Development and validation of a smoking rationalization scale for male smokers in China

Abstract

The purpose of this study is to develop a smoking rationalization scale for Chinese male smokers. A total of 35 focus groups and 19 one-on-one interviews were conducted to collect items of the scale. Exploratory factor analyses and confirmatory factor analyses were conducted to identify the underlying structure of the scale. Results found a 26-item scale within six dimensions (smoking functional beliefs, risk generalization beliefs, social acceptability beliefs, safe smoking beliefs, self-exempting beliefs, and quitting is harmful beliefs). The scale showed acceptable validity and reliability. Results highlight that smoking rationalization is common among Chinese male smokers, and some beliefs of smoking rationalization seem to be peculiar to China.

Keywords

China intention to quit rationalization scale smoking

Introduction

Previous research (Chapman et al., 1993; Dillard et al., 2006; McMaster and Lee, 1991; Peretti-Watel et al., 2007a; Weinstein et al., 2005) has documented a range of rationalization beliefs among smokers. Smoking rationalization, also known as self-exempting beliefs or disengagement beliefs, is well-documented among smokers in the United States, Europe, and Australia (Kleinjan et al., 2009; Oakes et al., 2004). Some smoking rationalization beliefs are “functional,” such as the belief that smoking reduces stress and increases concentration. Others are “risk-minimizing,” such as skepticism about the health consequences of smoking. Smoking rationalization is inversely associated with aspects of smoking cessation, such as intention to quit, quit attempts, and action of quitting (Borland et al., 2009; Kleinjan et al., 2009; Peretti-Watel et al., 2007b; Yong and Borland, 2008). These findings imply that a better understanding of smoking rationalization may be important in motivating smokers to quit.

Several measures have been developed to measure common smoking rationalization beliefs, such as smoking functional beliefs measures (Yong and Borland, 2008), measures of rationalizations (Fotuhi et al., 2013), and disengagement beliefs scales (Dijkstra and Brosschot, 2003; Dijkstra et al., 1999). Although validated, these measures were mostly developed for and used in western countries. Despite the universal nature of justifying or rationalizing smoking, there may be important yet distinct cultural differences across different cultures. Therefore, existing measures of smoking rationalization may not be the most appropriate in studying such beliefs in people from non-Western cultural backgrounds, such as Asian smokers.

China is the world’s biggest tobacco producing and consuming country, and smoking is common among Chinese men. The current smoking rate among Chinese males aged above 15 years is 52.1 percent. In contrast, the female smoking rate is extremely low in China with only 2.7 percent of women reporting current smoking (Yang, 2011). China has one of the lowest ratios of female-to-male smoking prevalence in the world (Hitchman and Fong, 2011), which is largely attributable to cultural norms (Hitchman and Fong, 2011; WHO Department of Gender, 2003). Chinese have a more positive attitude toward and higher acceptance of smoking among men than most western countries (ITC and Project Office of Tobacco Control, 2012). Many Chinese consider tobacco to be an important part of social and cultural interactions and feel strongly that tobacco consumption is important to the economy so much so that it is almost a patriotic duty to smoke (Ma et al., 2008). In China, the quit ratio (the proportion of former smokers among ever daily smokers) of Chinese smokers is low compared to other countries with only 18.7 percent having quit smoking in a national survey in 2010 (ITC and Project Office of Tobacco Control, 2012; Yang, 2011). Understanding rationalization beliefs popular among Chinese smokers may be useful in guiding quitting initiatives. Except for a few studies which have examined smoking-related beliefs (Ma et al., 2008; Shen et al., 2000), no study has comprehensively examined the extent of rationalization among Chinese smokers and no validated instrument has been developed for Chinese smokers.

This study aims to identify common beliefs that smokers use to rationalize smoking and to develop a smoking rationalization scale that applies to male Chinese smokers. We focused on Chinese men because of their high smoking prevalence and because we expect rationalization among Chinese females to be distinctively different from males (Ding et al., 2014). Such a scale could help inform future interventions for smoking cessation by targeting relevant rationalization beliefs.

