 (7.2) 224 (4.9)
Path n3 (→ daily smoker)1 81 (1.1) 61 (1.9) 20 (0.4)
Changes in smoking status over a year among baseline lifetime smokers
Path l0 (→ lifetime smoker)1 2472 (83.2) 1336 (79.8) 1125 (87.8) p<0.0001
Path l1 (→ past 30-day smoker)1 403 (13.6) 272 (16.2) 130 (10.1)
Path l2 (→ daily smoker)1 95 (3.2) 67 (4.0) 27 (2.1)
Changes in smoking status over a year among baseline past 30-day smokers
Path m−1 (→ lifetime smoker)1 667 (55.4) 391 (47.3) 276 (73.2) p<0.0001
Path m0 (→ past 30-day smoker)1 398 (33.1) 308 (37.3) 90 (23.9)
Path m1 (→ daily smoker)1 139 (11.5) 127 (15.4) 11 (2.9)
Changes in smoking status over a year among baseline daily smokers
Path d−2 (→ lifetime smoker)1 106 (36.3) 67 (27.8) 39 (76.5) p<0.0001
Path d−1 (→ past 30-day smoker)1 51 (17.5) 46 (19.1) 5 (9.8)
Path d0 (→ daily smoker)1 135 (46.2) 128 (53.1) 7 (13.7)
Open in a new tab
1

Visually clearer paths are shown in Figure 1.

3.3. Associations between Cognitive Attributions and Changes in Smoking Status over One Year

As shown in Table 2, seven out of eight cognitive attributions for smoking were significantly associated with changes in smoking status one year later: curiosity, autonomy, social image, social belonging, coping, engagement, and mental enhancement. However, the associations varied by baseline smoking status. Among adolescents who had never smoked, curiosity about smoking was positively associated with smoking initiation (OR=2.23; 95%CI=1.65, 3.02). Attempts to show autonomy (OR=1.80; 95%CI=1.05, 3.09), along with curiosity about smoking (OR=1.58; 95%CI=1.04, 2.41), was positively associated with smoking progression to two higher stages. However, none of the attributions were associated with smoking progression to more than two higher stages (p>0.05 for all). Among adolescents who had tried smoking, coping (OR=1.81; 95%CI=1.37, 2.38) and social image (OR=1.85; 95%CI=1.28, 2.69) were positively associated with smoking progression to a higher stage. Among adolescents who had already smoked during the past 30 days, coping (OR=0.55; 95%CI=0.40, 0.75) and social belonging (OR=0.66; 95%CI=0.45, 0.96) were negatively associated with smoking regression to a lower stage. Engagement (OR=1.62; 95%CI=1.02, 2.57) and mental enhancement (OR=2.19; 95%CI=1.26, 3.78) were positively associated with smoking progression to a higher stage. Among adolescents who had smoked daily, engagement was negatively associated with smoking regression back to the previous stage (OR=0.39; 95%CI=0.17, 0.88) or an even lower stage (OR=0.41; 95%CI=0.17, 0.98).

Table 2.

Associations between smoking attributions and subsequent smoking progression and regression1,2

