Predicting College Student Drinking and Smoking Intentions With Cognitively Accessible Attitudes and Norms

Abstract

Understanding the factors that make college students more likely to drink alcohol and smoke cigarettes is key to developing effective interventions in order to reduce these behaviors. This study sought to understand entering college students’ intentions to engage in smoking and drinking behavior by examining the cognitive accessibility (ease of retrieval from memory) of attitudes and norms for drinking. A sample of 413 first-year college students living in on-campus residence halls participated in the study in the first 2 weeks of their first semester of college. Reaction time measures of attitudes and norms assessed the cognitive accessibility of these constructs. Hierarchical linear regression analysis was conducted. Results indicated that the cognitive accessibility of both attitudes and peer injunctive norms predicted behavioral intentions to drink and smoke. Our findings indicate that when injunctive peer norms are accessible from memory, they are better predictors of drinking and smoking intentions than descriptive norms or injunctive family norms. Our work provides important guidance for interventions to reduce risky behavior in college students and suggests that emphasizing social costs of these behaviors may be a promising strategy.

Keywords

adolescents attitude accessibility cognitive accessibility norm accessibility reasoned action approach

According to the U.S. Department of Health and Human Services (2012), a majority of adult smokers started smoking by age 18 years, and an estimated 99% began before age 26 years, indicating that late adolescence and early adulthood are critical points of time for tobacco uptake. Although smoking initiation rates for teens have decreased, they are increasing for young adults (National Cancer Institute, 2015). Efforts to understand smoking uptake in the transition to college are needed to improve prevention efforts in this population.

Alcohol use also presents health risks to emerging adults, particularly to college students. College students are more likely to binge drink (5+ drinks) than noncollege adults of the same age (Stern, Terry-McElrath, & Patrick, 2017). Self-report surveys reveal that 60% of college students reported consuming alcohol in the past 30 days, and 40% of those students reported participating in binge drinking (Substance Abuse and Mental Health Services Administration, 2013). This exposure to risky drinking can put students in a position where they are vulnerable to avoidable injury or death.

Dual processing models of addiction posit that triggers to engage in addictive behavior are subconscious and operate automatically (Ostafin & Marlatt, 2008; Ostafin, Marlatt, & Greenwald, 2008). Because of the challenges that arise during transitions to college, self-control is often depleted, and first-year college students rely on automatic processing with regard to smoking and drinking behavior (Ostafin, Bauer, & Myxter, 2012). Thus, studying automatically activated constructs such as cognitively accessible attitudes and norms in this context offers the opportunity to understand better the precursors to drinking and smoking behavior, with the ultimate goal of improving interventions to reduce such behavior.

Cognitive Accessibility of Attitudes and Norms

Norms and attitudes are important determinants of one’s intention to act (Fishbein & Ajzen, 2010). An attitude is an individual’s evaluation of an object or abstract concept as good or bad (Fishbein & Ajzen, 1975). The cognitive accessibility of that attitude is the speed with which an individual can recall that judgment from memory. Attitudes that are more cognitively accessible are more influential on observable behavior (Fazio, Powell, &Williams, 1989).

Norms describe the various social pressures people perceive to engage or not to engage in behaviors (Cialdini & Trost, 1998; Rimal, 2008; Rimal & Real, 2005). Two distinct types of norms are relevant in considering drinking and smoking behavior in college students. Descriptive norms describe the individual’s perception of what those around us actually do (Lapinski & Rimal, 2005) and thus represent judgments of the prevalence of the behavior. Injunctive norms describe the perception that we “ought” to perform a behavior based on the opinions of a certain group (Cialdini & Trost, 1998). The cognitive accessibility of injunctive and descriptive norms is the speed with which these judgments are activated from memory (Rhodes & Ewoldsen, 2009). Frequent exposure to models of smoking and drinking behavior in college residence halls is likely to make descriptive norms for these behaviors more cognitively accessible. Additionally, cognitive representations of prior experiences with social approval and disapproval for actions are linked in memory; as those associations are rehearsed, they become stronger, resulting in increased accessibility from memory (Lang, 2009; Powell & Fazio, 1984).