Methods

Item generation

Upon obtaining verbal informed consent, we conducted semi-structured interviews (by focus group or one-on-one interview) among male smokers. The interviewees were selected with diverse demographic characteristics (e.g. occupation, ethnicities) from three Chinese cities (Shanghai, Nanning, and Mudanjiang) to gather smoking rationalization beliefs and attitudes. We intentionally selected smokers from different demographic backgrounds and geographic regions to maximize types of smoking rationalization beliefs. The interviews were conducted by two trained team members (X.H. and W.F.). The format of interview (focus group or one-on-one interview) was chosen based on interviewees’ characteristics and convenience. For example, if the interviewees worked in the same workplace and were available at the same time, then a focus group interview was chosen. Alternatively, if the interviewees worked in different workplaces or lived far away from each other, then independent one-on-one interviews were conducted. We used a standard protocol throughout all the interviews. Topics of the interviews are listed as follows:

Interviewees’ own experience of smoking, including (a) smoking history (i.e. age of smoking initiation, years of smoking, and the number of cigarettes smoked a day), (b) quitting attempts and quitting plans, and (c) smoking policies/rules in their workplace and home;

Interviewees’ views on smoking and health, including (a) the health consequences of smoking, especially the most personally relevant health consequences, (b) how they compare the risk of smoking with other health risks, and (c) their positive experience from smoking;

Interviewees’ views about quitting, including their benefits, barriers, and perceived harms of smoking cessation.

All questions were open-ended, and interviewees were encouraged to express their views. Each interview was recorded and transcribed verbatim. A total of 35 focus groups and 19 one-on-one interviews were conducted, each lasting 40–90 minutes. In total, 201 smokers from three cities were interviewed. Two team members (X.H. and W.F.) independently conducted content analysis to identify key themes from the transcribed interviews using Nvivo 10 (QSR International Pty, Ltd). Themes extracted by two team members were compared and any discrepancies were resolved through discussion. In cases where agreement could not be reached, a third team member (P.Z.) was consulted. Similar beliefs were merged and those mentioned by only one interviewee were excluded. Overall, a list of 47 candidate items most frequently mentioned by smokers formed the first draft of smoking rationalization scale. Each item used a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neither disagree nor agree, 4 = agree, 5 = strongly agree, higher scores represent higher levels of rationalization).

The draft scale was then modified following expert consultation and pilot testing. Items that were difficult to interpret or not applicable to other male smokers were deleted. The final scale with 33 items was derived based on team discussion to ensure its face validity, clarity, and interpretability (Appendix 1).

Scale development and evaluation

Another large sample of participants was recruited from Shanghai, Nanning, and Mudanjiang for scale development and evaluation. Through local Centers for Disease Control and Prevention (CDC), we selected workplaces that reflected main occupational categories (government and corporate officers, professional employees, businessmen, hospitality workers, farmers, manual workers, and students) in the China Statistical Yearbook of 2013 (Department of Population and Employment Statistics, National Bureau of Statistics of China, 2013). If a workplace agreed to participate, a list of all smokers’ names was provided by the contactors to researchers for random selection of participants. We supplemented workplace-based sampling with community sampling to recruit retirees. This sampling strategy was developed to balance the proportions of participants who were working with those who were retired (Xu et al., 2009; Yang, 2011).

Upon providing verbal consent, participants were invited to complete an anonymous self-administered questionnaire consisting of the 33-item rationalization scale and other smoking-related questions. Trained research assistants were present during the questionnaire administration for quality control and in case of questions. Ethics approval was granted by the Ethics Committee of the School of Public Health, Fudan University.

A total of 3727 smokers participated with a 98.3 percent completion rate (n = 3665) after excluding incomplete questionnaires and those with obviously flippant responses. Demographic characteristics are shown in Table 1.

Table 1.

Demographic characteristics of the respondents recruited from three cities in China (N = 3665).

Demographics	Shanghai (%) n = 1293	Nanning (%) n = 1175	Mudanjiang (%) n = 1198	Total	p
Age					<0.001
18–30	28.3	34.4	28.1	30.2
31–40	23.3	25.1	27.0	25.1
41–50	24.8	19.9	25.5	23.5
⩾51	23.6	20.6	19.4	21.3
Ethnicities					<0.001
Han	99.7	78.0	97.4	92.1
Others	0.3	22.0	2.6	7.9
Marital status					<0.001
Married	78.8	69.7	69.9	73.1
Not married	21.2	30.3	30.1	26.9
Highest educational attainment					<0.001
Junior school or lower	29.1	35.5	24.5	29.7
High school/technical school	27.6	23.2	30.8	27.2
Junior college	17.5	21.0	21.7	20.0
University or higher	25.8	20.3	23.0	23.2
Income per month					<0.001
<2000 Yuan^a	14.7	21.8	21.2	19.0
2000–3999 Yuan	43.0	42.1	55.8	46.8
4000–5999 Yuan	28.1	23.5	17.1	23.1
>6000 Yuan	14.2	12.6	5.8	11.0

1 dollar = 6.19 Yuan (2013).

After 2 weeks of initial survey, a convenience sample of 50 participants was invited to fill in the same questionnaire again to obtain test–retest reliability.