β (se) OR (95% CI)3 p β (se) OR (95% CI)3 p β (se) OR (95% CI)3 p
Among baseline never smokers
Path n1 (→ lifetime smoker)4 Path n2 (→ past 30-day smoker)4 Path n3 (→ daily smoker)4
Curiosity 0.80 (0.15) 2.23 (1.65, 3.02) <0.0001 0.46 (0.21) 1.58 (1.04, 2.41) 0.03 −0.02 (0.68) 0.98 (0.26, 3.75) 0.98
Coping −0.13 (0.18) 0.88 (0.62, 1.25) 0.47 0.14 (0.23) 1.16 (0.74, 1.80) 0.52 −0.42 (0.70) 0.66 (0.17, 2.57) 0.55
Social Image 0.16 (0.29) 1.18 (0.66, 2.10) 0.58 −0.24 (0.42) 0.79 (0.35, 1.80) 0.57 0.81 (0.90) 2.24 (0.38, 13.18) 0.37
Social Belonging 0.12 (0.30) 1.13 (0.63, 2.03) 0.68 0.30 (0.37) 1.35 (0.66, 2.78) 0.41 −0.42 (1.17) 0.66 (0.07, 6.56) 0.72
Engagement 0.04 (0.34) 1.05 (0.53, 2.05) 0.90 0.24 (0.40) 1.27 (0.58, 2.76) 0.55 1.02 (0.94) 2.76 (0.44, 17.39) 0.28
Autonomy 0.31 (0.23) 1.36 (0.86, 2.16) 0.19 0.59 (0.28) 1.80 (1.05, 3.09) 0.03 0.09 (0.79) 1.09 (0.23, 5.17) 0.91
Mental Enhancement −0.10 (0.30) 0.90 (0.50, 1.62) 0.73 0.29 (0.34) 1.34 (0.69, 2.59) 0.38 0.65 (0.84) 1.91 (0.37, 9.94) 0.44
Weight Control 0.31 (0.43) 1.36 (0.59, 3.13) 0.47 0.81 (0.44) 2.25 (0.94, 5.37) 0.07 -- -- --
Among baseline lifetime smokers
Path l1 (→ past 30-day smoker)4 Path l2 (→ daily smoker)4
Curiosity −0.03 (0.12) 0.97 (0.77, 1.23) 0.81 −0.46 (0.25) 0.63 (0.39, 1.02) 0.06
Coping 0.59 (0.14) 1.81 (1.37, 2.38) <0.0001 0.03 (0.29) 1.03 (0.59, 1.83) 0.91
Social Image 0.62 (0.19) 1.85 (1.28, 2.69) <0.001 0.49 (0.37) 1.64 (0.78, 3.41) 0.19
Social Belonging 0.20 (0.21) 1.22 (0.81, 1.82) 0.34 0.22 (0.40) 1.25 (0.57, 2.76) 0.58
Engagement 0.22 (0.21) 1.25 (0.83, 1.87) 0.29 0.37 (0.39) 1.45 (0.67, 3.13) 0.34
Autonomy −0.49 (0.27) 0.61 (0.36, 1.04) 0.07 0.38 (0.42) 1.46 (0.64, 3.35) 0.37
Mental Enhancement −0.05 (0.30) 0.95 (0.53, 1.69) 0.86 0.20 (0.57) 1.22 (0.40, 3.68) 0.73
Weight Control −0.69 (0.49) 0.50 (0.19, 1.30) 0.16 -- -- --
Among baseline past 30-day smokers
Path m−1 (→ lifetime smoker)4 Path m1 (→ daily smoker)4
Curiosity 0.08 (0.15) 1.08 (0.81, 1.45) 0.60 0.09 (0.22) 1.09 (0.71, 1.69) 0.68
Coping −0.60 (0.16) 0.55 (0.40, 0.75) <0.001 0.16 (0.23) 1.17 (0.74, 1.85) 0.50
Social Image −0.05 (0.20) 0.96 (0.64, 1.43) 0.82 0.46 (0.27) 1.59 (0.94, 2.69) 0.08
Social Belonging −0.42 (0.19) 0.66 (0.45, 0.96) 0.03 0.06 (0.25) 1.06 (0.65, 1.75) 0.81
Engagement −0.13 (0.19) 0.87 (0.61, 1.26) 0.47 0.48 (0.24) 1.62 (1.02, 2.57) 0.04
Autonomy −0.09 (0.22) 0.91 (0.59, 1.41) 0.68 0.18 (0.28) 1.20 (0.68, 2.09) 0.53
Mental Enhancement −0.40 (0.25) 0.67 (0.41, 1.09) 0.11 0.78 (0.28) 2.19 (1.26, 3.78) 0.01
Weight Control −0.03 (0.33) 0.98 (0.51, 1.88) 0.94 −0.66 (0.47) 0.52 (0.21, 1.29) 0.16
Among baseline daily smokers
Path d−2 (→ lifetime smoker)4 Path d−1 (→ past 30-day smoker)4
Curiosity 0.20 (0.47) 1.22 (0.49, 3.06) 0.67 0.06 (0.44) 1.06 (0.45, 2.49) 0.90
Coping −0.57 (0.42) 0.57 (0.25, 1.30) 0.18 −0.37 (0.42) 0.69 (0.31, 1.57) 0.38
Social Image 0.38 (0.50) 1.46 (0.55, 3.84) 0.45 0.28 (0.45) 1.33 (0.56, 3.18) 0.52
Social Belonging −0.32 (0.50) 0.72 (0.27, 1.92) 0.51 0.03 (0.45) 1.03 (0.43, 2.50) 0.95
Engagement −0.89 (0.45) 0.41 (0.17, 0.98) 0.05 −0.94 (0.41) 0.39 (0.17, 0.88) 0.02
Autonomy −0.32 (0.61) 0.73 (0.22, 2.38) 0.60 −1.13 (0.55) 0.34 (0.11, 1.02) 0.05
Mental Enhancement −0.51 (0.45) 0.60 (0.25, 1.45) 0.26 0.25 (0.40) 1.28 (0.58, 2.82) 0.54
Weight Control −1.46 (1.06) 0.23 (0.03, 1.84) 0.17 0.56 (0.72) 1.75 (0.43, 7.11) 0.44
Open in a new tab
1