In the literature on college student drinking, there have been inconsistent findings as to the relative strength of injunctive and descriptive norms (Meisel, Colder, & Reed, 2016). By focusing on the accessibility of these constructs, the present work assesses how the normative constructs that are top-of-mind to college students relate to future intention to drink. In addition, peer injunctive norms (Gunther, Bolt, Borzekowski, Liebhart, & Dillard, 2006; Paek & Gunther, 2007) and family injunctive norms (Hill, Hawkins, Catalano, Abbott, & Guo, 2005) have been found to influence adolescents’ drinking and smoking intentions. Although previous studies have indicated that cognitively accessible peer norms are more strongly associated with behavioral intention than family norms for adolescent smoking (Rhodes & Ewoldsen, 2009), other work has found that family injunctive norms relate to college drinking behavior (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007). Thus, we are uncertain how influential family injunctive norms will be for newly matriculated college students. We examined both types of injunctive norms in the present research.

Behavior Intention

This research is framed within the overall structure of the reasoned action approach (RAA; Fishbein & Ajzen, 2010), which comprises the theory of reasoned action (Fishbein & Ajzen, 1975), the theory of planned behavior (Ajzen, 1991), and associated theories (e.g., Fishbein & Yzer, 2003), which assert that attitudes and norms combine to determine behavioral intention.¹ Furthermore, one’s intention to perform a behavior is the best predictor of that behavior. This assertion has been backed by extensive empirical evidence (e.g., Albarracín, Johnson, Fishbein, & Muellerleile, 2001; Orbell, 2004; Sheppard, Hartwick, & Warshaw, 1988). Indeed, a recent meta-analysis showed a strong correlation between drinking intentions and drinking behavior (r = .54; Cooke, Dahdah, Norman, & French, 2016). Although intention is not a perfect predictor of behavior (it depends on moderators, e.g., individual differences; Conner, Rodgers, & Murray, 2007), because of the practical and ethical difficulties in observing people’s behaviors in their daily lives, behavior intention is frequently used as a proxy measure. The present work was concerned with understanding the contributors to drinking and smoking decisions among newly matriculated college students, so the focus was on behavior intention among these students. The present work considered the attitudinal and normative beliefs that college students access in making decisions about whether to drink or smoke.

The Present Research

The present research aimed to test the cognitive accessibility of RAA determinants of intention (attitudes, descriptive norms, injunctive norms) as they relate to intentions to drink and smoke among new first-year college students. This extends prior work (Rhodes & Ewoldsen, 2009; Rhodes, Ewoldsen, Shen, Monahan, & Eno, 2014), which examined the accessibility of these constructs in college students for cigarette smoking, but not family injunctive or descriptive norms, or drinking as an outcome. In investigating the relative strength of injunctive family and peer, as well as descriptive norms as predictors of drinking and smoking intention in first-year college students, we assess cognitive accessibility in an untested context.

Method

Procedure

First-year students were recruited from on-campus residence halls from a large Midwestern university within 2 weeks of the beginning of the fall term. Recruitment announcements were made via residence hall staff, posters displayed in common areas, and direct recruitment by research team staff who approached students outside the room where the mobile lab was established. First-year students were explicitly chosen because of their developing norms and attitudes about drinking alcohol and smoking cigarettes. Students who chose to participate were directed to the mobile laboratory, which was set up within common-use areas in residence halls. All participants provided informed consent before beginning the study. Participants completed three tasks to measure cognitive accessibility (attitude, injunctive norm, and descriptive norm accessibility) in the software program Direct RT, which is designed to accurately capture time in order to respond to stimuli. The order of the accessibility tasks was determined randomly for each participant. After completion of all three accessibility tasks, staff started the survey program and the participant completed the remainder of the study on the computer. Demographic information, previous experiences with alcohol and tobacco, and other measures not discussed in the present report were included in the survey. Finally, each participant received $10 in cash. All procedures were approved by the institutional review board of the university where the work was conducted. Parental consent was waived for students who were not yet 18 years old.

Measures

Demographic Information

Participants provided the following demographic information: Sex was recorded as either male or female. Participants indicated race via dropdown menu, and “other” option with short response was included. Participants indicated their age in years.

Greek Life

First-year students are unable to join fraternities or sororities until their second semester of undergraduate study. However, given previous work on affiliation with Greek life and alcohol consumption (U.S. Department of Health and Human Services, 2014), we asked participants to indicate their intention to join a fraternity or sorority in the future (yes/no).

Past Drinking and Smoking Behavior

Because many incoming first-year students have had previous experiences with alcohol, and many smokers begin using before age 18 years (Centers for Disease Control and Prevention, 2014; National Institutes of Health, 2015; Norman, & Conner, 2006), past experiences with these substances were measured through two items for each behavior. These items were, “In the past 30 days, on how many days did you have at least one drink of alcohol/one cigarette?” and “On days when you drank alcohol/smoked cigarettes, how many drinks/cigarettes did you have?” These items were multiplied to create an index of past month drinking and smoking behavior.