Statistical analysis

Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to explore the optimal model for the rationalization scale. SPSS 19.0 (IBM SPSS Statistics) was used for conducting EFA and LISREL 8.70 (Scientific Software International, Inc.) for CFA. To obtain stable and consistent factorial structures, multiple EFAs were conducted. We randomly divided the data into four groups using a random number generator: three of them (n = 916, 916, and 917, labeled as Groups 1, 2, and 3) for EFAs and one (n = 916, Group 4) for CFA.

Principal component factoring was chosen for the EFAs. Latent root criterion (retaining factors with eigenvalues greater than 1.0) and a scree test were used to determine the number of factors. As the factors were expected to be correlated, the oblique rotation method (Promax) was chosen. Items with factor loading less than 0.50 on any factor and those with factor loading at 0.40 or above on two or more factors were deleted (Hair et al., 2010).

CFA was conducted in Group 4 with model specification based on results from EFAs. Several fit indices were used, including χ²/degree of freedom (df) ratio (<2.0 is considered very well, 2.0–5.0 is acceptable), root mean square error of approximation (RMSEA < 0.08 means an acceptable fit), comparative-fit index (CFI > 0.90 as recommended value) (Hair et al., 2010; Wheaton, 1987).

After model structure was established, the validity and reliability of the model were assessed. In this study, we examined various types of construct validity, including convergent validity, discriminant validity, and nomological validity. Convergent validity was measured by standardized loading and the average variance extracted (AVE) of each item. A good rule of thumb is that standardized loading estimates should be 0.5 or higher (ideally 0.71 or higher), and AVE should be 50 percent or higher. Discriminant validity was assessed in two ways. One was the correlation coefficients between any two constructs, the other was the difference between the average AVE of two constructs and the squared interconstruct correlation (r²): (1) when the 95 percent confidence interval of the correlation did not overlap one, then the discriminant validity was basically satisfied; (2) if the average AVE is greater than r², then the discriminant validity was supported. The latter was a more conservative criterion for discriminant validity (Hair et al., 2010; Qiu and Lin, 2009). Variables including age, cigarettes consumed per day, nicotine dependence, and intention to quit were selected to develop the nomological network of rationalization. These variables were selected because they have been found to be consistently related to rationalization (Dillard et al., 2006; Kleinjan et al., 2009; Oakes et al., 2004; Yong et al., 2005). If the relationships between the rationalization scores and the empirically related variables selected above were confirmed, then the nomological validity of the scale was satisfied (Cronbach and Meehl, 1955; Hair et al., 2010). Pearson correlation coefficients (for continuous variables) and Spearman’s correlation coefficients (for categorical variables) were used to assess the nomological validity. Cronbach’s alpha was also calculated to assess the internal consistency of the scale. Higher value means better internal consistency and Cronbach’s alpha >0.7 was considered acceptable. Additionally, intraclass correlation coefficients (ICC) were calculated for the evaluation of test–retest reliability (Hair et al., 2010) (the procedure of the study is shown in Figure 1).

Results

EFA

According to the results of the latent root criterion and scree test, Model 1 and Model 3 suggested a six-factor solution, despite some minor differences in factor loading. Model 2 suggested a five-factor solution. The detailed information is presented in Table 2.

Table 2.

Oblique rotated factor loadings and extracted variance from EFA.

Factor	Items^a	Model 1 (Group 1, n = 916)	Model 2 (Group 2, n = 916)	Model 3 (Group 3, n = 917)
1	M18	0.81	0.83	0.88
	M19	0.78	0.74	0.74
	M21	0.70	0.70	0.69
	M20	0.69	0.63	0.60
	M17	0.55	0.75	0.79
2	M6	0.78	0.75	0.75
	M8	0.61	0.57	0.61
	M3	0.61	0.78	0.57
3	M25	0.76	0.70	0.81
	M26	0.72	0.62	0.86
	M24	0.71	0.74	0.73
	M32	0.63
	M31	0.58
	M27	0.52		0.69
	M22		0.69
4	M15	0.79	0.72	0.89
	M13	0.73	0.75	0.64
	M14	0.66	0.78
	M16	0.66	0.73	0.66
5	M2	0.81	0.78	0.74
	M4	0.72	0.58	0.73
	M5	0.69	0.53	0.66
	M1	0.61		0.81
	M7	0.51		0.51
	M10			0.50
	M28		0.64
	M30		0.51
6	M28	0.80		0.54
	M30	0.75		0.70
	M29	0.62		0.58
	M31			0.65
	M22			0.52
	Variance extracted	58.5%	56.4%	57.7%

EFA: exploratory factor analysis.

