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Predicting Young Adults’ Intentions to Get the H1N1 Vaccine: An Integrated Model Z. Janet Yang

a

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Department of Communication , State University of New York at Buffalo , Buffalo , New York , USA Published online: 28 May 2014.

To cite this article: Z. Janet Yang (2014): Predicting Young Adults’ Intentions to Get the H1N1 Vaccine: An Integrated Model, Journal of Health Communication: International Perspectives, DOI: 10.1080/10810730.2014.904023 To link to this article: http://dx.doi.org/10.1080/10810730.2014.904023

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Journal of Health Communication, 0:1–11, 2014 Copyright # Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2014.904023

Predicting Young Adults’ Intentions to Get the H1N1 Vaccine: An Integrated Model Z. JANET YANG

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Department of Communication, State University of New York at Buffalo, Buffalo, New York, USA

Young adults 19 through 24 years of age were among the populations that had the highest frequency of infection from the 2009 H1N1 pandemic. However, over the 2009–2010 flu season, H1N1 vaccine uptake among college students nationwide was around 8%. To explore the social cognitive factors that influenced their intentions to get the H1N1 vaccine, this study compares the predictive power of the theory of planned behavior (TPB), the health belief model (HBM), and an integrated model. The final model shows that several HBM variables influenced behavioral intentions through the TPB variables. The results suggest that even though the TPB seemed a superior model for behavior prediction, the addition of the HBM variables could inform future theory development by offering health-specific constructs that potentially enhance the predictive validity of TPB variables.

Declared as a global pandemic by the World Health Organization in June 2009, the 2009 H1N1 pandemic was the sixth pandemic of influenza that occurred in the past century (Neumann, Noda, & Kawaoka, 2009). Because older populations develop stronger immune response from the seasonal flu virus mutation, people between 20 and 40 years of age were at a higher risk of infection. As of November 2009, 79% of the confirmed U.S. cases had been in people younger than 30 years of age, and only 2% in people older than 65 years (Soares, 2009). Given the high-density living environment on college campuses, influenza—similar to other types of infectious diseases—spreads at a higher speed in university settings (Moe, Christmas, Echols, & Miller, 2001). As of late October 2009, 97% of the 274 colleges and universities participating in a pandemic influenza surveillance network organized by the American College Health Association reported new instances of influenza-like illness. As of February 2010, the majority of more than 90,000 cases reported through this network presented H1N1 symptoms. Thus, the college student population warrants special attention as a research target group. Given this elevated risk, the Centers for Disease Control and Prevention recommended young adults 19 through 24 years of age as one of the initial target groups for H1N1 vaccination. However, over the 2009–2010 flu season, vaccine uptake among college students was around 8% (American College Health Association, 2010), while the national

Address correspondence to Z. Janet Yang, Department of Communication, State University of New York at Buffalo, 359 Baldy Hall, Buffalo, NY 14260, USA. E-mail: zyang5@ buffalo.edu

influenza vaccination coverage for the initial target groups (including pregnant women, health care workers, young adults from 19 to 24 years old, and so forth) was 34.2% (Centers for Disease Control and Prevention, 2010a). As the H1N1 influenza pandemic spread across the globe, it received heightened media attention in the United States, rated as one of the top stories of 2009 by Time magazine (Altman, 2009). Thus, public awareness about H1N1 and the H1N1 vaccine was high over the 2009–2010 flu season (Sunil & Zottarelli, 2011). In response to the Centers for Disease Control and Prevention’s emergency risk communication effort, health services at most colleges and universities launched information campaigns to promote preventive behaviors against the H1N1 virus including vaccination (Centers for Disease Control and Prevention, 2010b). Most universities also offered free vaccine to registered students. Thus, college students were likely to have both high awareness of the H1N1 vaccine and low physical barriers to access the vaccine. This scenario presents a unique context to study why young adults chose not to get vaccinated after the H1N1 pandemic. First, getting the H1N1 vaccine is a volitional act (Ajzen & Fishbein, 2005). People can make a conscious decision whether to get vaccinated. Second, college students might base their decisions on the behavior and expectations of influential others in their social environment, especially their friends and family. Third, exposure to information about the H1N1 vaccine likely influenced their decisions. Thus, building on the theory of planned behavior (TPB) and the health belief model (HBM), two well-known and empirically supported models of health behavior, this research draws new linkages among the established concepts

2 in both models. In this sense, it makes a theoretical contribution to health communication research by testing a novel congregation of these concepts. In addition, possible mediation and moderation among the variables are tested to streamline key variables’ direct and indirect effects on behavioral intentions. The practical goal of this research is to identify key social cognitive factors that could inform the promotion of vaccine uptake in this high-risk population.