Attitude and Norm Accessibility

Standard procedures were used to measure the accessibility of attitudes and norms (Arpan, Rhodes, & Roskos-Ewoldsen, 2007; Rhodes & Ewoldsen, 2009). To measure the cognitive accessibility of attitudes, participants were asked to indicate, as quickly as they could while maintaining response accuracy, whether they like or dislike the object/behavior described on the screen. Participants were trained to keep their index fingers on the “Q” key and the “P” key, which were assigned to indicate the “Like” and “Dislike” response, respectively. Whether the positive (“like”) response was indicated by the right (P) or left (Q) hand was counterbalanced between subjects for all tasks. Both the response (like or dislike) and the time to respond in milliseconds were recorded. Four blocks of 24 trials each were administered. The first two blocks were randomly ordered trials requiring respondents to “Press the Like key” and “Press the Dislike key,” which serves to familiarize the respondents with the response task. The next two blocks presented attitude objects, and the responses were recorded. The critical items appeared in the last block and were presented in random order with filler items to make a block of 24 trials. The critical items for alcohol use were drinking beer, binge drinking, doing shots, and playing drinking games. The critical items for tobacco use were smoking cigarettes, bumming a cigarette or cigarettes, and buying cigarettes.

To measure the cognitive accessibility of injunctive norms, participants were shown a brief descriptor of a likely important person in their lives. Family targets were parents, grandparents, and siblings. Peer targets were significant other, roommates, best friend, and friends. Participants were asked to generate a mental image of that person, or the person who most closely filled that role for them, and think about that person. For each person shown, the participant was told to press the “yes” or the “no” key to indicate whether this person wanted them to engage in each of the target behaviors related to drinking and smoking. These target behaviors included drinking beer, doing shots, binge drinking, and drinking games for alcohol and smoking cigarettes, bumming a cigarette, buying cigarettes, and cigarettes for smoking. Target items were shown in random order with filler items such as donating blood and studying. Faster reaction times to respond to these items indicated an accessible norm.

In the descriptive norm accessibility task, participants were asked whether many or not many of their friends currently engaged in the target behaviors. Drinking target behaviors were drinking beer, doing shots, binge drinking, and drinking games. Smoking target behaviors were smoking cigarettes, bumming a cigarette, buying cigarettes, and cigarettes. Faster reaction times to respond to these items indicated an accessible norm.²

For each task, a positive (prodrinking or smoking) response, that is, “Like,” “Yes,” or “Many,” was coded as 1 and a negative response (antidrinking or smoking), that is, “Dislike,” “No,” or “Not Many,” was coded as −1. Responses to the critical items for each assessment (attitude, peer injunctive, family injunctive, and descriptive norm) were averaged to form an index of the valence of the responses. Because reaction time data typically have a strong positive skew, Fazio (1990) recommends using an inverse transformation (1000/x), which was done for all reaction time measures, then reaction times were averaged across critical items. Average inverse reaction times were then multiplied by the valence index. After transformation, numbers with a higher absolute value indicate faster responses, and thus more accessible constructs. Positive numbers indicate constructs that are in the positive direction, that is, prodrinking or prosmoking, and negative numbers indicate attitudes that are opposed to drinking and smoking. Values near zero represent relatively inaccessible attitudes and norms, indicating that attitudes and norms require more time to activate from memory (see Rhodes & Ewoldsen, 2009). Other cognitive accessibility variables (injunctive peer, injunctive family, and descriptive norms) were transformed in an identical manner. Additionally, average response speeds for distractor items were transformed, averaged, and used as a control representing the general speed of response.

Behavioral Intention

Behavioral intentions for both drinking and smoking behaviors were measured with two items. Participants indicated their response to single-item measures for drinking and smoking on a scale of, 0 = Very unlikely to 10 = Very likely, “the next time you go out with friends, how likely do you think it is that you will get drunk/smoke cigarettes?” Descriptive statistics and interitem correlations of study variables are presented in Table 1 for drinking and Table 2 for smoking.

Table 1.

Interitem Correlations, Means, and Standard Deviations for Drinking Variables.