Items with factor loading less than 0.50 on any factor and those with factor loading at 0.40 or above on two or more factors were deleted and not presented in Table 2.

Despite minor differences, the core items for each factor were consistent across models. For the six-factor models (Model 1 and Model 3), Factor 1 was labeled “smoking functional beliefs,” as the main items focused on the function of smoking (e.g. M18 “smoking can eliminate fatigue and be refreshing”). Factor 2 was named “risk generalization beliefs,” similar to the constellation of beliefs termed “life is a risk jungle” in previous research (Oakes et al.,2004) as the main items were about comparing harms of smoking with other dangers in life (e.g. M6 “Air pollution, food safety and life stress are much more dangerous to health than smoking”). Factor 3 concerned the government’s forbearance of smoking and the wide acceptance of smoking among Chinese doctors, celebrities, and society (e.g. M31 “I will consider quitting smoking only if the government closes the tobacco factories”). This group was named “Social acceptability beliefs.” Factor 4 included items that underpinned the measures to reduce or counteract harms of smoking (e.g. M16 “It’s safe to smoke high-quality cigarettes”), therefore it was interpreted as “Safe smoking beliefs.” Items in Factor 5 concern skepticism about the harms of smoking and perceived exemption from harms (e.g. M5 “Smoking is not always bad for you because many smokers live long lives while many non-smokers don’t”). Therefore, Factor 5 was named “Self-exempting beliefs.” Factor 6 was about the negative attitude toward quitting smoking (e.g. M28 “If you have smoked for a long time, the body adapts to the cigarettes and reaches a balance. Quitting will therefore lead to illness”), so the sixth factor was labeled “Quitting is harmful beliefs.”

Yet, Model 2 was a five-factor solution. The first four factors were almost the same as the first four factors in the six-factor models. Factor 5 in Model 2 can be considered a combination of Factor 5 and Factor 6 in the six-factor models. This Factor 5 in Model 2 was therefore named “skeptical beliefs on smoking and quitting.”

CFA

CFA was performed among Group 4 to identify the best model. The single-factor model which assumed all 33 items belong to an overall “rationalization” factor was also analyzed as the baseline model. The indices of model fitness such as χ²/df ratio, RMSEA, and CFI were calculated (see Table 3). According to χ²/df ratio, RMSEA, and CFI, Model 1 was the best solution.

Table 3.

Fit indices for comparing among different models from CFA (Group 4, n = 916).

Model	No. of items	χ ²	df	χ²/df < 5.0	RMSEA < 0.08	CFI > 0.90
Single-factor model	33	3595.3	495	7.26	0.098	0.95
Model 1	26	1367.7	284	4.82	0.065	0.97
Model 2	21	1286.6	179	7.19	0.082	0.96
Model 3	26	1482.0	284	5.21	0.068	0.97

CFA: confirmatory factor analysis; RMSEA: root mean square error of approximation; CFI: comparative-fit index.

Model evaluation and modification

CFA results suggested that Model 1 has the best fit among the four models. Therefore, our final scale has 26 items with a six-factor structure. Results from validity and reliability tests of Model 1 are described as follows.

Convergent validity

In Model 1, each item had a standardized loading greater than 0.5. AVEs of five factors were greater than 0.50 or close to 0.50, except for Factor 2, which has a relative low AVE value at 0.40 (Table 4).

Table 4.

Item characteristics and standardized loading and AVEs of the final model (Model 1).