Age, gender, ethnicity, geographic region, district economy rank, and school academic rank are covariates in the models.

2

P values smaller than 0.05 are shown in bold.

3

The reference groups are students whose smoking status remained at the same stages over one year (e.g., on path n0, l0, m0, or d0).

4

Visually clearer paths are shown in Figure 1.

4. Discussion

Our previous studies among Chinese adolescents identified specific cognitive attributions for smoking including curiosity about smoking, coping, social image, social belonging, engagement, autonomy, mental enhancement, and weight control (Guo et al., 2010). Seven out of these eight attributions (exclusive of weight control) were associated with actual smoking behaviors (Guo et al., 2010), and six out of the eight attributions (exclusive of social belonging and weight control) were associated with subsequent smoking initiation and progression one year later (Guo et al., 2012). Certain attributions (e.g., curiosity about smoking, autonomy, coping, social image) were more important at the earlier stages of smoking, whereas different attributions (e.g., engagement, mental enhancement) were more important at more advanced stages of smoking (Guo et al., 2010; Guo et al., 2012). Consistencies and inconsistencies between these findings in the Chinese cultural context and other findings from western cultures are discussed elsewhere (Guo et al., 2010; Guo et al., 2012).

Findings from the present study extend the previous attribution studies by demonstrating a comprehensive profile about how cognitive attributions for smoking influenced smoking at different points along the smoking trajectory in different ways. This study provides new information about which cognitive attributions preceded smoking progression from each of the initial stages to each of the higher stages, and which cognitive attributions prevented smoking from regressing from each of the initial stages to each of the lower stages. Several new questions raised in the previous studies are also addressed in this study.

Social influences have been widely reported as one of the most important reasons for adolescent smoking (Allbutt et al., 1995; Cronan et al., 1991; Rugkasa et al., 2001; Sarason et al., 1992; Stanton et al., 1993; Stanton & Silva, 1993; Treacy et al., 2007). Consistent with previous findings, Guo et al. (2010) found that social image, through which adolescents may want to make friends and obtain respect from friends, and social belonging, through which adolescents may want to maintain friendship and group identity, were both significantly associated with Chinese adolescent smoking (Guo et al., 2010). However, whereas social image was significantly associated with subsequent smoking progression, it was surprising that social belonging was not (Guo et al., 2012), although friend smoking and being offered cigarettes by friends have been widely reported to associate with Chinese adolescent smoking (Chen, Fang, Li, Stanton, & Lin, 2006; Grenard et al., 2006; Hesketh, Ding, & Tomkins, 2001; Zhang, Wang, Zhao, & Vartiainen, 2000; Zhu, Liu, Shelton, Liu, & Giovino, 1996). The present study helps to distinguish the different functions of social image and social belonging attributions in the smoking trajectory. The social image attribution preceded smoking progression among adolescents who were at earlier stage of smoking, whereas the social belonging attribution prevented smoking from regressing among adolescents who were at a more advanced stage of smoking. In other words, adolescents may escalate their smoking to present a social image to others and to make friends. Once they have developed a habit of smoking, it may be difficult for them to quit because they have developed friendships that are based on smoking together and therefore may prefer to continue smoking to maintain their group identity and membership. In Chinese society, sharing and gifting of cigarettes are commonly used as ways to enhance social interactions; refusing to accept a cigarette offer may be perceived as rude and could lead to exclusion from social groups (Rich & Xiao, 2012).