Variable	1	2	3	4	5	6	7	8	M	SD	Min	Max
1. Sex	—								0.46	.499	0.00	1.00
2. Past drinking behavior	−.190*	—							26.447	34.239	0.00	200.00
3. Greek life	.089	.299**	—						0.36	.481	0.00	1.00
4. Attitude accessibility	−.142**	.545**	.248**	—					0.012	0.953	−2.07	4.16
5. Family injunctive norm accessibility	−.139**	.334**	.032	.331**	—				−1.067	.616	−3.55	.399
6. Peer injunctive norm accessibility	−.115*	.438**	.244**	.552**	.385**	—			−.237	.987	−2.93	8.25
7. Descriptive norm accessibility	.113*	.181**	.119**	−.359**	.115*	.347*	—		.598	.859	−3.04	3.39
8. Behavioral intention	−.008	.604**	.322**	.654**	.291**	.510**	.232**	—	4.934	3.369	0.00	10.00

Note. N = 413. Sex was coded as 0 = male, 1 = female. Involvement in Greek life was coded as 0 = no intentions to join, 1 = intended to join or already belonging to Greek life.

Correlation is significant at the .05 level. **Correlation is significant at the .01 level.

Table 2.

Interitem Correlations, Means, and Standard Deviations for Smoking Variables.

Variable	1	2	3	4	5	6	7	8	M	SD	Min	Max
1. Sex	—								0.46	.499	0.00	1.00
2. Past smoking behavior	−.025	—							.501	3.628	0.00	60.00
3. Greek life	.089	−.043	—						0.36	.481	0.00	1.00
4. Attitude accessibility	−.056	.425**	.009	—					−1.258	.670	−6.89	1.36
5. Family injunctive norm accessibility	−.043	.075	.010	.325**	—				−1.404	.457	−3.31	2.03
6. Peer injunctive norm accessibility	−.083	.293**	−.001	.488**	.309**	—			−1.233	.627	−5.23	1.78
7. Descriptive norm accessibility	.140**	.122*	.057	.210**	.097	.306**	—		−.646	.920	−2.24	3.60
8. Behavioral intention	−.061	.576**	.056	.532**	.201**	.488**	.261**	—	.361	1.293	0.00	10.00

Note. N = 413. Sex was coded as 0 = male, 1 = female. Involvement in Greek life was coded as 0 = no intentions to join, 1 = intended to join or already belonging to Greek life.

Correlation is significant at the .05 level. **Correlation is significant at the .01 level.

Analysis Plan

Hierarchical linear regression analysis was used to predict intention to drink alcohol and smoke cigarettes. The first step of the model included the control variables: sex, past drinking/smoking behavior, intention to join a fraternity/sorority, and reaction time control variables for the attitude and norm accessibility measures. The second step of the model examined whether the cognitive accessibility of attitudes, injunctive family norms, injunctive peer norms, and descriptive norms were associated with behavioral intentions. Separate models were built for smoking and drinking behavior intention.

Results

Participant Characteristics

A total of 413 participants³ were included in the study. Students were eligible to participate if they were first-year students. There were no other criteria for participation. The ethnic makeup of the sample was predominantly White (76.5%), followed by multiracial or race not listed (9.8%), Asian (6.3%), Black (5.3%), Hispanic (1.5%), and Alaskan Native or Pacific Islander (0.2%). Our sample was 46% female; average age was 18 years, and 35.4% of participants intended to participate in Greek life.

Regression Analyses

Table 1 presents the correlations of all drinking variables used in the study, as well as their means and standard deviations. Table 2 presents the same information for all smoking variables.

Step 1 of the model for drinking intention was significant, F(7, 394) = 38.17, p < .001, r² = .404, and Step 2 added significantly to the variance explained by the model, F(10, 391) = 49.88, p < .001, r2 = .561 Δr² = 0.157 (see Table 3). Specifically, this analysis showed that attitude accessibility and peer injunctive norm accessibility were significant predictors of drinking intentions.

Table 3.

Regression Model for Determinants of Drinking Intentions.

Predictor	Step 1				Step 2
Predictor	B	SE	β	t	B	SE	β	t
Sex	.68	.27	.10	2.46**	.90	.24	.13	3.75**
Past drinking	.06	.004	.60	14.0**	.03	.004	.33	7.58**
Greek life	.93	.29	.13	3.25**	.57	.25	.08	2.25*
Attitude accessibility speed control	−.12	.65	−.02	−.29	−.18	.57	−.02	−.31
Family injunctive norm accessibility speed control	−1.28	.52	−.11	−2.47*	−.60	.46	−.05	−1.31
Peer injunctive norm accessibility speed control	−.19	.40	−.02	−.47	.10	.34	.01	.29
Descriptive norm accessibility speed control	.30	.51	.04	.54	−.30	.49	−.05	−.62
Attitude accessibility					1.52	.17	.43	0.09**
Family injunctive norm accessibility					−.04	.21	−.01	−.20
Peer injunctive norm accessibility					.47	.15	.14	3.19*
Descriptive norm accessibility					−.20	.15	−.05	−1.34

Note. r² = .561. p < .001. N = 413.