Factor	Items	Standardized loadings	AVE
F1: smoking functional beliefs	M18 “Smoking can eliminate fatigue and be refreshing”	0.80	0.57
	M19 “Smoking is good for inspiration and active thinking”	0.77
	M21 “Smoking can reduce interpersonal distance and make social interaction easier”	0.70
	M20 “Smoking is a good way to kill time”	0.74
	M17 “Smoking can relieve tension and stress”	0.76
F2: risk generalization beliefs	M6 “Air pollution, food safety, and life stress is much more dangerous to health than smoking”	0.73	0.40
	M8 “If smoking was so bad for health, the government would have banned tobacco sales”	0.52
	M3 “A lot of non-smokers also get lung cancer”	0.64
F3: social acceptability beliefs	M25 “Many famous people smoke, so it is normal to smoke”	0.79	0.51
	M26 “Smoking is pretty normal for men”	0.79
	M24 “There are so many smokers in society, so it’s hard for you to be different”	0.77
	M32 “Lots of doctors smoke, so it’s unconvincing for them to persuade me to quit”	0.66
	M31 “I will consider quitting smoking only if the government closes the tobacco factory”	0.54
	M27 “Smoking is part of my lifestyle that others can’t interfere with”	0.71
F4: safe smoking beliefs	M15 “Low tar cigarettes can reduce the harms of smoking/is less harm”	0.65	0.55
	M13 “If you don’t inhale the smoke into the lungs, the harm is minimized”	0.75
	M14 “People like me who do not smoke many cigarettes are not at risk of smoking health problems”	0.80
	M16 “It’s safe to smoke high-quality cigarettes”	0.75
F5: self-exempting beliefs	M2 “I have not experienced any harm to my health”	0.67	0.47
	M4 “I think I may have genes which protect me from the harms of smoking”	0.72
	M5 “Smoking is not always bad for you because many smokers live long lives while many non-smokers don’t”	0.76
	M1 “There is still insufficient medical evidence to prove that smoking is harmful”	0.54
	M7 “The fact that I can still smoke means my health status is not bad”	0.72
F6: quitting is harmful beliefs	M28 “If you have smoked for a long time, the body would adapt to the cigarette and reach a balance and quitting will lead to illness”	0.73	0.54
	M30 “After quitting smoking, I will get weight, which is also harmful to health”	0.69
	M29 “If you try to quit and fail, you will smoke more than before, so you would rather not to quit”	0.78

AVE: average variance extracted.

Discriminant validity

The standardized correlation coefficients (r) of any two factors ranged from 0.48 to 0.75 and none of their 95 percent confidence intervals (95% confidence intervals (CIs)) overlapped 1, which has met the basic standard of discriminant validity. However, few of the average AVEs were greater than the corresponding interconstruct squared correlation (r²). As the comparison of the average AVE of two constructs with the squared interconstruct correlation (r²) is a more conservative measurement for discriminant validity, we could conclude that Model 1 has an acceptable discriminant validity (see Table 5).

Table 5.

The standardized correlation coefficients(r), average AVEs, and r² for Model 1 (n = 916, Group 4).

r r²/aAVE	F1	F2	F3	F4	F5
F1
F2	0.520.27/0.47*
F3	0.750.56/0.54	0.500.42/0.46*
F4	0.590.34/0.56*	0.480.23/0.47*	0.40.41/0.53*
F5	0.510.26/0.52*	0.700.49/0.44	0.640.41/0.49*	0.750.56/0.51
F6	0.580.34/0.55*	0.570.32/0.47*	0.690.48/0.52*	0.670.45/0.54*	0.720.52/0.50

aAVE: average AVE of two factor.

: aAVE>r².

F1: smoking functional beliefs; F2: risk generalization beliefs; F3: social acceptability beliefs; F4: safe smoking beliefs; F5: self-exempting beliefs; F6: quitting is harmful beliefs.

Nomological validity

Evidence in our study showed positive correlation between each factor and the number of cigarettes consumed per day (except Factor 2) and smoker’s age, inverse associations were found between each factor and the intention to quit smoking, as found in previous research, indicating good nomological validity in Model 1.

Scores and reliability

Percentage of participants who agreed/strongly agreed on each item varied from 28.1 to 69.8 percent. On average, Factor 1 (smoking functional beliefs) had the highest score and Factor 5 (self-exempting beliefs) had the lowest score (see Table 6).

Table 6.

Item descriptive statistics, internal consistency, and correlations between each factor and related variables (n = 3665).

Factor	Items	Agree or strongly agree (%)	Subscale mean (SD)	Cronbach’s α	Correlation coefficient (r)
Factor	Items	Agree or strongly agree (%)	Subscale mean (SD)	Cronbach’s α	No. of cigarettes/day^a	Nicotine dependence^a	Intention to quit^b	Age^a
F1: smoking functional beliefs	M17 “Smoking can relieve tension and stress”	69.8	3.64 (0.69)	0.820	0.042*	0.140**	−0.144**	0.061**
F1: smoking functional beliefs	M18 “Smoking can eliminate fatigue and be refreshing”	69.7
	M19 “Smoking is good for inspiration and active thinking”	54.6
	M20 “Smoking is a good way to kill time”	54.8
	M21 “Smoking can reduce interpersonal distance and make social interaction easier”	63.1
F2: risk generalization beliefs	M3 “A lot of non-smokers also get lung cancer”	59.6	3.50 (0.72)	0.605	0.023	0.197**	−0.155**	0.103**
F2: risk generalization beliefs	M6 “Air pollution, food safety, and life stress is much more dangerous to health than smoking”	54.7
	M8 “If smoking was so bad for health, the government would have banned tobacco sales”	48.2
F3: social acceptability beliefs	M24 “There are so many smokers in society, so it’s hard for you to be different”	45.1	3.32 (0.70)	0.821	0.092**	0.170**	−0.166**	0.098**
	M25 “Many famous people smoke, so it is normal to smoke”	43.7
	M26 “Smoking is pretty normal for men”	51.7
	M27 “Smoking is part of my lifestyle that others can’t interfere with”	48.2
	M31 “I will consider quitting smoking only if the government closes the tobacco factory”	43.3
	M32 “Lots of doctors smoke, so it’s unconvincing for them to persuade me to quit”	43.9
F4: safe smoking beliefs	M13 “If you don’t inhale the smoke into the lungs, the harm is minimized”	41.1	3.29 (0.74)	0.767	0.114**	0.204**	−0.159**	0.118**
	M14 “People like me who do not smoke many cigarettes are not at risk of smoking health problems”	33.3
	M15 “Low tar cigarettes can reduce the harms of smoking/is less harm”	43.9
	M16 “It’s safe to smoke high-quality cigarettes”	39.9
F5: self-exempting beliefs	M1 “There is still insufficient medical evidence to prove that smoking is harmful”	41.3	3.08 (0.77)	0.784	0.132**	0.188**	−0.110**	0.190**
	M2 “I have not experienced any harm to my health”	42.2
	M4 “I think I may have genes which protect me from the harms of smoking”	28.1
	M5 “Smoking is not always bad for you because many smokers live long lives while many non-smokers don’t”	35.7
	M7 “The fact that I can still smoke means my health status is not bad”	37.3
F6: quitting is harmful beliefs	M28 “If you have smoked for a long time, the body would adapt to the cigarette and reach a balance and quitting will lead to illness”	44.2	3.28 (0.73)	0.741	0.100**	0.197**	−0.151**	0.119**
	M29 “If you try to quit and fail, you will smoke more than before, so you would rather not to quit”	42.5
	M30 “After quitting smoking, I will get weight, which is also harmful to health”	29.6
Total	–	–	3.30 (0.56)	0.920	0.106**	0.221**	−0.175**	0.159**

SD: standard deviation.

Pearson correlation coefficients.

Spearman’s correlation coefficients.

p < 0.05; **p < 0.01.

Cronbach’s alpha coefficients ranged between 0.74 and 0.92 except for Factor 2 (0.605), meaning that each factor had acceptable-to-good internal consistency (see Table 6).

Among the test–retest subsample, the ICC of the two surveys was 0.739 (p < 0.01), indicating good test–retest reliability.

Discussion

This study is the first to develop a smoking rationalization scale within the context of Chinese culture. The newly developed scale provides a validated instrument for the evaluation of smoking rationalization beliefs held by Chinese male smokers. As the item selection and scale validation samples consisted of male smokers with diverse demographic backgrounds from different geographic regions, the scale is intended to be used among the general population of male smokers in China. The scale could also be informative to research within similar cultural contexts. Given that smoking rationalization is expected to be associated with low intention to quit, our scale, a quantifiable measurement for smoking rationalization, may play an important role in addressing the low quitting intention among smokers in China.

Three versions of the model were tested. A six-factor model with 26 items was identified as the optimum solution. Although some indicators of convergent validity and discriminant validity were marginal, the basic requirements for both convergent and discriminant validity were satisfied. Moreover, for convergent validity, some experts suggested that an AVE value of 0.30 or greater was acceptable in social science research (Qiu and Lin, 2009). By this criterion, the convergent validity was sufficiently good for the scale. For discriminant validity, although factors were expected to be correlated, 95 percent confidence intervals of the interconstruct correlation coefficients did not overlap 1, we can conclude that the factors were related but they referred to different concepts.

The rationalization scale includes six dimensions—smoking functional beliefs, risk generalization beliefs, social acceptability beliefs, safe smoking beliefs, self-exempting beliefs, and quitting is harmful beliefs. The smoking functional beliefs, self-exempting beliefs, and risk generalization beliefs echoed previous studies among Australian, British, Canadian, and American smokers (Fotuhi et al., 2013; Oakes et al., 2004), suggesting that some beliefs for rationalizing smoking are likely to be shared by smokers from different cultures.

We report several new culture-specific beliefs in this study. First, in our scale, smoking for socialization (item “Smoking can reduce interpersonal distance and make social interaction easier”) is one of five items of smoking functional beliefs in our Chinese sample, showing good internal consistency (Cronbach’s alpha = 0.82). In the study based on smokers from Canada, the United States, the United Kingdom, and Australia, the social interaction item (item “Smoking makes it easier for you to socialize”) was excluded from their final functional subscale because of low correlations with other items (Cronbach’s alpha = 0.58) (Fotuhi et al., 2013). This difference implies that socialization is indispensable to the subscale of smoking functional beliefs in our study, especially when compared with those in western countries. Our finding is consistent with Ma’s exploratory study about the myths and misconceptions about smoking in China, one of which being that tobacco use was an important part of social interaction (Ma et al., 2008). In China, offering and accepting cigarettes are regarded as common social etiquette, helping build friendship and trust among men. Well-constructed within the socialization item, smoking functional beliefs accurately reflect this smoking situation in China.

Second, we find that the social acceptability is an important component of rationalizing beliefs among Chinese smokers, while such belief is uncommon among western smokers, according to previous studies. In fact, as mentioned above, China has a higher acceptance of smoking among men than most western countries (ITC and Project Office of Tobacco Control, 2012). Even for government officials and physicians, who are expected to be “role models in tobacco control,” the smoking prevalence is as high as 54.1 and 40.4 percent, respectively (Yang, 2011). In addition, the tobacco industry has been promoting the social acceptability of smoking through advertisements (Chu et al., 2011; Wang et al., 2016). All these help make the pro-smoking values the “norms” of the Chinese society, which explains why the social acceptability beliefs are prevalent among our participants.

Another component of smoking rationalization among Chinese smokers is the safe smoking beliefs. Although similar findings have been reported in other studies that smokers believed they could “do something” to make smoking safe, the specific approaches are different. In Malaysia, smokers often believed that certain foods and drinks could reduce hazards associated with tobacco smoking (Jackson et al., 2004), while in Finland smokers tried to undo the harms of smoking by adopting other healthy lifestyles such as physical activity (Heikkinen et al., 2010). In our study, Chinese smokers appear to focus more on cigarettes themselves. Such lay beliefs may be influenced by the “low tar and less harm” marketing strategy used by the tobacco industry (Elton-Marshall et al., 2010; Yang, 2012). In China, many hold the misperception that low-tar cigarettes are less harmful, and the highest prevalence of this misperception is among medical professionals (Yang, 2011). All of this evidence suggests that the tobacco industry’s promotion of low-tar cigarettes has achieved widespread penetration.

In addition, we find that the “quitting is harmful beliefs” is apparently a unique phenomenon among male smokers in China. No other study has identified similar rationalization. One possible reason may be that in other countries with more comprehensive tobacco control policies and campaigns, smokers may be more aware of the benefits from quitting smoking. In China, health communication on tobacco control is not widespread and mainly concentrates on the dangers of smoking rather than the benefits of cessation. In addition, influenced by theories from traditional Chinese medicine, many Chinese smokers believe that when someone smokes for a long time, their body has established a “balance” and quitting would break this balance and lead to disease.

Overall, our study suggests that rationalization is a common practice among male smokers in China with a majority of participants endorsing various types of rationalization beliefs. This finding is important. As rationalization reflects a distorted perception, it prevents smokers from being aware of the true danger of smoking, and it provides “excuses” for continuing to smoke. Therefore, conventional anti-smoking campaigns and health education that simply deliver the negative health consequences of smoking can’t automatically lead to smoking cessation if smoking rationalization is common. Smoking cessation may be particularly challenging considering that the tobacco industry has made great efforts in advertising and marketing to reinforce smoking rationalization beliefs (Chu et al., 2011; Lesyna, 2012; Wang et al., 2014, 2016). Based on the findings from our study, we speculate that public health programs and interventions targeting rationalization beliefs commonly held by smokers might be more successful in persuading smokers to quit. The newly developed scale could be an useful tool to quantify smoking rationalization in the context of surveillance, behavioral epidemiological research and development and evaluation of smoking cessation interventions.

However, there are some potential limitations in this study. First, although scale items were generated from a diverse sample from three Chinese cities, it is still possible that some important locally relevant beliefs were missed. However, we believe that through a comprehensive derivative process, the items selected, particularly the final 26 items, represent the most common beliefs held by the general smoking population. Second, the survey relied on self-reports, which is subject to social desirability bias; therefore, some potentially common, but undesirable beliefs could have been suppressed.

In conclusion, we developed a parsimonious, validated rationalization scale for male smokers in China. The scale consisted of 26 items under six dimensions. Our work, together with previous research, suggests that smoking rationalization is a common phenomenon among smokers and that there are many similarities across countries and cultures. However, our study finds several culture-specific beliefs particularly held by Chinese smokers in rationalizing smoking. This study established the theoretical basis for future research on the role of rationalization in smoking cessation and could inform intervention strategies that dispel widely held rationalization beliefs among male smokers in China.

Footnotes

Appendix

Appendix 1.

Modification process.

Reason for modification	Original items	Modification
Similar to other items	7. Harms from smoking may show up after many years, but there are no obvious bad impacts in the short term	Similar to item M3, deleted
	8. At present, smoking gives me more benefits than potential harms to health	Similar to item M24–M28, deleted
	30. Smoking can bring a pleasant sense of satisfaction
Share similar contents and were merged into new item	1. The medical evidence that smoking is harmful is exaggerated	There is still insufficient medical evidence to prove that smoking is harmful (M1)
Share similar contents and were merged into new item	2. There is still no good evidence to show how many cigarettes are needed to harm health
	5. Compared with smoking, air pollution and other environmental factors cause more lung cancers	Air pollution, food safety, and life stress is much more dangerous to health than smoking (M6)
	12. Air pollution, food safety, and life stress is much more dangerous to health than smoking
	21. People should live in the present and needn’t to consider things in a few decades	Smoking is enjoyable, so why not enjoy yourself and smoke (M23)
	22. Smoking is my life enjoyment, why not enjoy yourself and smoke	If you have smoked for a long time, the body would adapt to the cigarette and reach a balance and quitting will lead to illness (M28)
	23. Even if I can survive more several years after quitting smoking, I will feel it’s boring
	32. If you have smoked for a long time, the body would adapt to the cigarette and reach a balance and quitting will lead to illness
	35. Quit smoking will weaken body’s immune system
	36. It’s too difficult for me to quit	There are so many smokers in society, so it’s hard for you to be different (M24)
	43. I don’t want to smoke, but I have to smoke for social acceptance and fitting in
	44. There are so many smokers in society, so it’s hard for you not to smoke
Less agreement from smokers	15. Rather than worrying about smoking being harmful, it’s better for health if you keep your mind calm	Deleted
	29. Smoking helps control body weight
	47. Smoking is legal, so it has nothing to do with other people
Beyond the theme	41. Women smoking make others feel bad	Deleted
Item list.
M1	There is still insufficient medical evidence to prove that smoking is harmful
M2	I have not experienced any harm to my health
M3	A lot of non-smokers also get lung cancer
M4	I think I may have genes which protect me from the harms of smoking
M5	Smoking is not always bad for you because many smokers live long lives while many non-smokers don’t
M6	Air pollution, food safety, and life stress is much more dangerous to health than smoking
M7	The fact that I can still smoke means my health status is not bad
M8	If smoking was so bad for health, the government would have banned tobacco sales
M9	Passive smoking is more dangerous than active smoking, so I choose active smoking instead of passive smoking
M10	Tobacco itself is not harmful, it is processing technology such as additives that make it harmful
M11	Life is full of risks, it’s impossible to eliminate each risk, so it’s unwise to be just concerned about smoking
M12	You can overcome the harms of smoking by other lifestyle practices such as healthy eating and regular exercise
M13	If you don’t inhale the smoke into the lungs, the harm is minimized
M14	People like me who do not smoke many cigarettes are not at risk of smoking health problems
M15	Low tar cigarettes can reduce the harms of smoking/is less harm
M16	It’s safe to smoke high-quality cigarettes
M17	Smoking can relieve tension and stress
M18	Smoking can eliminate fatigue and be refreshing
M19	Smoking is good for inspiration and active thinking
M20	Smoking is a good way to kill time
M21	Smoking can reduce interpersonal distance and make social interaction easier
M22	Smoking is helpful in raising government revenue
M23	Smoking is enjoyable, so why not enjoy yourself and smoke
M24	There are so many smokers in society, so it’s hard for you to be different
M25	Many famous people smoke, so it is normal to smoke
M26	Smoking is pretty normal for men
M27	Smoking is part of my lifestyle that others can’t interfere with
M28	If you have smoked for a long time, the body would adapt to the cigarette and reach a balance and quitting will lead to illness
M29	If you try to quit and fail, you will smoke more than before, so you would rather not quit
M30	After quitting smoking, I will get weight, which is also harmful to health
M31	I will consider quitting smoking only if the government closes the tobacco factory
M32	Lots of doctors smoke, so it’s unconvincing for them to persuade me to quit
M33	Quit smoking is a natural process, if one day I feel sick, I would quit smoking naturally

Authors’ note

X.H. and W.F. are joint first authors.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by National Nature Science Foundation of China (71203033) and partly funded by consulting service for center of excellence in Global Health Policy Development and Governance in China (GHSP-CS-OP3-02). Dr Ding (APP1072223) is funded by an early career fellowship from the National Health and Medical Research Council of Australia.

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