This study indicates that each cognitive attribution actually influenced smoking at a certain point along the smoking development trajectory. Curiosity, autonomy, social image, social belonging, and coping influenced earlier stages of smoking, whereas mental enhancement and engagement influenced later stages of smoking. This implies that health communications might be more effective if they can be targeted and stage-matched. For example, messages that counter-argue the attributions of curiosity, autonomy, social image, social belonging, and coping might be more effective for adolescents who are in the early stages of smoking acquisition, whereas messages that counter-argue the attributions of mental enhancement and engagement might be more effective for adolescents who have already become established smokers.

This study also describes how each cognitive attribution influenced smoking in a particular way. Curiosity, autonomy, social image, and mental enhancement preceded smoking progression. Social belonging prevented smoking from regressing. Coping and engagement produced dual effects both to facilitate smoking progression and to prevent smoking from regressing. These findings indicate that health communications might be more effective if they can be purposive. For example, messages that counter-argue the attributions of curiosity, autonomy, social image, and mental enhancement might be more effective in smoking prevention programs; messages that counter-argue the attributions of social belonging might be more effective in smoking cessation programs; whereas messages that counter-argue the attributions of coping and engagement could be useful in both smoking prevention and cessation programs.

This study finds that, while many adolescents initiated and progressed their smoking during one-year period, many other adolescents regressed their smoking to lower stages. This implies that smoking behavior is unstable in adolescence, which might provide us with a good opportunity for smoking intervention. While it is important to prevent adolescents from trying the first cigarette, it may be equally important to encourage experimenters and occasional smokers to decrease their smoking frequency and number of cigarettes smoked. Once smoking habits and nicotine dependence are established, cessation could become more difficult. To prevent and control smoking progression among adolescents long-term, comprehensive programs with both prevention and cessation components may be more efficient than other programs with only a single component.

This study has several limitations. First, the measures of cognitive attributions might not have covered the full range of possible attributions for smoking among Chinese adolescents. Further studies need to investigate broader cognitive attributions of smoking, as well as to ascertain the psychometric properties of the scale. Second, this study did not take into account the clustering of individuals within the same units (e.g., school, class) in polychotomous logistic regression, due to limited capability of the statistical program used. Third, although the large sample size of this longitudinal study provided a good opportunity to investigate the roles that cognitive attributions played on dynamic changes of smoking status, the power was not sufficient to stratify the analyses by gender. In addition, in this sample, about 1.1% of baseline never-smokers and 3.2% of lifetime smokers progressed their smoking rapidly to daily smoking one year later. However, none of the cognitive attributions significantly predicted this rapid progression. While it is possible that none of the tested attributions contributed to the rapid progression, it is also possible that the number of students having these progressions was too small and there was not sufficient power to detect significant predictors of these progressions.

In summary, this study used longitudinal data to investigate the roles that cognitive attributions played in dynamic changes in smoking behaviors. It demonstrates that each of the cognitive attributions influences smoking at a specific point along the smoking trajectory in a particular way. By identifying cognitive attributions associated with smoking initiation and progression, this study provides evidence for primary and secondary smoking prevention programs. By identifying cognitive attributions associated with smoking progression from each initial stage to each of the higher stages, this study helps us understand why some adolescents progressed their smoking gradually to an advanced stage, but others progressed more rapidly to even more advanced stages within same time period. By identifying cognitive attributions associated with smoking regression from each initial stage to each of the lower stages, this study tells us how the cognitive attributions played different roles in smoking regression and provides us with evidence for smoking cessation programs. This study proposes that anti-smoking programs may benefit from targeted, stage-matched, and purposive strategies. Moreover, it recommends developing and implementing comprehensive anti-smoking programs that incorporate primary and secondary smoking prevention components and smoking cessation components.

Highlights.

We test whether attributions for smoking are associated with future smoking behaviors.> 7 out of 8 attributions for smoking are associated with subsequent smoking behaviors. > Different smoking attributions influence different stages of smoking. > Some attributions precede smoking to progress, others prevent smoking from regressing. > These findings could inform smoking prevention and cessation programs.