Indicates significance at p < .05. **Indicates significance at p < .001.

The analysis predicting smoking behavioral intentions was significant in Step 1, showing that past smoking behavior was moderately associated with intentions to smoke, F(5, 397) = 47.58, p < .001, r² = .375. Sex and Greek life were deemed to be nonsignificant demographic variables and were dropped from analysis. Step 2 was also significant and added significantly to the variance accounted for in smoking behavior, F(9, 393) = 80.10, p < .001, r2 = .647, Δr² = 0.272. Specifically, this analysis showed that attitude accessibility and peer injunctive norm accessibility were significant predictors of smoking intentions. Echoing the findings for drinking, the accessibility of the descriptive norm and family injunctive norms were not significantly related to intention to smoke. The regression model is presented in Table 4.

Table 4.

Regression Model for Determinants of Smoking Intentions.

Predictor	Step 1				Step 2
Predictor	B	SE	β	t	B	SE	β	t
Past smoking	.21	.01	.57	14.76**	.10	.01	.29	8.13**
Attitude accessibility speed control	.93	.26	.30	3.62**	1.53	.20	.50	7.71**
Family injunctive norm accessibility speed control	.18	.20	.04	.89	.07	.16	.02	.42
Peer injunctive norm accessibility speed control	−.16	.16	−.05	1.00	−.02	.12	−.01	−.17
Descriptive norm accessibility speed control	−.39	.22	.15	−1.81	.20	.17	.01	.12
Attitude accessibility					1.16	.09	.60	12.35**
Family injunctive norm accessibility					.03	.10	.01	.34
Peer injunctive norm accessibility					.34	.08	.16	4.36**
Descriptive norm accessibility					−.08	.05	−.05	−1.75

Note. r² = .647. p < .001. N = 413.

Indicates significance at p < .05. **Indicates significance at p < .001.

Discussion

The findings of the present study demonstrated that the cognitive accessibility (ease of activating in memory) of attitudes and norms was a strong predictor of behavior intention, and this is true for both alcohol consumption and cigarette smoking in newly matriculated first-year college students. Students with highly accessible drinking and smoking attitudes were more likely to intend engaging in drinking and smoking, respectively. Additionally, students who could quickly indicate that their peers approve of them drinking (i.e., those with a cognitively accessible peer injunctive norm) indicated higher drinking and smoking intentions. The effects of norm accessibility were found only for peer injunctive norms. Our data did not support the influence of the cognitive accessibility of familial injunctive norms or descriptive norms on behavioral intention. Although other work has examined the relative strength of perceived injunctive and descriptive norms in college drinking behavior (e.g., Lee, Geisner, Lewis, Neighbors, & Larimer, 2007; Neighbors et al., 2007), this is the first study of which we are aware to demonstrate that cognitively accessible injunctive norms, but not descriptive norms, predict intention to drink and smoke in beginning college students.

Overall, the cognitive accessibility of predictors in the RAA (Fishbein & Ajzen, 2010) were strongly related to drinking and smoking behavior intention, and the final models accounted for 56.1% and 64.7% of variance in behavior intention, respectively. In line with previous work, attitude accessibility acted as the strongest single determinant of intention to smoke and drink (DiBello, Miller, Neighbors, Reid, & Carey, 2018). In line with prior research, both previous experience with alcohol and smoking predicted behavioral intentions to drink and smoke (Centers for Disease Control and Prevention, 2013), and sex was significantly related to drinking intention (Norman & Conner, 2006). Sex was not related to intent to smoke cigarettes in our sample.

Our results must be considered in light of past findings that have been inconsistent with regard to the relative influence of peer and family norms on college student drinking. For example, Neighbors et al. (2007) found that both injunctive peer and parental norms were predictive of past drinking behavior and drinking problems. However, the Neighbors et al. (2007) study used the norm constructs to predict drinking behavior reported over the past 3 months. For new college students, recalling prior drinking behavior may indicate more parental influence, because they were likely still living with their parents during the time of the reported behavior. In our study, we asked about future intention to drink, and we used reports of past drinking as control variables because we were specifically interested to know how decisions to drink emerge in the college environment. Furthermore, examining the cognitive accessibility of the normative constructs helps understand the information available to students as they make behavioral decisions.