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TPB The TPB suggests that an individual’s behavior can be predicted by his or her behavioral intention. This behavioral intention, in turn, has three antecedents: (a) a favorable or unfavorable attitude toward performing the act, (b) perceived social pressure to performing the act (subjective norm), and (c) perceived capacity to perform the act (perceived behavioral control) (Ajzen, 1988). Ajzen and Fishbein (2005) found that across different meta-analyses, these three variables correlated well with a wide range of behavioral intentions (attitude: .45 to .60; subjective norm: .34 to .42; perceived behavioral control: .35 to .46). The relative effect of attitude, subjective norm, and perceived behavioral control on behavioral intentions, however, can vary as a function of the behavior and the population under investigation. For example, when personal control or autonomy is a determining factor in the performance of the behavior, perceived behavioral control tends to exert a greater influence on behavioral intention (Ajzen & Fishbein, 2005). Over the past three decades, the TPB has guided hundreds of empirical tests to explain why people engage in certain behaviors (for a review, see Ajzen & Fishbein, 2005), including a wide array of studies related to health behaviors (Ajzen & Manstead, 2007). Most of these studies used their research findings to guide the design of persuasive messages and interventions that could effectively engender behavioral change (Aldrich & Cerel, 2009; Andrews, Silk, & Eneli, 2010; Campo et al., 2003). The TPB serves this purpose well because of the explanatory power it has exemplified in predicting behavioral intentions (Albarracin, Johnson, Fishbein, & Muellerleile, 2001; Godin & Kok, 1996; Sheeran & Taylor, 1999). Ajzen (1991) suggested that behavioral beliefs, normative beliefs, and control beliefs were fundamental to the formation of attitude, subjective norm, and perceived behavioral control. For example, behavioral beliefs refer to an individual’s beliefs about the likelihood that performing the behavior would lead to certain outcomes. Then, the likelihood that these outcomes would occur, together with the individual’s favorable or unfavorable evaluations of these outcomes, constitute attitude. Similarly, normative beliefs are composed of two aspects: (a) perceived behavioral expectations from an individual’s family, friends, and other important referent groups in his or her social environment; and (b) the individual’s motivation to comply with these expectations. Control beliefs involve factors that can facilitate or impede one’s performance of the behavior, including the perceived likelihood of a given control factor being present

Z. J. Yang and the power of the control factor to influence behavior. TPB-based research usually focuses on self-efficacy and controllability to assess perceived behavioral control (Ajzen, 1991). This study first tests whether attitude (Hypothesis 1), subjective norm (Hypothesis 2), and self-efficacy (Hypothesis 3) are positively related to behavioral intentions. HBM The HBM proposes that health related behavior is a function of perceived threat (perceived susceptibility and severity) and outcome expectations (perceived benefits and barriers). The HBM also includes a cues to action variable whereby the individual is spurred to adopt the preventive behavior by internal or external stimuli such as exposure to information from the mass media or through discussions with other people (Rosenstock, 1966). Later, scholars have suggested that self-efficacy should be incorporated into the HBM as a separate independent variable (Rosenstock, Strecher, & Becker, 1988). Previous research has examined the HBM with a number of preventive behaviors including physical activity (Juniper, Oman, Hamm, & Kerby, 2004; O’Connell, Price, Roberts, Jurs, & McKinley, 1985), vaccinations (Blue & Valley, 2002; Montano, 1986), diet (Becker, Maiman, Kirscht, Don, & Drachman, 1977; Chew, Palmer, & Kim, 1998), cancer screenings and self-exams (McClenahan, Shevlin, Adamson, Bennett, & O’Neill, 2007; Millar, 1997), and regular physical exams (Von Ah, Ebert, Ngamvitroj, Park, & Kang, 2004). Rosenstock (1966) suggested that perceived susceptibility referred to one’s subjective risks of contracting a disease or a health condition. Janz and Becker (1984) further conceptualized perceived susceptibility as an individual’s feeling of their personal vulnerability. Perceived severity, on the other hand, involved both the emotional arousal and personal difficulties induced by the thought of a health threat or a condition. Janz and Becker (1984) also suggested that perceived severity should include evaluations of both medical consequences (e.g., death) and possible social consequences. People were expected to vary widely in their acceptance of personal susceptibility and severity. Once perceived susceptibility and severity create a readiness for action, outcome expectations define the particular course of action that the individual takes. Here, perceived benefits focus on the effectiveness of a recommended action in reducing one’s personal susceptibility or severity. Perceived barriers, however, represent a range of logistical or psychological issues that potentially deter the individual from taking the action. For example, perceived barriers might involve the expenses, inconvenience, and pain associated with the recommended action. Rather than incorporating self-efficacy into the perceived barriers component of the HBM (Janz & Becker, 1984), Rosenstock and colleagues (1988) argued that self-efficacy should be considered an independent variable because it helped to account for both the initiation and the maintenance of behavioral change. In addition, self-efficacy was centered on an individual’s capacity to perform a behavior and personal control, which