Acknowledgements

Role of Funding Sources

This research was supported by the University of Southern California/Claremont Graduate University Transdisciplinary Tobacco and Alcohol Use Research Center (TTAURC), funded by the National Institutes of Health (Grant #1 P50 CA84735-01) and the Sidney R. Garfield Endowment. The sponsoring organizations had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

The authors thank the co-investigators and staff at the University of Southern California and Claremont Graduate University for participation in the CSCS in study design, protocol development, data collection, and data management. The authors thank the project directors and staff at the Centers for Disease Control and Prevention in the cities of Chengdu, Hangzhou, Harbin, Qingdao, Shenyang, and Wuhan and the Institute for Health Education in Kunming, China, for assistance with project coordination and data collection. We also thank the principals, physicians, and teachers in the participating schools for their cooperation. Finally, we express our gratitude to the national Chinese Center for Disease Control and Prevention, Peking University School of Public Health, and the municipal government, Health Bureau, and Education Committee in each participating city for their support.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Author Disclosures

Contributors

Qian Guo was the Project Manager of China Seven Cities Study (CSCS). She conducted the present smoking attribution research and wrote the manuscript. C. Anderson Johnson was the Principal Investigator (PI) of Transdisciplinary Tobacco and Alcohol Use Research Center and PI of CSCS. He worked closely with Qian Guo on this study and provided instructive and mentor assistance throughout the process. Jennifer B. Unger provided input on statistical analysis and interpretation. Paula H. Palmer and Chih-Ping Chou were co-PI’s of CSCS. All authors contributed to and have approved the final manuscript.

Conflict of Interest

All authors declare that they have no conflicts of interest.