Implications for Programs to Reduce Excessive Drinking in College Students

This work has important implications for interventions with college students to reduce drinking and smoking behavior. Our findings demonstrate that the cognitive accessibility of injunctive peer norms is a more important determinant of drinking and smoking behavior than the accessibility of descriptive norms. This work suggests that a fruitful direction for research is to examine the feasibility and efficacy of developing interventions that make salient the social disapproval of excessive drinking. As noted by others (e.g., Lac & Donaldson, 2018; Meisel et al., 2016; Rutledge, McCarthy, & Lendyak, 2014), including an emphasis on injunctive norms in college drinking programs would be beneficial. For example, residence hall staff might facilitate discussions of residents’ negative reactions to their neighbors coming home drunk or smoking in common areas. Many existing programs have focused exclusively on descriptive norms (e.g., Perkins, 2003; Perkins & Berkowitz, 1986). Despite their early promise, these approaches have generally been found to be ineffective (Foxcroft, Moreira, Almeida Santimano, & Smith, 2015), although they are still prevalent (Miller et al., 2013). Including injunctive information in interventions that present personalized normative feedback to at-risk students may have promise (Dotson, Dunn, & Bowers, 2015), but finding ways to present this normative information without raising resistance is an important task for future research (Steers et al., 2016).

Limitations and Future Research

The data for this research were drawn from a cross-sectional survey conducted early in the first semester of college and used future behavioral intention as the main outcome variable. As indicated earlier, the use of intentions has a long history in the framework of the RAA. However, when feasible, it is always preferable to measure actual behavior. Acknowledging that the relationships among attitudes, injunctive and descriptive norms, and behavior intention are complex and deserving of increasingly sophisticated examination and theorizing (e.g., Lac & Donaldson, 2018; Lewis, Litt, & Neighbors, 2015), future research should include a longitudinal design to establish a causal relationship between life transitions such as entering college and attitude and norm accessibility. Also, note that we use the word “predicts” to describe the relationship between our measures of attitude and norm accessibility. We do this because the measure of behavioral intention implies a future event. Importantly, only longitudinal research can demonstrate a causal relationship.

Conclusion

Our findings indicate that salient attitudes and norms for drinking and smoking predict intention to engage in these risky behaviors among new college students. Specifically, cognitively accessible attitudes and peer injunctive norms predicted both drinking and smoking intention among first-year college students within the first 2 weeks of fall semester. The cognitive accessibility of descriptive norms was not a significant predictor of drinking or smoking, nor was the accessibility of familial injunctive norms, which replicates and extends the findings of Rhodes and Ewoldsen (2009), suggesting that injunctive peer norms are a more powerful driver of risky behavior among college students than descriptive or familial injunctive norms.

Footnotes

Authors’ Note

Bridget Potocki is now at the Department of Communication at Western Illinois University, Macomb, IL, USA.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Nancy Rhodes

References

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. doi:10.1016/0749-5978(91)90020-T

Albarracín

Johnson

B. T.

Fishbein

Muellerleile

P. A.

(2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127, 142-161.

Arpan

Rhodes

Roskos-Ewoldsen

D. R.

(2007). Attitude accessibility: Theory, methods, and future directions. In Roskos-Ewoldsen

D. R.

Monahan

(Eds.), Communication and social cognition: Theories and methods (pp. 351-376). Mahwah, NJ: Lawrence Erlbaum.

Centers for Disease Control and Prevention. (2013). 2013 youth risk behavior survey. Retrieved from https://www.cdc.gov/mmwr/pdf/ss/ss6304.pdf

Centers for Disease Control and Prevention. (2014, June). Tobacco product use among adults: United States 2012-2013. MMWR Morbidity and Mortality Weekly Report, 63, 542-547.

Cialdini

R. B.

Trost

M. E.

(1998). Social influence: Social norms, conformity, and compliance. In Gilbert

D. T.

Fiske

S. T.

Lindzey

(Eds.), The handbook of social psychology (4th ed., pp. 151-192). Boston, MA: McGraw-Hill.

Conner

Rodgers

Murray

(2007). Conscientiousness and the intention-behavior relationship: Predicting exercise behavior. Journal of Sport & Exercise Psychology, 29, 518-533.

Cooke

Dahdah

Norman

French

D. P.

(2016). How well does the theory of planned behaviour predict alcohol consumption? A systematic review and meta-analysis. Health Psychology Review, 10, 148-167.

DiBello

A. M.

Miller

M. B.

Neighbors

Reid

Carey

K. B.

(2018). The relative strength of attitudes versus perceived drinking norms as predictors of alcohol use. Addictive Behaviors, 80, 39-46.

10.

Dotson

K. B.

Dunn

M. E.

Bowers

C. A.

(2015). Stand-alone personalized normative feedback for college student drinkers: A meta-analytic review, 2004 to 2014. PLoS One, 10, e0139518. doi:10.1371/journal.pone.0139518

11.

Fazio

R. H.

(1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In Zanna

(Ed.), Advances in experimental social psychology (Vol. 23, pp. 75-109). Orlando, FL: Academic Press.

12.

Fazio

R. H.

Powell

M. C.

Williams

C. J.

(1989). The role of attitude accessibility in the attitude-to-behavior process. Journal of Consumer Research, 6, 280-288.

13.

Fishbein

Ajzen

(1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

14.

Fishbein

Ajzen

(2010). Predicting and changing behavior: The reasoned action approach. New York, NY: Psychology Press.

15.

Fishbein

Yzer

M. C.

(2003). Using theory to design effective health behavior interventions. Communication Theory, 13, 164-183.

16.

Foxcroft

D. R.

Moreira

M. T.

Almeida Santimano

N. M.

Smith

L. A.

(2015). Social norms information for alcohol misuse in university and college students. Cochrane Database of Systematic Reviews, 1, CD006748.

17.

Gunther

A. C.

Bolt

Borzekowski

D. L.

Liebhart

J. L.

Dillard

J. P.

(2006). Presumed influence on peer norms: How mass media indirectly affect adolescent smoking. Journal of Communication, 56, 52-68. doi:10.1111/j.1460-2466.2006.00002.x56

18.

Hill

K. G.

Hawkins

J. D.

Catalano

R. F.

Abbott

R. D.

Guo

(2005). Family influences on the risk of daily smoking initiation. Journal of Adolescent Health, 37, 202-210. doi:10.1016/j.jadohealth.2004.08.014

19.

Lac

Donaldson

C. D.

(2018). Testing competing models of injunctive and descriptive norms for proximal and distal reference groups on alcohol attitudes and behavior. Addictive Behaviors, 78, 153-159. doi:10.1016/j.addbeh.2017.11.024

20.

Lang

(2009). The limited capacity model of motivated mediated message processing. In Nabi

Oliver

M. B.

(Eds.), The SAGE handbook of mass media processes and effects (pp. 193-204). Thousand Oaks, CA: Sage.

21.

Lapinski

M. K.

Rimal

R. N.

(2005). An explication of social norms. Communication Theory, 15, 127-147. doi:10.1111/j.1468-2885.2005.tb00329.x

22.

Lee

C. M.

Geisner

I. M.

Lewis

M. A.

Neighbors

Larimer

M. E.

(2007). Social motives and the interaction between descriptive and injunctive norms in college student drinking. Journal of Studies on Alcohol and Drugs, 68, 714-721. doi:10.15288/jsad.2007.68.714

23.

Lewis

M. A.

Litt

D. M.

Neighbors

(2015). The chicken or the egg: Examining temporal precedence among attitudes, injunctive norms, and college student drinking. Journal of Studies on Alcohol and Drugs, 76, 594-601.

24.

Meisel

S. N.

Colder

C. R.

Read

J. P.

(2016). Addressing inconsistencies in the social norms drinking literature: Development of the injunctive norms drinking and abstaining behaviors questionnaire. Alcoholism: Clinical and Experimental Research, 40, 2218-2228. doi:10.1111/acer.13202

25.

Miller

M. B.

Leffingwell

Claborn

Meier

Walters

Neighbors

(2013). Personalized feedback interventions for college alcohol misuse: An update of Walters & Neighbors (2005). Psychology of Addictive Behaviors, 27, 909-920. doi:10.1037/a0031174

26.

National Cancer Institute. (2015). Cancer trends progress report. Bethesda, MD: National Institutes of Health, U.S. Department of Health & Human Services. Retrieved from http://progressreport.cancer.gov

27.

National Institutes of Health. (2015). College drinking. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism. Retrieved from https://pubs.niaaa.nih.gov/publications/CollegeFactSheet/Collegefactsheet.pdf

28.

Neighbors

Lee

C. M.

Lewis

M. A.

Fossos

Larimer

M. E.

(2007). Are social norms the best predictor of outcomes among heavy-drinking college students? Journal of Studies on Alcohol and Drugs, 68, 556-565. doi:10.15288/jsad.2007.68.556

29.

Norman

(2011). The theory of planned behavior and binge drinking among undergraduate students: Assessing the impact of habit strength. Addictive Behaviors, 36, 502-507. doi:10.1016/j.addbeh.2011.01.025

30.

Norman

Conner

(2006). The theory of planned behavior and binge drinking: Assessing the moderating role of past behavior within the theory of planned behavior. British Journal of Health Psychology, 11, 55-70.

31.

Orbell

(2004). Intention-behavior relations: A self-regulation perspective. In Haddock

Maio

G. R.

(Eds.), Contemporary perspectives on the psychology of attitudes (pp. 145-168). New York, NY: Psychology Press.

32.

Ostafin

B. D.

Bauer

Myxter

(2012). Mindfulness decouples the relation between automatic alcohol motivation and heavy drinking. Journal of Social and Clinical Psychology, 31, 729-745.

33.

Ostafin

B. D.

Marlatt

G. A.

(2008). Surfing the urge: Experiential acceptance moderates the relation between automatic alcohol motivation and hazardous drinking. Journal of Social and Clinical Psychology, 27, 404-418. doi:10.1521/jscp.2008.27.4.404

34.

Ostafin

B. D.

Marlatt

G. A.

Greenwald

A. G.

(2008). Drinking without thinking: An implicit measure of alcohol motivation predicts failure to control alcohol use. Behaviour Research and Therapy, 46, 1210-1219. doi:10.1521/jscp.2008.27.4.404

35.

Paek

H. J.

Gunther

A. C.

(2007). How peer proximity moderates indirect media influence on adolescent smoking. Communication Research, 34, 407-431. doi:10.1177/0093650207302785

36.

Perkins

H. W.

(2003). The social norms approach to preventing school and college age substance abuse: A handbook for educators, counselors, and clinicians. San Francisco, CA: Jossey-Bass.

37.

Perkins

H. W.

Berkowitz

A. D.

(1986). Perceiving the community norms of alcohol use among students: Some research implications for campus alcohol education programming. International Journal of the Addictions, 21, 961-976.

38.

Powell

M. C.

Fazio

R. H.

(1984). Attitude accessibility as a function of repeated attitudinal expression. Personality and Social Psychology Bulletin, 10, 139-148. doi:10.1177/0146167284101016

39.

Rhodes

Ewoldsen

D. R.

(2009). Attitude and norm accessibility and cigarette smoking. Journal of Applied Social Psychology, 39, 355-372. doi:10.1111/j.1559-1816.2009.00529.x

40.

Rhodes

Ewoldsen

D. R.

Shen

L. J.

Monahan

J. L.

Eno

(2014). The accessibility of family and peer norms in young adolescent risk behavior. Communication Research, 41, 3-26.

41.

Rimal

R. N.

(2008). Modeling the relationship between descriptive norms and behaviors: A test and extension of the theory of normative social behavior (TNSB). Health Communication, 23, 103-116. doi:10.1080/10410230801967791

42.

Rimal

R. N.

Real

(2005). How behaviors are influenced by perceived norms: A test of the theory of normative social behavior. Communication Research, 32, 389-414.

43.

Rutledge

P. C.

McCarthy

B. J.

Lendyak

R. M.

(2014). Let’s get this party started: The effects of descriptive and injunctive norms on preparty behavior in college students. Substance Use & Misuse, 49, 166-175. doi:10.3109/10826084.2013.824473

44.

Sheppard

B. H.

Hartwick

Warshaw

P. R.

(1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325-343.

45.

Steers

M. N.

Coffman

A. D.

Wickham

R. E.

Bryan

J. L.

Caraway

Neighbors

(2016). Evaluation of alcohol-related personalized normative feedback with and without an injunctive message. Journal of Studies on Alcohol and Drugs, 77, 337-342.

46.

Stern

Terry-McElrath

Patrick

(2017). Beverage-specific patterns of 5+alcoholic drink consumption by young adults in the US. Addictive Behaviors, 65, 19-24. doi:10.1016/j.addbeh.2016.09.010

47.

Substance Abuse and Mental Health Services Administration. (2013). Results from the 2013 national survey on drug use and health: Summary of national findings. Rockville, MD: Author.

48.

U.S. Department of Health and Human Services. (2012). Preventing tobacco use among youth and young adults: A report of the Surgeon General. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health.

49.

U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the Surgeon General. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health.