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H1N1 Vaccine Uptake set a clearer theoretical boundary from the broad range of other barriers. When both perceived threat and outcome expectations are taken into account, cues to action depict internal or external stimuli that trigger the transition from psychological readiness to actual behavior. Health communication through the mass media and interpersonal discussions could serve as cues to action that complete the behavioral change proposed in the HBM (Harrison, Mullen, & Green, 1992). However, as Carpenter (2010) pointed out, ‘‘cue to action is the most underdeveloped and rarely measured or researched element of the model (p. 662). Janz and Becker (1984) reviewed empirical research based on the HBM and found substantial evidence supporting the HBM in the explanation and prediction of individuals’ health-related behaviors. In particular, perceived barriers have performed well consistently, whereas perceived severity had relatively lower predictive power as compared with the other variables. Carpenter (2010) also found that ‘‘benefits and barriers were consistently the strongest predictors’’ (p. 661). Other meta-analyses suggested that even though the HBM was predictive of behavior, it did not possess the same explanatory power as social cognitive theory or the TPB (Zimmerman & Vernberg, 1994); the HBM was also not as good at predicting future behavior as compared with retrospective studies (Harrison et al., 1992). Collectively, these meta-analyses pointed to a need for new research to ‘‘abandon the simple four-variable additive model and instead examine possible mediation and moderation among the variables’’ (Carpenter, 2010, p. 668). For instance, Strecher, Champion, and Rosenstock (1997) suggested that future HBM research should look at more complex causal models and examine interactions among the variables. The next step of study is to test whether perceived threat (Hypothesis 4), outcome expectations (Hypothesis 5), and cues to action (Hypothesis 6) were positively related to behavioral intentions. Integrated Model Although the TPB and the HBM emphasize different aspects of behavioral formation, there are natural linkages between them. First, both models recognize the importance of self-efficacy in determining individuals’ adoption of a health behavior. That is, personal control is viewed as an important factor that leads to the initiation of a behavior. Furthermore, the TPB’s subjective norm might influence how the HBM’s cues to action trigger health behavioral change. In particular, even when external stimuli exist in one’s social environment, people who are cued into taking the recommended preventive action are more likely those who are more subjected to social norms. Thus, subjective norm is expected to mediate the relationship between cues to action and behavioral intentions so that when subjective norm is added to the model, the direct relationship between cues to action and behavioral intentions becomes nonsignificant (Hypothesis 7). Another linkage between the TPB and the HBM comes from recent theory development. In particular, communication scholars emphasized that a cost-and-benefit analysis

3 approach should become an integral part of the TPB’s conceptualization and evaluation of attitude (Fishbein & Yzer, 2003). Following a cost-and-benefit analysis of the potential outcome, researchers could identify behavioral beliefs that are most central to an individual’s decision to adopt a recommended behavior. Therefore, the HBM’s perceived threat and outcome expectations variables could offer insight on the type of behavioral beliefs that warrant inclusion as part of the assessment of attitude in the TPB. Thus, this study also examined whether perceived threat and outcome expectations influence behavioral intentions indirectly through attitude. Because the indirect relationships here will exemplify in different directions, this study will explore the nature of these relationships through a research question (Research Question 1). The integrative model of behavioral prediction (Fishbein, 2000) reflected this integration. However, since getting the vaccine does not require specific skills and environmental constraint is not a major concern for college students who can use their school’s health services, the two new additions introduced by the integrative model of behavioral prediction are not quite relevant here. Thus, this study is focused on the original TPB when exploring theory integration. Fishbein (2002) argued that many behavioral determinants such as perceived threats and benefits were reflected in the behavioral, normative, and control beliefs and served as antecedents to attitude, subjective norms, and perceived behavioral control. In addition, for new variables to be included in the TPB, they should account for a meaningful amount of explained variance in behavioral intention independent of existing predictors (Ajzen, 1991; Conner & Armitage, 1998). Thus, the goal of this theory integration is to explore the linkages between the HBM and the TPB, specifically focusing on how the HBM variables might serve as antecedents to existing TPB variables to enhance the overall predictive power of the model. Most studies linking these two models together have focused on comparing the utility of the two models in predicting behavioral intention, showing that the TPB is often superior (Bish, Sutton, & Golombok, 2000; McClenahan et al., 2007). Reid and Aiken (2011) integrated key concepts from five models of health behavior to predict condom use intentions, including the TPB and the HBM. However, their integrated psychosocial model ‘‘favored completeness over parsimony’’ (Reid & Aiken, 2011, p.1511), making their attempt at theory integration seems more data driven than theory driven. The authors also failed to provide a clear theoretical underpinning that justifies the model specification. In contrast, on the basis of the aforementioned rationale, this study tests a model that effectively integrates key concepts from the TPB and the HBM (Figure 1). Specifically, this study first tests the applicability of the TPB and the HBM in predicting college students’ intention to get the H1N1 vaccine. Then, to isolate the factors from each model that are most influential on behavioral intentions, the predictive power of an integrated model is assessed.

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Z. J. Yang realistic estimate of the likelihood that they would get the vaccine. Attitude Ajzen (1988) suggested that attitude measures, which are an extension of behavioral beliefs, should contain items representing both instrumental and experiential attitudes toward a behavior. Thus, three semantic differential scales were used to gauge these two aspects on 0–10 scales (averaged scale: M ¼ 6.23, SD ¼ 2.13, a ¼ .82).

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Subjective Norm

Fig. 1. Proposed integrated model. H ¼ hypothesis; RQ ¼ research question.

Method Data were collected through a self-administered survey of 420 college students at a large northeastern public university in October 2010. Thirty students indicated that they had never heard of the H1N1 flu, so they were removed from subsequent analyses (N ¼ 390). Respondents were recruited from a large undergraduate class with students from different majors and were offered course research credit for participating. Total enrollment was 473, so the response rate was 88.8% and the completion rate was 82.5%. The sample was predominantly white (73.5%), with an average age of 20 years old (SD ¼ 2.87) and a median family income of $62,500. The sample was gender balanced (50.8% female). Although there are genuine concerns about generalizability when collecting data from undergraduate students, it is important to study this particular population because of the low uptake rate during the H1N1 pandemic. Furthermore, proposed relationships in both the TPB and the HBM have been identified numerous times in nonstudent samples. Because the focus of this study is to compare the predictive power of these two models, the student sample would suffice this theory testing purpose. The following sections describe the key variables and their operationalization in this study. Key measures were adopted from past research based on the TPB and the HBM (Ajzen & Fishbein, 2005; Janz & Becker, 1984; Rosenstock, 1966). Table 1 shows descriptive statistics for all variables and the averaged scales, along with reliability scores. Behavioral Intentions Two direct measures were used to assess respondents’ intentions to get the vaccine on 5-point Likert scales (averaged scale: M ¼ 2.75, SD ¼ 0.94, a ¼ .84). Since data were collected right before the beginning of the 2010 flu season, it was expected that respondents would have a fairly

Ajzen (1988) suggested that both the normative belief strength and the motivation to comply with the referent group should be used to obtain a complete measure of subjective norm. Even though direct measures of subjective norms are more commonly used in TPB studies, this indirect measurement strategy has also been used widely in the literature (see, for example, Giles et al., 2007; Nejad, Wertheim, & Greenwood, 2005). Thus, subjective norm was assessed as the product term of these two aspects on 0–10 scales (averaged scale: M ¼ 25.80, SD ¼ 22.01, a ¼ .79). Self-Efficacy Ajzen and Fishbein (2005) suggested that both capacity and control beliefs should be used to assess self-efficacy. Thus, three scales were used to gauge these two aspects on 0–10 scales, with the first two items assessing capacity beliefs and the last item assessing control beliefs (averaged scale: M ¼ 5.81, SD ¼ 2.36, a ¼ .91). Perceived Susceptibility Rosenstock (1966) suggested that susceptibility referred to one’s subjective risks of contracting a disease or a health condition. Janz and Becker (1984) also argued that people varied widely in their feelings of personal vulnerability to a condition. Thus, four measures were used to assess perceived susceptibility to the H1N1 flu on 5-point Likert scales (averaged scale: M ¼ 3.56, SD ¼ 0.73, a ¼ .75). Perceived Severity Rosenstock (1966) suggested that perceived severity involved both the degree of emotional arousal created by the thought of a disease and by the kinds of difficulties individuals believed a given health condition would create for them. Janz and Becker (1984) also suggested that this dimension should include evaluations of both medical consequences (e.g., death) and possible social consequences. Thus, four measures were used to assess these two aspects on 5-point Likert scales related to the H1N1 flu (averaged scale: M ¼ 2.94, SD ¼ 0.88, a ¼ .77). Perceived Benefits Two items were used to assess respondents’ perceived benefits of the H1N1 vaccine as it related subjectively to

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H1N1 Vaccine Uptake Table 1. Descriptive data for key variables

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Concept and measure Behavioral intention (1–5 scale) Do you plan to get the H1N1 vaccine this year? If you were given an opportunity to get the H1N1 vaccine, how likely is it that you would? Averaged scale Reliability score (a) Attitude (0–10 scale) Generally speaking, I feel that getting the H1N1 vaccine is: Bad. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Good Harmful. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Beneficial Foolish. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Wise Averaged scale Reliability score (a) Subjective norm (0–10 scale) My family members think I should get the H1N1 vaccine. Generally speaking, I want to do what my family members think I should do. My close friends think I should get the H1N1 vaccine. Generally speaking, I want to do what my close friends think I should do. Product term Reliability score (a) Self-efficacy (0–10 scale) I know just what to do to get the H1N1 vaccine. I know how to get the H1N1 vaccine. It is easy for me to get the H1N1 vaccine if I wanted to. Averaged scale Reliability score (a) Perceived susceptibility (1–5 scale) The H1N1 flu can happen to many people, including my family, loved ones and friends. I am at risk of suffering from the H1N1 flu. The H1N1 flu can happen anytime to anyone, such as a healthy individual. The chance of me contracting the H1N1 flu is high. Averaged scale Reliability score (a) Perceived severity (1–5 scale) The H1N1 flu causes death quickly. Many people can die from the H1N1 flu. A person who contracts the H1N1 flu will die if not treated. The H1N1 flu is a fatal virus. Averaged scale Reliability score (a) Perceived benefits (1–5 scale) Taking the H1N1 vaccine will be effective in preventing me from contracting the flu. Taking the H1N1 vaccine will help to boost my body immunity in battling the virus. Averaged scale Reliability score (a) Perceived barriers (1–5 scale) The H1N1 vaccine is affordable for me (reversed). The H1N1 vaccine is costly for me. The H1N1 vaccine is easily accessible to me (reversed). Averaged scale Reliability score (a) Response costs: Taking the H1N1 vaccine will result in certain side effects to my body. My body may react adversely to the H1N1 vaccine. It is risky to take the H1N1 vaccine. Averaged scale Reliability score (a)

M

SD

2.54 2.95

1.03 1.00

2.75

0.94 .84

5.95

2.60

6.55 6.19 6.23

2.27 2.54 2.13 .82

4.69 5.53 3.90 4.64 25.80

2.83 2.57 2.48 2.55 22.01 .79

5.46 5.80 6.18 5.81

2.54 2.63 2.54 2.36 .91

4.23 3.39 3.87 2.73 3.56

0.78 1.21 0.95 0.99 0.73 .75

2.65 3.43 2.79 2.91 2.94

1.04 1.11 1.22 1.18 0.88 .77

3.65 3.66 3.65

0.97 1.01 0.99 .99

2.19 2.19 2.40 2.26

1.01 1.02 0.96 0.83 .78

3.36 3.24 3.25 3.28

0.95 0.96 0.83 0.84 .90 (Continued )

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Z. J. Yang

Table 1. Continued

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Concept and measure Cues to action (1–5 scale) In the past one year, how much attention have you paid to news related to the H1N1 flu from: Television Newspaper Internet Radio Magazine Averaged scale Reliability score (a) How often have you discussed the H1N1 flu in the following contexts in the past one year? Family in person Friends in person Family and friends online Averaged scale Reliability score (a) Control variables Age Gender Ethnicity What is your family’s income level, before tax? Did you get the H1N1 vaccine this past flu season? Knowledge test score

the reduction of their susceptibility to the H1N1 flu (Rosenstock, 1974) on 5-point Likert scales (averaged scale: M ¼ 3.65, SD ¼ 0.99, a ¼ .99). Perceived Barriers Six items were used to assess the respondents’ negative perceptions associated with getting the vaccine. Three items were focused on logistic barriers related to getting the vaccine, such as cost and accessibility. The other three were focused on respondents’ concerns about the potential negative outcome from getting the vaccine, such as side effects and adverse reaction. Factor analysis showed that these six items loaded on two distinct factors. Thus, these six items were averaged into two scales, with the first labeled perceived barriers (averaged scale: M ¼ 2.26, SD ¼ 0.83, a ¼ .78) and the latter labeled response costs (averaged scale: M ¼ 3.28, SD ¼ 0.84, a ¼ .90), a concept originally proposed in the Protection Motivation Theory (Maddux & Rogers, 1983). Cues to Action To assess how news from the mass media serves as external stimuli, respondents’ attention to news related to the H1N1 flu from five mass media were assessed on 5-point scales (averaged scale: M ¼ 2.45, SD ¼ 0.89, a ¼ .80). Similarly, the amount of interpersonal discussion that the respondents had with their family and friends about the H1N1 flu over the past year was used to assess external stimuli from their social environment on 5-point scales (averaged scale:

M

SD

3.08 2.22 2.94 2.12 1.90 2.45

1.26 1.21 1.28 1.18 1.06 0.89 .80

2.86 2.75 1.72 2.44

1.13 1.17 1.07 0.91 .74

19.92 50.8% female 73.5% White $62,500 (median) 18.8% yes 5.86

2.87

1.38

M ¼ 2.44, SD ¼ 0.91, a ¼ .74). This measurement strategy is different from the more direct measures used in some HBM research (Blue & Valley, 2002) or the experimental manipulation of cues to action (Mattson, 1999). However, few HBM studies have empirically assessed the contribution of this variable (Carpenter, 2010; Janz & Becker, 1984). Given the heightened media attention (Altman, 2009) and extensive public discourse (Austin, Liu, & Jin, 2012) surrounding the H1N1 influenza, this measurement strategy might offer a more realistic assessment of the external stimuli that potentially activated the protective health behavior. Control Variables Control variables included age, gender, ethnicity, household income, respondents’ experience with the H1N1 vaccine in the past flu season (18.8% received), and their knowledge about H1N1, based on correct (1)=incorrect (0) answers judging eight statements such as ‘‘A person carrying the H1N1 influenza can spread the virus by coughing in public.’’ Scores from these eight items were summed to create the knowledge test score (ranged from 0 to 8, M ¼ 5.88, SD ¼ 1.39). Analysis Data were analyzed using hierarchical ordinary least square regression in SPSS 20 and path analysis in LISREL 8.80. Hierarchical ordinary least square regression allows for the assessment of incremental changes in explained variance in

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H1N1 Vaccine Uptake the dependent variable from variables entered at each step while controlling for variables entered in previous steps (Cohen, Cohen, West, & Aiken, 2003). Demographic and other control variables were entered in the first block, followed by other key variables from the TPB and the HBM. With its ability to simultaneously estimate paths among all independent and dependent variables, path analysis was conducted to test the proposed integrated model (Figure 1). The multivariate normality test through the PRELIS package of LISREL 8.80 indicated that the multivariate distribution of the variables did not meet the normality requirement (relative multivariate kurtosis ¼ 1.19, p < .05). Thus, asymptotic covariance matrix was weighted for model testing. Once the direct and indirect relationships among the key variables were established, additional mediation analysis with a bootstrapping approach using the INDIRECT macro (Hayes, 2009) and moderation analysis using the MODPROBE macro (Hayes & Matthes, 2009) were conducted to further explore the indirect relationships.

Results In the TPB model based on regression analysis, attitude (b ¼ .25, p < .001) and subjective norm (b ¼ .40, p < .001) were positively related to behavioral intentions, supporting the first two hypotheses. Respondents who had more favorable attitude toward the vaccine were more likely to get the vaccine in the upcoming flu season. Similarly, respondents who felt greater social pressure to get the vaccine were also more likely to say that they would. In comparison, Hypothesis 3 was not supported. Self-efficacy was not significantly related to behavioral intentions, showing that behavioral control was not a central concern that respondents had about getting the vaccine. The TPB accounted for 39% of the variance in behavioral intentions (Table 2). The next group of hypotheses was focused on the HBM. Regression results showed that perceived susceptibility was significantly related to behavioral intentions (b ¼ .13, p < .05). Respondents who felt that they were susceptible to the H1N1 virus were more likely to get the vaccine. Thus, Hypothesis 4 was partially supported. Both perceived benefits (b ¼ .27, p < .001) and one dimension of perceived barriers, response costs (b ¼
.24, p < .001), were significantly related to behavioral intentions. Respondents who understood the benefits of the vaccine were more likely to get it, but those who had concerns about the drawbacks of the vaccine were less likely to get it. Thus, Hypothesis 5 was partially supported. Between the two measures of cues to action, only interpersonal discussion was significantly related to behavioral intentions (b ¼ .15, p < .05). Respondents who had discussed with their family and friends about the H1N1 flu were more likely to get the vaccine. Thus, Hypothesis 6 was also partially supported. Controlling for the other HBM variables, self-efficacy was not significantly related to behavioral intentions. The HBM accounted for 30% of the variance in behavioral intentions. When key variables from both models were entered into one regression model, most of the existing relationships

Table 2. Hierarchical regression results for model comparison TPB model b Age Gender (male ¼ 1, female ¼ 2) White (White ¼ 1, other ¼ 0) Income Knowledge test score Past behavior Adjusted R2 Attitude DR2 Subjective norm DR2 Perceived behavioral control DR2 Perceived susceptibility Perceived severity DR2 Perceived benefits Perceived barriers Response costs DR2 News attention Discussion DR2 Adjusted R2 Analysis of variance 

p < .05.



p < .01.



HBM model b

Integrated model b

.00
.03

.08 .02

.04
.01

.08

.02

.07

.02 .01 .15 .13 .25 .18 .40 .08
.07 .00 — — — — — — — — — — .39 F(9, 322) ¼ 4.24

.07 .07 .22 .13 — — — — .07 .04 .13 .05 .02 .27 .04
.24 .11
.03 .15 .02 .30 F(14, 314) ¼ 11.23

.01 .02 .14 .13 .21 .17 .31 .09
.06 .00 .09 .03 .01 .13 .04
.17 .03 .08
.04 .00 .41 F(16, 310) ¼ 15.33

p < .001.

remained significant with the exception of perceived susceptibility and interpersonal discussion. This change indicated that it was worthwhile to further explore possible indirect relationships in subsequent analyses. Regression analysis showed that attitude (b ¼ .21, p < .001) and subjective norm (b ¼ .31, p < .001), together with perceived benefits (b ¼ .13, p < .05) and response costs (b ¼
.17, p < .05), accounted for 41% of the variance in behavioral intentions. Controlling for the TPB variables, HBM variables accounted for another 3% of the variance in behavioral intentions. Among the demographic and control variables, only past behavior was consistently related to behavioral intentions in all three models. Respondents who received the vaccine during the past flu season indicated a greater likelihood to get the vaccine this year, which suggests behavioral consistency among the respondents. To further explore the relationships proposed in Figure 1, a path model was specified to the data (Figure 2). After nonsignificant paths were removed, the model fit was satisfactory: v2(12) ¼ 14.68, p ¼ .26, v2=df ¼ 1.22, RMSEA ¼.025, GFI ¼.94, CFI ¼ 1.00. Path analysis showed that perceived barriers were significantly related to self-efficacy (b ¼
.53,

8

Z. J. Yang

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Fig. 2. Path analysis results from the integrated model. Standard coefficients are reported. Fit indices: v2(12) ¼ 14.68, p ¼ .26, v2=df ¼ 1.22, RMSEA ¼.025, GFI ¼.94, CFI ¼ 1.00.  p < .05.  p < .01.  p < .001.

p < .001), which was not surprising because these two concepts are intricately related (Janz & Becker, 1984). The path model also showed that interpersonal discussion was significantly related to subjective norm (b ¼ .22, p < .01). Mediation analysis indicated that subjective norm mediated the relationship between interpersonal discussion and behavioral intentions (B ¼ .15, p < .01 ! B’ ¼ .07, ns, total indirect effect: .09, 95% confidence interval: [.05, .14]). Thus, cues to action influenced behavioral intentions through subjective norm, supporting Hypothesis 7. Last, to answer the research question, path analysis showed that perceived benefits (b ¼ .26, p < .001) and response costs (b ¼
.11, p < .05) were significantly related to attitude. In contrast, perceived susceptibility and perceived severity were not. Moderation analysis showed that attitude moderated the relationship between response costs and behavioral intention (B ¼ .05, p < .05), as well as the relationship between perceived benefits and behavioral intention (B ¼
.05, p < .01). In both cases, the coefficient for response costs and perceived benefits were nonsignificant for models where attitude was adjusted to be one standard deviation above the mean. In contrast, response costs (b ¼
.28, p < 001) and perceived benefits (b ¼ .21, p < 001) mattered more in predicting behavioral intention when attitude was adjusted to be one standard deviation below the mean. That is, response costs and perceived benefits were more likely to influence respondents’ intention to get the vaccine when they did not hold much favorable attitude toward getting the vaccine. It is interesting that subjective norm also moderated the relationship between response costs and behavioral intentions (Figure 3). Response costs mattered more in predicting behavior intention when subjective norm was adjusted to be one standard deviation below the mean (b ¼
.29, p < 001). However, the coefficient for response costs was nonsignificant for the model where subjective norm was adjusted to be one standard deviation above the mean. The final path model, excluding the demographic controls, accounted for 29% of the variance in subjective norm, 28% of the variance in self-efficacy, 26% of the variance in attitude, and 38% of the variance in behavioral intentions.

Fig. 3. Interaction between subjective norm and perceived response costs.

Discussion Together, these results showed that outcome expectations, general attitude toward the vaccine, and subjective norm were the most important determinants of behavioral intentions in regard to influenza vaccination. In addition, interpersonal discussion, serving as a cue to action, also led to a stronger intention to get the vaccine. Even though the integrated model did not offer much additional predictive power, it helped to streamline relationships among established concepts in accounting for behavioral intentions. As shown in Figure 2, key variables from the HBM primarily functioned through attitude, subjective norm, and self-efficacy, exerting indirect effects on behavioral intentions. Self-efficacy also influenced behavioral intentions through attitude and subjective norm, which offered additional support for previous research (Yzer, 2007). Consistent with past research (Bish et al., 2000; McClenahan et al., 2007), the HBM accounted for less variance in behavioral intentions as compared with the TPB. However, significant predictors from both models suggested a coherent story. That is, outcome expectations weighed a lot more than perceived threat in influencing young adults’ intentions to get the vaccine. In particular, beliefs about the effectiveness of the vaccine in safeguarding oneself from the H1N1 flu and beliefs about potential side effects of and adverse reactions related to the vaccine were key factors. Results from the moderation analyses further clarified this influence, showing that perceived benefits and response costs were particularly influential among those with limited favorable attitude toward the vaccine. Similarly, response costs were more likely to deter young adults from getting the vaccine when they sensed limited social pressure from their family and friends. Together, these findings add to the expanding literature that argues for more research on response costs, which involve concerns about potential negative outcomes from performing a health behavior (Cameron et al., 2009; Champion & Skinner, 2002). When the data were collected, the Centers for Disease Control and Prevention had declared an end to the 2009

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H1N1 Vaccine Uptake H1N1 pandemic, which might explain why perceived threat was not as strong a predictor of behavioral intentions. Nonetheless, it was alarming that even though the H1N1 pandemic resulted in over 12,000 deaths in the United States (Centers for Disease Control and Prevention, 2010), young adults in the sample did not see it as a severe health threat that warranted their attention. Bish and colleagues (2000) argued that the poorer prediction of intention afforded by the HBM was attributable to the lack of correspondence between measures of the HBM variables and the measure of intention. In this study, perceived threat and cues to action were associated with the H1N1 flu, but perceived benefits and barriers, self-efficacy, and behavioral intentions were all associated with the flu vaccine. This mismatch also suggested some degree of inconsistency in measures. That being said, most models with both a threat appraisal component and an efficacy appraisal component, such as the HBM and the Extended Parallel Process Model (Witte, 1992), are often subjected to this issue because perceived threat is focused on the health threat, while perceived outcomes and behavioral intentions are usually oriented around the solutions. Thus, the difficulty with operationalization might also explain why perceived threat was not a significant predictor in the integrated model. The most recent Kaiser Family Foundation study of more than 2,000 young people found that even though their overall media consumption has increased over the past ten years, the amount of time they spent using traditional media has decreased (Rideout, Foehr, & Roberts, 2010). The majority of the media sources that constituted the media attention index were traditional media, which might explain why attention to H1N1 news from these media was not an effective external stimulus for behavioral intentions. In contrast, interpersonal discussion about the H1N1 flu worked through subjective norm to trigger young adults’ interest in the vaccine. The classic mediation effect here explains why interpersonal discussion was no longer significantly related to behavioral intention after subjective norm was entered into the model. Perhaps because of the homogeneous nature of the respondents in the sample, none of the demographic variables had a significant effect on behavioral intentions. However, the consistent, positive relationship between past behavior and behavioral intentions suggests that behavioral initiation is a critical step for health promotions related to vaccination. Once high-risk populations are convinced to adopt the behavior, there is a greater chance that they will continue to engage in the behavior. In addition, it was not surprising that even though the respondents scored relatively high on the knowledge test, their existing knowledge did not lead to greater intentions to get the vaccine. Past research has shown many times that knowledge does not usually lead to behavioral change (see, for example, Baranowski, Cullen, Nicklas, Thompson, & Baranowski, 2012; Clark & Zimmerman, 1990). Findings from this study also suggest that the TPB and the HBM are essentially pursuing the same fundamental constructs that account for behavioral formation. This is

9 hardly surprising given that both models are based on expectancy value theory (Bish et al., 2000). As shown in the path model, outcome expectations could be viewed as an antecedent to attitude because the TPB formulation of attitude is based on behavioral beliefs, which are essentially beliefs about the likelihood that an action might lead to favorable or unfavorable outcomes. However, response costs, which extend perceived barriers to include concerns with drawbacks that may become noticeable only after the behavior was performed, seem to suggest additional dimensions of attitude that need to be included in future research. For example, side effects and adverse reactions are important potential outcomes that need to be included to gauge individuals’ attitude toward the vaccine. The strong, consistent effect of subjective norm suggests that an assessment of social influence is a necessary component of social cognitive models that predict behavioral intentions. In addition, subjective norm seems an important way station through which external stimuli, such as interpersonal discussion, engenders behavioral intention. Informing theory development, the indirect effects identified through the path analysis and additional mediation and moderation tests suggest that most of the HBM variables might be indirectly related to behavior because their relationship to behavioral intentions was either mediated or moderated by TPB variables. Thus, future research should continue to explore integrated models based on the TPB and the HBM not as simple additive models, but as more complex causal models and examine interactions among the variables. Findings from this study also have important practical implications. First of all, formative research that reveals salient behavioral beliefs in the target population is important. Once these behavioral beliefs are identified, health communication could tailor the message to foster more favorable attitude toward the vaccine. From this study, given how attitude moderated the relationship between perceived outcome (perceived benefits and response costs) and behavioral intentions, highlighting the effectiveness of the vaccine and refuting misperceptions surrounding the vaccine seem essential. Second, given the strong effect of subjective norm, health communication campaigns could depict getting the vaccine as a socially responsible behavior and target important referent groups to reach high-risk populations. Even though this study only assessed young adults’ general discussion with others about the H1N1 flu, the fact that this discussion influenced their intention to get the vaccine through subjective norm highlighted the importance of encouraging more conversations about this issue among this population group to make sure this issue is on their attention span. In addition, on the basis of the moderation effect that subjective norm exhibited on the relationship between response costs and behavioral intentions, especially how respondents with lower subjective norm were more likely influenced by perceived response costs, it seems all the more important to establish a provaccine social environment among these young adults. Overall, risk and health communication about flu vaccination targeted at young adults should focus on their existing beliefs related to getting the

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10 vaccine, counter misperceptions regarding the safety and efficacy of the vaccine, and promote vaccination as a health behavior that not only protects people who receive the vaccine themselves, but also those around them. While discussing theoretical and practical contributions of this study, it is also important to point out limitations. Besides the obvious issue with limited generalizability because of the convenience sample, measurement strategy for key variables needs improvement. For instance, even though subjective norm had a consistently strong relationship with behavioral intentions, both descriptive norm and injunctive norm should be included to assess this concept. Given that the behavioral intention measures did not used the word intend, one could argue that they might have assessed other notions such as probability of action or willingness. Perhaps more important, the behavioral intention measures did not specify that the vaccine for the 2010–2011 flu season incorporated vaccine for the H1N1 strain along with other strains. Although this wording emphasized H1N1 vaccine, more precise wording here could avoid possible confusions among the respondents. Relatively lower reliability for several scales, such as perceived susceptibility and severity, might have attenuated the relationship they had with behavioral intentions. Using additional items to assess these variables could enhance the reliability of the scales. In contrast, the two items measuring perceived benefits appeared together in the questionnaire, which probably led to the almost perfect correlation between these two items. Most important, the college student population has a greater tendency for risk-taking (Irwin & Millstein, 1986; Jack, 1986) and subject to peer influence (Maxwell, 2002). These unique traits might have amplified the effect of subjective norm on behavioral intentions, which warrants more caution from the readers when interpreting the results. In sum, the TPB, once again, accounted for a greater amount of variance in behavioral intentions and stood out as the more economic and robust model. The integrated model clearly shows that most of the HBM variables influenced behavioral intentions indirectly through the TPB variables. This finding suggests that future research should continue to explore theoretical integration, perhaps with enhanced measures, to see whether a health specific model (i.e., the HBM) can inform the measurement strategy of a more general model (i.e., the TPB) to improve its ability to predict intentions and behavior in a health context.

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