References

  1. Allbutt H, Amos A, Cunningham-Burley S. The social image of smoking among young people in Scotland. Health Education Research. 1995;10:443–454. [Google Scholar]
  2. Aloise-Young PA, Hennigan KM, Graham JW. Role of the self-image and smoker stereotype in smoking onset during early adolescence: a longitudinal study. Health Psychology. 1996;15(6):494–497. doi: 10.1037//0278-6133.15.6.494. [DOI] [PubMed] [Google Scholar]
  3. Barton J, Chassin L, Presson CC, Sherman SJ. Social image factors as motivators of smoking initiation in early and middle adolescence. Child Development. 1982;53(6):1499–1511. [PubMed] [Google Scholar]
  4. Chen X, Fang X, Li X, Stanton B, Lin D. Stay away from tobacco: a pilot trial of a school-based adolescent smoking prevention program in Beijing, China. Nicotine and Tobacco Research. 2006;8(2):227–237. doi: 10.1080/14622200600576479. [DOI] [PubMed] [Google Scholar]
  5. Cronan TA, Conway TL, Kaszas SL. Starting to smoke in the Navy: when, where and why. Social Science and Medicine. 1991;33(12):1349–1353. doi: 10.1016/0277-9536(91)90278-k. [DOI] [PubMed] [Google Scholar]
  6. Grenard JL, Guo Q, Jasuja GK, Unger JB, Chou CP, Gallaher PE, et al. Influences affecting adolescent smoking behavior in China. Nicotine and Tobacco Research. 2006;8(2):245–255. doi: 10.1080/14622200600576610. [DOI] [PubMed] [Google Scholar]
  7. Guo Q, Unger JB, Azen PS, Li C, Spruijt-Metz D, Palmer PH, et al. Cognitive attributions for smoking among adolescents in China. Addictive Behaviors. 2010;35(2):95–101. doi: 10.1016/j.addbeh.2009.09.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Guo Q, Unger JB, Azen PS, MacKinnon DP, Johnson CA. Do Cognitive Attributions for Smoking Predict Subsequent Smoking Development? Addictive Behaviors. 2012;37(3):273–279. doi: 10.1016/j.addbeh.2011.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Heider F. The psychology of interpersonal relations. New York: Wiley; 1958. [Google Scholar]
  10. Hesketh T, Ding QJ, Tomkins A. Smoking among youths in China. American Journal of Public Health. 2001;91(10):1653–1655. doi: 10.2105/ajph.91.10.1653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Johnson CA, Palmer PH, Chou CP, Pang Z, Zhou D, Dong L, et al. Tobacco use among youth and adults in Mainland China: the China Seven Cities Study. Public Health. 2006;120(12):1156–1169. doi: 10.1016/j.puhe.2006.07.023. [DOI] [PubMed] [Google Scholar]
  12. Jones EE, Davis KE. From acts to dispositions: the attribution process in person perception. Advances in Experimental Social Psychology. 1965;2:219–266. [Google Scholar]
  13. Jones EE, Nisbett RE. The actor and the observer: divergent perceptions of the causes of behavior. Morristown: New Jersey: General Learning Press; 1971. [Google Scholar]
  14. Kelley HH. Attribution theory in social psychology. Nebraska Symposium on Motivation. 1967;15:192–238. [Google Scholar]
  15. Kleinke CL, Staneski RA, Meeker FB. Attributions for smoking behavior: comparing smokers with nonsmokers and predicting smokers' cigarette consumption. Journal of Research in Personality. 1983;17:242–255. [Google Scholar]
  16. McGee R, Stanton WR. A longitudinal study of reasons for smoking in adolescence. Addiction. 1993;88(2):265–271. doi: 10.1111/j.1360-0443.1993.tb00810.x. [DOI] [PubMed] [Google Scholar]
  17. Monson TC, Snyder M. Actors, observers, and the attribution process. Journal of Experimental Social Psychology. 1977;13:89–111. [Google Scholar]
  18. Rich ZC, Xiao S. Tobacco as a social currency: cigarette gifting and sharing in China. Nicotine and Tobacco Research. 2012;14(3):258–263. doi: 10.1093/ntr/ntr156. [DOI] [PubMed] [Google Scholar]
  19. Ross L. The intuitive psychologist and his shortcomings: distortions in the attribution process. Advances in Experimental Social Psychology. 1977;10:174–220. [Google Scholar]
  20. Ross L, Greene D, House P. The false consensus effect: an egocentric bias in social perception. Journal of Educational Psychology. 1977;13:279–301. [Google Scholar]
  21. Rugkasa J, Knox B, Sittlington J, Kennedy O, Treacy MP, Abaunza PS. Anxious adults vs. cool children: children's views on smoking and addiction. Social Science and Medicine. 2001;53(5):593–602. doi: 10.1016/s0277-9536(00)00367-1. [DOI] [PubMed] [Google Scholar]
  22. Sarason IG, Mankowski ES, Peterson AV, Jr, Dinh KT. Adolescents' reasons for smoking. Journal of School Health. 1992;62(5):185–190. doi: 10.1111/j.1746-1561.1992.tb06039.x. [DOI] [PubMed] [Google Scholar]
  23. Stanton WR, Mahalski PA, McGee R, Silva PA. Reasons for smoking or not smoking in early adolescence. Addictive Behaviors. 1993;18(3):321–329. doi: 10.1016/0306-4603(93)90033-6. [DOI] [PubMed] [Google Scholar]
  24. Stanton WR, Silva PA. Consistency in children's recall of age of initiating smoking. International Journal of Epidemiology. 1993;22(6):1064–1069. doi: 10.1093/ije/22.6.1064. [DOI] [PubMed] [Google Scholar]
  25. Treacy MP, Hyde A, Boland J, Whitaker T, Abaunza PS, Stewart-Knox BJ. Children talking: emerging perspectives and experiences of cigarette smoking. Qualitative Health Research. 2007;17(2):238–249. doi: 10.1177/1049732306297679. [DOI] [PubMed] [Google Scholar]
  26. Weiner B. An attributional theory of motivation and emotion. New York: Springer-Verlag; 1986. [Google Scholar]
  27. Yang G, Fan L, Tan J, Qi G, Zhang Y, Samet JM, et al. Smoking in China: findings of the 1996 National Prevalence Survey. The Journal of the American Medicine Association. 1999;282(13):1247–1253. doi: 10.1001/jama.282.13.1247. [DOI] [PubMed] [Google Scholar]
  28. Zhang L, Wang W, Zhao Q, Vartiainen E. Psychosocial predictors of smoking among secondary school students in Henan, China. Health Education Research. 2000;15(4):415–422. doi: 10.1093/her/15.4.415. [DOI] [PubMed] [Google Scholar]
  29. Zhu BP, Liu M, Shelton D, Liu S, Giovino GA. Cigarette smoking and its risk factors among elementary school students in Beijing. American Journal of Public Health. 1996;86(3):368–375. doi: 10.2105/ajph.86.3.368. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES