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“The only thing necessary for these diseases to the triumph is for good people and governments to do nothing.”



Single-Motive and Multi-Motive Processing of a Threat Appeal:  Promoting the Preventative Health Behavior of Influenza Vaccinations


Jason W. Anderson

University of Wisconsin-Madison

Author Note

            Correspondence may be addressed to Jason Anderson, Department of Communication Arts, University of Wisconsin-Madison, 821 University Avenue, Madison, Wisconsin, 53706, (608) 226-0431, or JWANDER1@STUDENTS.WISC.EDU


This study considers the impact of behavioral commitment on the cognitive and affective processing of a persuasive message advocating influenza vaccination behaviors, and the resulting impact on the integration of information into attitudes, behavioral intention, and behavior. It was argued that prior behavior commitments would lead some processors to engage in concurrent validity-seeking and defensive processing. This multi-motive processing may explain the limited effectiveness of persuasive messages, specifically threat appeals. A non-random sample (n=178) of university students was collected. Results with regard to attitude ambivalence (p<.05), and other indicators (i.e., negatively valenced cognitions, biased cognitive processing, and affective arousal) were consistent with predicted differences between single- and multi-motive processors.  Implications for theoretical and applied research are discussed.

Keywords: multi-motive processing, defensive processing, validity processing, persuasion, and influenza.

Single-Motive and Multi-Motive Processing of a Threat Appeal:  Promoting the Preventative Health Behavior of Influenza Vaccinations

            Many attempts at persuading individuals to engage in healthy behaviors are based on the assumption that making one aware of a life-threatening event should lead to the adoption of behaviors that alleviate the threat. These persuasive messages, known as threat appeals (Leventhal, 1971; Rogers, 1975), have been shown to effective in garnering behavioral change in a variety of contexts, including health messages (Boster & Mongeau, 1984; Mongeau, 1998). In his meta-analysis, Mongeau (1998) reported correlations of .19 between level of threat and attitude change and .12 between level of threat and behavioral change. These analyses support threat appeals as a compelling message form and means of garnering attitude and behavior change. As such, threat appeals are an important tool at the disposal of health message designers. On the other hand, the correlations observed in the meta-analyses are still far from universally effective even though goals for self-preservation are surely the most fundamental of all human motivations.

            In order to clarify the limited efficacy of threat appeals, this study examines individuals exposed to a persuasive message—a threat appeal—concerning influenza and the need for influenza vaccinations. The findings detail the effects of motivations for self-preservation, and in some instances, the effects of countervailing motivations that serve to constrain the effectiveness of threat appeals. Moreover, the study provides an insight into when these competing motives are present and how they affect the persuasion process.


            Primarily spread through the air, influenza, commonly known as "the flu," poses a threat to everyone in the proximity of the carrier. On average, about 10% of all Americans contract the virus each flu season ( The only cure for the virus is to let it run its course, a process that may take two weeks ( As a result, most of these victims only incur the miseries that accompany the infections (i.e., fever, muscle aches, fatigue, chills, sweating, etc.), as well as possible work, school, and leisure-related debts. These hardships are certainly unwanted, but many who contract the disease lose much more. During an average year, approximately 20,000 Americans die from influenza and many more are hospitalized (; American Medical Association, 1999). When influenza leads to influenza related pneumonia, the flu becomes deadly. In fact, influenza and influenza-related pneumonia are the sixth most common cause of death in the United States ( Furthermore, influenza can exacerbate existing medical conditions (i.e., asthma, heart disease, emphysema, AIDS, diabetes) and lead to additional medical complications (; University of Wisconsin Hospitals and Clinics, 1999). Some strains of influenza have more dire consequences than others. The Spanish Flu claimed the lives of 500,000 Americans and 20 million people worldwide from 1918-1919 ( Researchers note that it is a question of "when," not "if" another pandemic occurs, and that the world is far overdue for another flu pandemic ( While no cure for the flu exists, vaccinations, which decrease the chances of contracting the flu from 20% to 2% and shorten recovery times, are a viable tool for combating the flu (,, World Health Organization, 1999). Despite this information, not all Americans receive annual vaccinations. Therefore, this study seeks to understand and garner flu vaccination behaviors.

Threat Appeals as Persuasive Messages and Motivations To Avoid Influenza Vaccinations

            Extant research concerning influenza vaccination behaviors appears to focus on identifying predictors of vaccination behaviors (Chapman & Coups, 1999) and the role of information-based interventions in increasing vaccination rates (Herman, Speroff, & Cebul, 1994; Ohmit, Furumoto, Monto, & Fasano, 1995) particularly among at risk groups (i.e., the elderly). The research in this area does not appear to take advantage of a body of literature concerning the design of persuasive messages, including threat appeals. However, existing research may still inform the design of persuasive messages in this context.

Herman et al.’s (1994) information-based intervention found that interventions that illustrated the dangers of failing to get a flu shot for the elderly were somewhat successful in increasing rates of vaccination, but the intervention was only successful in achieving a 36% vaccination rate compared to the 23% reported by the control group. The findings illustrate the need for persuasive messages to go beyond not only identifying threats to one’s health, but also presenting a compelling argument for addressing the health threat.

Threat appeals are one form of persuasive messages that have been shown to be effective (Mongeau, 1998), and in this study, a threat appeal serves as a vehicle for conveying a persuasive message. By their nature, threat appeals have implications for one’s self-preservation goals, but threat appeal exposure does not lead to universally practiced health behaviors (Mongeau, 1998). Therein lies the focus of the present study.

Influenza infections can be deadly (, and infections can have costly social and economic effects; however, despite the undeniably high costs and likelihood of infection, individuals still fail to perform health behaviors that can help one avoid these costs (i.e., receive an influenza vaccination). Despite salient threats to one’s goals for self-preservation, across various health issues the research suggests that threat appeals are not as effective in changing behaviors as it seems they should be (Mongeau, 1998). Within the context of influenza vaccination behaviors, this study searches for explanations to these findings by identifying other salient motivations that might conflict with goals for self-preservation.

Two Motives for Message Processing

            Functional theorists hold that attitudes may serve multiple functions. Smith, Bruner, and White (1956), as well as Katz (1960) agree that one such function is to allow an individual to contemplate an attitude object (e.g., get a flu shot) in order to discern the potential harms or benefits the attitude object holds for the individual. Herek’s (1986) neofunctional approach builds from this contention. Herek contends that message targets process messages in an effort to accurately evaluate the evidence and claims forwarded by a persuasive message. This concern for accuracy is also reflected in the development of the Heuristic Systematic Model (HSM) of message processing (Chaiken, Eagly, & Liberman, 1989). According to the HSM, message targets may process messages through a validity seeking orientation in an effort to maintain, reformulate, or develop “accurate” attitudes (Chaiken et al., 1989). Therefore, one motive for message processing is the quest to hold accurate attitudes.

            As suggested by functional theorists and HSM research, individuals may hold other motives for evaluating a persuasive message. Thus, for some individuals, a persuasive message (i.e., a threat appeal) may inherently attack a held attitude or behavior position. In such cases, one salient motive may be a defense motive. Under such a motive, individuals engage in defensive processing in order to protect an attitude or behavior in which they have a vested interest or prior commitment (Chaiken et al., 1989; Giner-Sorolla, & Chaiken, 1997; Herek, 1986). When individuals have already formed a position on an issue, persuasive messages that attack the attitude or behavior may motivate them to engage in defensive processing in an effort to avoid any requisite changes in one’s self-image made necessary by a successful attack.

            Validity seeking and defense motives should result in substantively different processing outcomes. Given a compelling persuasive message, individuals processing through a validity seeking orientation should show (a) a large number of cognitive responses, (b) a low number of counterarguments, (c) strong agreement with the advocated position, (d) a strong attitude-intention correlation, (e) a strong intention-behavior correlation, and (f) low attitude ambivalence. Relative to validity seeking processors, defensive processors should show (a) roughly the same number of cognitive responses, (b) a high (versus lower) number of counterarguments, (c) weaker (versus stronger) agreement with the advocated position, (d) an equally strong attitude-intention correlation, (e) an equally strong intention-behavior correlation, and (f) roughly the same low level of attitude ambivalence. In order to make sense of these motivations, it is important to consider the potential conflict between them.

Single- Versus Multi-Motive Processing

            It has been argued that individuals exposed to a persuasive message may orient toward both validity seeking and defense motives with each motive yielding a unique impact on message processing. However, it is also important to note the tension between these potentially salient motives and their processing demands. For influenza vaccinations and other health behaviors, the rewards of such processing behaviors may conflict with potential rewards for performing an alternative behavior. For example, condom use affects one’s likelihood of contracting AIDS or an STD, yet a large portion of the non-monogamous population fails to use condoms for a variety of reasons (i.e., loss of sensation, lack of spontaneity in the sexual act, etc.). Similarly, flu vaccinations may allow one to greatly avoid the effects of an influenza outbreak. At the same time, getting an influenza vaccination also has costs such as pain and resource debts (e.g., loss of leisure time, money for the vaccination, etc.). Furthermore, for those who have never received a vaccination, they may be forced to re-evaluate their self-concept (e.g., I was wrong about the need for vaccinations, I am not invincible, etc.).

            Clearly there are reasons for persuasive health messages to orient individuals to engage in validity seeking in an effort to meet goals for self-preservation; however, for some individuals, there appear to be reasons to orient toward a defense motive as well. Cognitive consistency theories provide a rationale for individuals orienting toward defensive processing in the face of a persuasive health message (Festinger, 1957). These theories argue that individuals desire to hold attitudes and perform behaviors that are consistent with beliefs and behaviors performed in the past. Thus, defensive processing allows one to avoid the tension or dissonance that arises from performing behaviors or espousing attitudes that run counter to prior behaviors and beliefs.

In traditional laboratory research, investigators tend to manipulate conditions or assume that participants engage persuasive messages under a validity seeking orientation. But, as cognitive consistency theories suggest, the notion of single-motive validity seeking processing seems unwarranted. In applied contexts, individuals exposed to a message bring with them a history of issue-relevant behaviors and attitudes. Therefore, the effectiveness of persuasive messages may be affected by motivations that run counter to the message advocacy. 

Given a compelling, persuasive message advocating influenza vaccination behavior, one should expect all message recipients to recognize threats to self-preservation goals and orient toward validity seeking processing. And, for those who have a history of receiving flu vaccinations and positive attitudes toward vaccinations, the assumption that one engages in validity seeking, single-motive processing appears warranted. But, not all individuals have a history of practicing flu vaccination behaviors. In the context of a persuasive message advocating influenza vaccinations, messages might implicate not only a self-preservation goal, but also a held attitude and behavior toward vaccinations. As a result, those with a vested interest or behavioral commitment to vaccination avoidance may realize conflict as they attempt to orientate toward defense and validity seeking processing demands. If we can also assume that all individuals seek self-preservation, then some message targets must engage in multi-motive processing (i.e., concurrent defense and validity seeking motivations) based on one’s prior attitudes and beliefs toward the issue at hand. It is argued that, unlike single-motive processors, multi-motive processors realize conflict while engaging in message processing. The following discussion focuses on how this conflict manifests itself in the persuasion process.

Manifestations of Single- and Multi-Motive Processing

            Cognitive response. When individuals are motivated to process messages through a desire to hold valid attitudes, there is an increase in the depth of message processing (e.g., Chaiken et al., 1989). Furthermore, defense motives have been shown to enhance one’s depth of processing (Giner-Sorolla & Chaiken, 1997). Thus, multi-motive processors might be expected to exhibit greater depth of processing than single-motive processors given the joint effects of the motives (i.e., concurrent validity seeking and defense motive processing).[i] With regard to the issue of influenza vaccinations, when exposed to a message favoring vaccinations, we suggest that:

            H1: Non-vaccinators (i.e., behavioral commitment to avoid vaccinations) will report more total cognitions than prior vaccinators (i.e., behavioral commitment to get vaccinations).

            Beyond the depth of processing, research suggests that single- and multi-motive processors may differ in the valence of their reported cognitions. Research suggests that cognitive reports that are favorable toward the message (i.e., supporting arguments) result in agreement with a message’s advocacy (Giner-Sorolla & Chaiken, 1997). On the other hand, cognitive reports that are unfavorable (i.e., counter-arguments) result from a disagreement with the message’s advocacy. Given their prior behaviors toward vaccinations and exposure to a compelling persuasive message, we suggest that:

            H2: Non-vaccinators will produce a more negative dominant cognitive response (i.e., total positive cognitions minus total negative cognitions) than prior vaccinators.

            Biases in message processing. This study employs a threat appeal as a form of a persuasive message. Threat appeals are composed of two key elements (Dillard, 1994; Mongeau, 1998). First, there is a threat component. The threat component orients the message recipient to the negative outcomes associated with failing to adopt the recommendations of the message. The threat component contains two central elements: (a) severity and (b) likelihood (Dillard, 1994; Witte, 1993, 1994). The severity element emphasizes the nature of the threatening event and the costs of one’s present behavior, while the likelihood element expresses the probability that the event will occur and the consequences of the event for the individual.

            Second, the action component details the recommendations that must be carried out to avert the threatening event. Again, the action component contains two key elements: (a) response efficacy and (b) self-efficacy. Response efficacy addresses the likelihood that carrying-out the recommendations advocated by the message will avert the threatening event, and self-efficacy emphasizes one’s ability to carry-out the recommendations (Dillard, 1994; Witte, 1993).


The nature of each of these key elements of the threat appeal is determined by a subjective judgment made by each message recipient. It is proposed behavioral commitments lead individuals to engage in defensive processing. As such, one’s perceptions of the severity, likelihood, response and self efficacy may be biased by behavioral commitments. Therefore, we propose that:

            H3: Compared to prior vaccinators, non-vaccinators will underestimate one or more of the following: (a) the perceived likelihood of contracting influenza, (b) the perceived severity of contracting influenza, (c) the response-efficacy of recommended action (i.e., obtain an influenza vaccination), and (d) their self-efficacy regarding the execution of the recommended action.

            Emotional responses to the persuasive message. While cognitive responses to persuasive messages are of interest, it is also important to consider one’s emotional responses. Emotions result from one’s appraisal of the interaction between one’s goals and environment (Frijda, 1986; Lazarus, 1991). Specifically, when one appraises a threat to self-preservation goals based on salient features of his or her environment, fear is aroused (Lazarus, 1991). Threat appeals are specifically designed to emphasize such threats regardless of one’s behavioral commitments. Therefore, we propose that validity seeking processors—single- and multi-motive processors alike—will experience an arousal of fear. Specifically, we predict:

            H4: Following exposure to the threat component of the message, both prior vaccinators and non-vaccinators will experience an increase in fear.

Similar to the appraisal process for fear, anger is aroused when individuals recognize a presence in their environment that threatens one’s goal for autonomy (Lazarus, 1991; Dillard, Kinney, & Cruz, 1996). For those individuals who have a vested interest or behavioral commitment to avoid flu vaccinations, a threat appeal that attacks these attitude and behavioral positions should be viewed as an attempt to limit one’s autonomy (Brehm & Brehm, 1981; Lazarus, 1991; Dillard, Kinney, & Cruz, 1996).  Due to this nature of threat appeals, the persuasive message should lead these individuals to not only experience fear following measure exposure, but also anger. Therefore, we propose:

            H5: Non-vaccinators will report a greater experienced level of anger than prior vaccinators.

Researchers have also noted that threat appeals arouse emotions other than fear and anger (Dillard, Plotnick, Godbold, Friemuth, & Edgar, 1996). Specifically, we were interested in differences in the arousal of negative emotions among single- and multi-motive processors. Differences in the arousal of sadness and guilt might explain differences obtained between these groups of processors. Therefore, we asked:

RQ1: What is the relationship between the experience of sadness and guilt following exposure to the threat component of the message for prior vaccinators and non-vaccinators.

While it is important to consider the effects of message processing, it is also important to consider the resultant impact of multi-motive processing on the integration of this information into attitude, behavioral intention, and behavioral outcomes. Below, these effects are addressed.

            Information integration. According to combinatorial theories of attitudes, attitude change occurs by integrating information with pre-existing attitudes (Anderson, 1971; Fishbein & Azjen, 1975). When exposed to messages containing information salient to one’s attitudes, individuals weight the salient information and then arrive at an attitude polarity judgment represented somewhere on a continuum ranging from good to bad at the extremes (Fishbein & Azjen, 1975). Among single-motive processors, we expect that integration is smooth and efficient. However, among multi-motive processors, individuals are oriented toward two competing and conflicting motives, validity seeking and defense. This conflict arises out of the desire to defend an attitude through defensive processing, while concurrently addressing one’s desire for self-preservation through rational, unbiased validity processing. Therefore, we propose:

            H6: Non-vaccinators will produce a correlation between dominant cognitive response and attitude that is lower in magnitude than prior vaccinators.

            Attitude ambivalence. The conflict between these two competing motives should also be realized in terms of the certainty with which attitudes are held. While single-motive processors are free to focus on evaluating the validity of the message’s advocacy, multi-motive processors not only seek to evaluate the validity of the message, but also to defend an attitude position to which they are behaviorally committed (i.e., avoid influenza vaccinations). Due to the concurrent motivations, multi-motive processors exhibit less certainty in their attitude position, or in other words, greater attitude ambivalence (Gross, Holtz, & Miller, 1995). Thus, we propose:

            H7: Non-vaccinators will report higher levels of attitude ambivalence than prior vaccinators.

            Bridging the gap from attitudes to behaviors. Fishbein and Azjen’s (1975) theory of reasoned action, as well as Azjen’s (1991) theory of planned behavior, illustrate the role of attitude polarity judgments as a cause of behavioral intention. The linkages between attitude judgments and behavioral intentions have also been established in meta-analyses of this relation (Kim & Hunter, 1993a, 1993b). Unlike single-motive processors, multi-motive processors must make sense of information gleaned from conflicted message processing. We propose that the conflict impacts the attitude-behavioral intention relationship such that:

            H8: Non-vaccinators will produce a correlation between attitude and behavioral intention that is lower in magnitude than prior vaccinators.

Similarly, Fishbein and Azjen’s (1975) theory of reasoned action has shown that behavioral intention is the best predictor of behavior—a correlation of about .6. Again, for multi-motive processors, the conflict that was generated at the information integration level of processing has implications for not only the attitude-behavioral intention relation, but also carry-over effects on the behavioral intention-behavior relation. Specifically, we propose that:

H9: Non-vaccinators will produce a correlation between behavioral intention and behavior that is lower in magnitude than prior vaccinators.

These hypotheses allow us to examine the persuasion process from message exposure through the enactment of subsequent behaviors. This examination provides an insight into both affective and cognitive processes that take place among single- and multi-motive processors.



            The data for this study were obtained as part of a larger study conducted at a large Mid-western university.[ii] The initial sample for this study consisted of 181 participants. In order to remove any participants who were physically unable to receive a flu shot, participants were asked to report whether or not they were pregnant or allergic to eggs. This resulted in the removal of two participants from the analyses. An additional participant had to be excluded because she failed to report her vaccination history. Therefore, the sample size for the analyses below is 178 including 53 males and 125 females with a mean age of 20.21 (range=17-34) (See Table 1). Participants were recruited from undergraduate courses across various departments at the university, and they received a small amount of extra credit for completing the study.


Prior to beginning the experiment, two threat appeals were developed with the intention of creating differential levels of threat. Given the data subset of interest, the focus here is on the development of the high threat message during the pre-test phase. We sought to construct a message that was persuasive, compelling, and as realistic as possible. To that end, the message was constructed to include each of the necessary elements of a threat appeal (Dillard, 1994; Witte, 1993). Beyond constructing a threat and action component, we also sought to develop compelling arguments within each component. These arguments were supported by evidence from expert sources (e.g., Center for Disease Control, National Institute of Infectious Diseases, etc.) and were presented in both a narrative and a statistical form in an effort to produce a compelling message (See Table 2). Morley (1987) identified three indicators of compelling messages: (a) novelty, (b) believability, and (c) importance. The messages were pre-tested along each of these dimensions to determine whether or not the message was compelling. Three close-ended items on 7-point semantic differential scales were utilized to assess each argument. Pre-tests suggested that the respondents (n=27) perceived the message to be compelling. On average, the respondents found the arguments contained in the message to be novel (X=4.35, range=1.53-6.30, sd=1.17), believable (X=5.8, range=4.25-7.00, sd=.85), and important (X=5.44, range=2.1-7.00, sd=6.1). With mean scores consistently above the mid-point of scales, the study proceeds under the assumption that the message can be characterized as persuasive and compelling.

Procedures and Measures

The onset of data collection for this study coincided with that of a University Health Service’s (UHS) program that provided free influenza vaccinations to all interested university students, faculty, and staff members. Data collection for this phase of the study continued until one week before the end of the program.

Upon arrival at the laboratory, participants were randomly assigned to a high or low threat messages condition (with the high threat condition being the focus of the analyses below), as the experimenter distributed a questionnaire, message booklet, and consent form to each participant. Next, the experimenter read aloud the cover story explaining the experimenters were interested in refining health messages to be used in an actual web-based health campaign. Then, participants were directed to sign a consent form if they still wished to participate before continuing with the study. This phase of the study took about half an hour to complete.

The second phase of the study consisted of two parts and began immediately after the conclusion of UHS’ flu vaccination program. During phase one of the study, we had asked participants to give us consent to review their flu vaccination records on file at UHS. Their consent allowed us to discern whether or not they had participated in the UHS program. The other aspect of the second phase of the program concerned a follow up questionnaire that was administered online via a university website. The questionnaire contained self-report behavioral measures that are discussed below. Collection procedures of phase two are discussed below.

            Measurement. In most persuasion research, participants are presented with a complete message and asked to provide their evaluations of it. However, threat appeals are generally constructed such that there are two clear components to the message, that is, the threat or problem component followed by the action or solution component. We had an interest in evaluating each of these components, while connecting our research to previous work. Thus, two measurement conditions were created. First, the interrupted measurement condition was created by asking participants to read the threat component. After reading the threat, the participants then provided closed- and open-ended data regarding their reactions to it. At this point in the interrupted condition, respondents were asked to return to the message booklet, read the action component and provide data relevant to that section of the message. In the second condition, labeled non-interrupted, participants read through the entire message then provided data on each section individually. The experimental instructions for the two conditions differed. Therefore, each session was randomly designated as either an interrupted or a non-interrupted.            Commitment to Flu Vaccination Behavior. In order to discern participants’ prior influenza vaccination behaviors, a single-item measure was included in the questionnaire. Specifically, participants were asked “How many times have you received an influenza vaccination?” Response options included were as follows: (a) never, (b) once or twice, (c) three-four times, and (d) five or more.  This item was then used to create a dichotomous flu vaccination behavior variable that serves as the independent variable in the analyses that follow. Participants reporting no history of flu vaccinations (n=88) were labeled non-vaccinators, while those reporting at least one prior flu vaccination (n=90) were labeled vaccinators.

            Open-Ended Responses. Following exposure to the message, respondents were asked to list any thoughts and feelings they had while reading the message.  These reports were then evaluated by three trained coders.[iii] In the first pass through the data, the coders unitized the responses.  Participants were instructed to report one thought or feeling per response box, but it was common to find reports such as "I want to get [a flu shot] right away, because I am afraid I will forget." These reports were best considered as two separate thoughts. Therefore, 20 questionnaires were randomly selected. Working independently, each coder unitized the data into psychological thought units (roughly, independent clauses). Then, the percentage of agreement for the first pass through the data was computed as twice the number of agreements divided by Coder 1's total units plus Coder 2's total units for each pair of coders yielding agreement of 86%, 89%, and 84%. At this point, sources of error were discussed and additional training was provided. When the above procedures were repeated, a second pass through the data yielded acceptable agreement levels of 98%, 97%, and 97% between the pairs of coders. Given these levels of agreement, each coder proceeded to unitize a third of the remaining questionnaires yielding a total of 5,515 open-ended responses.

            A second round of coding was conducted to identify open-ended reports of affective experiences that were redundant with close-ended reports of the same. Utilizing a list of feeling terms compiled by Shaver, Schwartz, Kirson, and O'Connor (1987), coders classified a unit as affective whenever these words appeared within a participant's report. Coding of a random sample of 20 questionnaires yielded acceptable reliability (κ= .96, .93, and .94), thus further training was not warranted. After coding, the affective units were removed, and a data set of 5,099 cognitive responses was obtained.[iv]

            Finally, coders classified the remaining responses as either (a) supportive comments (i.e., responses expressing agreement with the message), (b) negative comments (i.e., responses expressing disagreement with the message), and (c) neutral comments (i.e., response that merely reiterates the message). Although initial coding attempts were not acceptably reliable (κ=.76, .78, and .84), reliability was established after additional training that addressed problem areas (κ=.87, .85, and .95). At this point, the remaining data set was coded with 1,405 supportive, 607 negative, and 3,087 neutral comments identified.

            The open-ended data were utilized to create two measures:  total cognitive response and dominant cognitive response. Total cognitive response was created by summing all negative, supportive and neutral comments produced by each participant. Dominant cognitive response was created by subtracting all the negative reports produced by a participant from all the positive reports produced. Essentially, dominant cognitive response measures that approach or are less than zero indicate open-ended responses that are increasingly counter-argumentative in nature.

            Severity.  For the severity variable, three close-ended items on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) were included (e.g., “From what I know about the flu, I think that it is very serious”). Alpha for the three-item measure was .63.[v] Attempts to improve the reliability were unsuccessful. 

            Probability.  We also assessed the perceived probability of contracting influenza.  Three close-ended items on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) were included (e.g., "There is a real possibility that I could contract influenza this academic year"). Cronbach's alpha for the measure was .84.

            Response efficacy. Reponse efficacy was collected using three close-ended items with a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) (i.e., "Getting a vaccination is a sure-fire way to reduce the possibility of contracting the flu"). Reliability analyses revealed a low reliability level (α=.52) with the three-item measure, but when one of the items was dropped, a reliability level of .72 was achieved and used in subsequent analyses.

            Self-Efficacy.  To assess self-efficacy, we utilized three close-ended items with a 5-point scale ranging from strongly disagree (1) to strongly agree (5) (i.e., “If I resolved to get a flu shot, I am certain that I would be able to do it"). Again, preliminary analyses yielded an unacceptable reliability level (α=.50) for the three-item measure, but when one item was dropped the alpha improved to an acceptable .81. This two-item measure was used for the following analyses.

            Emotions.  Emotion measures of (a) fear, (b) anger, (c) sadness, (d) guilt, and (e) happiness were collected using items that had been demonstrated to be reliable (Dillard & Peck, 1998). The scales for each of the emotion items ranged from none of the emotion experienced (0) to a great deal of the emotion experienced (4). Three items were collected for measures of fear (i.e., fearful, afraid, and scared), anger (i.e., irritated, angry, and aggravated), and sadness (i.e., sad, dreary, and blue), while the guilt measure consisted of two items (i.e., guilty and ashamed). Respondents were asked to respond to the emotion items before reading the threat appeal (i.e., baseline) and after reading the threat appeal (i.e., post-threat). Analyses revealed that baseline emotion measures for fear (α=.82), anger (.84), sadness (.67), and guilt (.72), as well as post-threat measures (α=.94, .90, .74, and .70 respectively) were reliable. Using these measures, we developed an emotion change score for fear, anger, sadness, and guilt by subtracting the baseline measure of each emotion from the post-threat measure.          

            Attitude. Participants were exposed to a message that urged them to take part in the UHS’ free influenza vaccination program. After reading the entire message, they were asked “The idea of me getting a flu vaccination at University Health Services is…” followed by a series of seven 7-point semantic differential scales (i.e., good/bad, wise/foolish, positive/negative, favorable/unfavorable, undesirable/desirable, necessary/unnecessary, and not essential/essential). The attitude toward the message advocacy measure was reliable (α=.92).

            Behavioral intention. Following exposure to the entire message, respondents were asked to make a probability judgment concerning their likelihood of getting a flu vaccination. We asked “All things considered, how likely is it that you will get a flu vaccination from University Health Services during the 1999-2000 school year?" Participants reported their judgment on a scale ranging for (0) “Certain that I will not” to (100) “Certain that I will.”

            Attitude ambivalence.  This variable was constructed from three close-ended items using five-point Likert-type scales ranging from (1) strongly disagree to (5) strongly agree (e.g., “When thinking about the flu, my mind is split on whether I should or should not get a vaccination").  Cronbach’s alpha for attitude ambivalence was .61. Attempts to enhance the reliability were unsuccessful. Therefore, lack of support for findings regarding the attitude ambivalence variable should be interpreted with caution.

            Behavior. A behavior measure was created from two sources of data. First, during phase one of the study, some participants gave the researchers informed consent to review UHS flu vaccination records. Therefore, records were reviewed and behavioral measures were coded as either (a) having received a vaccination following exposure to the threat appeal, (b) having received a vaccination prior to exposure to the threat appeal, (c) not receiving a vaccination, or (d) not consenting to review of records. This coding resulted in a total of 33 missing cases on the behavior variable. Subsequently, the self-report data from the questionnaire administered during the second phase of the study were reviewed for these 33 missing cases. By cross-referencing the self-report data with the other behavioral data, six missing data points were eliminated from the behavior variable. This procedure yielded a total sample size of n=152 for the behavior variable.



Preliminary Analyses

Descriptive Analyses. Before any of the main analyses were interpreted, the distributions of the dependent variables were analyzed, but the findings did not signal any immediate problems with the variables of interest.

            Measurement Condition. In order to enable the main analyses below to ignore the measurement manipulation, a multivariate analysis was conducted to test for any main or interaction effects of the measurement condition upon our dependent variables of interest. Neither a main effect for measurement condition (Λ =.959, ns), nor an interaction effect with our independent variable—vaccination history—(Λ=.921, ns) was observed. Therefore, the effects of the measurement condition will be ignored in the following discussion.

Main Analyses

            Cognitive Response. H1 predicted that behavioral commitment to avoid influenza vaccinations would be related to greater depth of processing, such that non-vaccinators would report more total cognitions than vaccinators. A t test showed no difference between non-vaccinators and vaccinators (t (177)=.51, ns, h=.03) (See Table 3). Therefore, the analyses provided no support for H1.

            H2 predicted that the dominant cognitive response produced by non-vaccinators would be more negative than those produced by vaccinators. A t test revealed a significant difference between the groups (t (177) =2.282, p<.05, h=.17), such that non-vaccinators produced more negative dominant cognitive responses than vaccinators. Thus, the analyses provided support for H2.

            Biases in Message Processing.   H3 predicted that non-vaccinators would produce biased perceptions of the message by underestimating  one or more of the elements of the threat appeal. A series of t test were analyzed for each of the elements: severity, likelihood, response-, and self-efficacy. The t tests revealed no significant differences between non-vaccinators and vaccinators on severity (t (177)=.79, ns, h=.06), likelihood (t (177)=43.26, ns, h=.00), or self-efficacy (t (177)=53.30, ns, h=.05) (See Table 3). However, analyses did reveal that non-vaccinators produced significantly lower estimates of response-efficacy than did vaccinators (t (177)=2.10, p<.05, h=.16). Thus, the analyses provided only limited support for H3.

            Emotional responses to the persuasive message. First, in order to address questions concerning emotional responses to the threat appeal, we conducted a series of analyses to discern whether or not each group realized a significant change in emotion from baseline measures of each emotion to post-threat measures. This analysis informs the reader as to whether or not the threat appeal resulted in a change in emotion experience for the non-vaccinators and vaccinators. For non-vaccinators, paired sample t tests revealed that post-threat appeal measures of fear (t (177)=11.83, p<.05), sadness (t (177)=3.32, p<.05), and guilt (t (177)=3.19, p<.05) were significantly higher than baseline measures, while post-threat measures of anger were not significantly different from baseline measures (t (177)=1.91, p=.06) (See Table 4). Among vaccinators, the only emotion resulting in a significant change from baseline to post-threat measures was fear (t (177)=10.33, p<.05). Analyses for anger, sadness, and guilt (t (177)=.94, 1.03, .71 respectively) failed to achieve statistical significance for the vaccinator group.  

Second, for each emotion, we compared the change scores for emotional experiences between the groups. H4 predicted that, due to the nature of the persuasive message (i.e., a threat appeal), both vaccinators and non-vaccinators would experience an increase in fear following exposure to the message. A t test revealed no significant differences between the groups in change in fear (t (177)=1.25, ns, h=.10). Thus, the analyses revealed an increase in fear for both non-vaccinators and vaccinators, but the mean change scores for the groups were not significantly different providing support for H4.

            H5 predicted non-vaccinators would report a greater experienced level of anger than vaccinators. A t test did not show a significant difference in levels of anger change produced by the groups (t (177)=.68, ns; h=.06). Although the mean change scores were in the predicted direction, the means were not significantly different. Thus,  H5 was not supported.

RQ1 sought to uncover the relations between the experience of sadness and guilt following exposure to the threat component of the message for vaccinators and non-vaccinators. A t test was conducted to test for differences in emotion change scores for sadness and guilt between the groups. The t test revealed no significant differences between non-vaccinators and vaccinators with regard to change in sadness (t (177)=1.52, ns, h=.11). With regard to guilt change, a t test did show a significant difference between the groups (t (177)=2.65, p<.05, h=.20), such that non-vaccinators reported a significantly more guilt following exposure to the threat appeal than did vaccinators.

            Information integration. H6 predicted that behavioral commitment to avoid influenza vaccinators would serve to lower the magnitude of the correlation between dominant cognitive response and attitude, such that non-vaccinators would produce a correlation lower in magnitude than that produced by vaccinators. A bivariate correlation revealed correlations of .48 (p<.05) and .38 (p<.05) for non-vaccinators and vaccinators respectively (See Table 5). A post-hoc z test was then conducted to test for differences between the correlations, but the z-test did not reveal a significant difference (z=.81, ns). Furthermore, the pattern of the correlations ran counter to those predicted by H6. Therefore, analyses provided no support for H6.

H7 predicted a relation between behavior commitment to avoid influenza vaccinations and attitude certainty such that non-vaccinators would report higher levels of attitude ambivalence than vaccinators. A t test revealed a significant difference in attitude ambivalence between the groups (t (177)=2.55, p<.05, h=.19), such that non-vaccinators reported higher attitude ambivalence reports than did vaccinators (See Table 3). Therefore, the findings supported H7.

Bridging the gap from attitudes to behaviors. H8 predicted that due to their behavioral commitment to avoid vaccinations, non-vaccinators would report correlations between attitude and behavioral intention lower in magnitude than vaccinators following exposure to the message. A bivariate correlation revealed correlations of .80 (p<.05) and .69 (p<.05) for non-vaccinators and vaccinators respectively. A z test was then conducted to test for differences, but none were revealed (z=1.65, p=.10). Furthermore, the pattern of the correlations ran counter to those predicted by H8 revealing no support for H8.

Finally, H9 predicted that behavioral commitment to avoid influenza vaccinators would serve to lower the magnitude of the correlation between behavioral intention and behavior,  such that non-vaccinators would produce a correlation lower in magnitude than that produced by vaccinators. A bivariate correlation revealed correlations of .03 (ns) and .24 (p<.05) for non-vaccinators and vaccinators respectively.  Again, a post-hoc z test of differences between the magnitudes of the correlations was conducted but failed to identify any significant differences (z=-.63, ns). Although the pattern of the correlations was in the predicted direction, analyses provided little support for H9.


There is a considerable body of evidence that suggests threat appeals are effective in garnering attitude and behavioral change; however, threat appeals are far from universally effective. The findings here suggest that multi-motive processing arising from prior behavioral commitments may explain some of the limitations in the effectiveness of threat appeals.

Attitude Ambivalence

Under single-motive processing, processors produce cognitions in an attempt to develop or maintain an attitude either in favor of or opposed to an advocated position. This processing leads to attitude certainty, which is an antecedent of attitude-behavior consistency (Gross et al., 1995). On the other hand, multi-motive processors orient toward both cognitions in favor of and opposed to a message advocacy. We argue that these processing demands lead to attitude uncertainty or ambivalence and have implications for the consistency between attitudes and behaviors. Dillard and Anderson (2000) found that one of the strongest indicators of multi-motive processing was attitude ambivalence (h=.46) among non-, low, and high-tanners exposed to a threat appeal concerning sun exposure. Attitude ambivalence reflects the tension between the inherent incompatibility of competing motives to process. In a sense, attitude ambivalence is a measure of one’s struggle to integrate information from a persuasive message with attitudes held prior to message exposure that indicts the held attitude.

It was predicted that participants committed to avoiding flu vaccinations, non-vaccinators, would report greater attitude ambivalence than vaccinators. As predicted, non-vaccinators did report greater attitude ambivalence than vaccinators. We argue that the heightened ambivalence is derived from the tension between goals for self-preservation and behavioral commitments faced under multi-motive processing.  Of note, the effect size for the finding was only h=.19. Thus, the differences between single- and multi-motive processing may not be as evident given the issue at hand. 

Cognitive Response

            Prior research (Chaiken et al., 1989; Giner-Sorolla & Chaiken, 1997) lead to predictions that multi-motive processors would produce more total cognitions than single-motive processors. However, the additive effect of validity seeking and defense motives was not realized in our analyses. Instead, the data suggest a possible ceiling effect on the cognitive capacity of participants based on the relatively large number of cognitions produced by all participants (See Table 3). Dillard and Anderson (2000) found a similar effect for depth of processing among single- and multi-motive processors. Of note, both studies obtained depth of processing measures with large standard deviations. Further research controlling for individual differences (i.e., need for cognition) may better inform this prediction. While it is important to consider the volume of cognitive responses produced by the groups, it is also informative to explore the valence of reported cognitions. We discuss this issue below.

            Based on the notion that defensive processing requires that one counter-argue against a persuasive message and research that suggests a relation between defense motives and negative cognitions (Giner-Sorolla & Chaiken, 1997), we predicted that multi-motive processors would produce cognitive reports more negatively valenced than would single-motive processors. Analyses revealed that multi-motive processors produced reports that were more nearly balanced in terms of total supportive and counter-argumentative reports than were reports by single-motive processors whose reports were significantly more supportive in nature (See Table 3). For multi-motive processors in this context, we believe that this balance between the reports reflects the conflict faced by multi-motive processors, as they attempt avoid the threat of contracting influenza and maintain behavioral commitments to avoid vaccinations. Given some evidence of single- and multi-motive processing, we now consider the potential biases resulting from multi-motive processing in comparison to single-motive processing.

Biases in Message Processing

            We had predicted that the tension multi-motive processors realize, as they attempt to juggle conflicting goals, would result in biased processing of the persuasive message. Specifically, these biases would take the form of systematic underestimations of the various elements of the threat appeal—likelihood of the threat, severity of the threat, response efficacy of the recommended actions, or self efficacy to carry out the recommended actions. Analyses revealed that the only significant between groups difference was obtained for the response efficacy variable. Consistent with our prediction, non-vaccinators produced a lower estimate for the efficacy of the recommendations than did vaccinators. The pattern of the findings lends some support to two conclusions. First, both vaccinators and non-vaccinators exhibit indicators of validity processing. The largely similar means for likelihood, severity, and self efficacy indicate similar perceptual processes, as participants attempt to validate the threat appeal. Second, the underestimation of response efficacy by non-vaccinators may reflect an attempt to rectify the conflict between the concurrent, competing motivations to maintain one’s behavioral commitment and simultaneously resolve a threat to self-preservation. In other words, non-vaccinators underestimate the efficacy of getting an influenza vaccination in order to justify inattention to the threat and maintaining behavioral commitments to avoid vaccinations. While this bias is informative, we also considered how affective experiences might inform the findings.

Emotional responses to the persuasive message

            Consistent with our predictions, both vaccinators and non-vaccinators experienced fear arousal following exposure to the threat appeal, and the level of arousal experienced was not significantly different between the groups. This arousal suggests that both groups recognize influenza as a threat to self-preservation goals and therefore process the message in an effort to validate attitudes and behaviors concerning the threat. The findings provide some evidence of motivation toward validity seeking processing by both groups, not just single-motive processors.

With regard to the experience of other emotions, vaccinators failed to experience a significant change in emotion for baseline to post-threat measures of anger, sadness, and guilt. On the other hand, non-vaccinators experienced a significant change from baseline to post-threat in sadness and guilt (of note, anger change from baseline to post-threat approached significance). Sadness occurs when one appraises the environment and discerns a loss of well-being which one is unable to restore (Frijda, 1986; Lazarus, 1991). For non-vaccinators, appraisal revealed a loss and resignation resulting in experiences of sadness due to the inherent incompatibility of their competing goals. These findings appear to be consistent with multi-motive processing demands.

Similarly, non-vaccinators also experienced guilt which is experienced when one appraises that they have failed to meet some personal standard (i.e., maintaining my commitment to a valued behavior) (Frijda, 1986; Lazarus, 1991). In this study, it appears that multi-motive processors recognized the likelihood, the severity, and their ability to carry out threat averting recommendations, yet their behavioral commitment to avoid vaccinations compelled them to forego carrying out the recommendations. It appears that one method of relieving the tension of multi-motive processing is to underestimate the efficacy of the recommended response. However, for multi-motive processors, this practice results in the arousal of guilt, which is experienced as one recognizes his or her failure to attend to self-preservation goals.

A final note with regard to anger is also warranted. While levels of statistical significance were not realized for both baseline to post-threat measures of change and between groups change in anger, the pattern of the means was in the predicted direction. While it appears that non-vaccinators realized the implications of the persuasive message with regard to their loss of autonomy, sadness and guilt experiences appear to be better illustrators of defensive processing. This finding in combination with an increase in fear as an indication of validity processing lends further support to the presence of multi-motive processing and its affective outcomes.

Information Integration: From Message Processing to Behavior

The success of persuasive messages is contingent upon the integration of cognitions about the message into attitudes, and subsequently, transforming attitudes into actual behaviors. This process of integration and transformation can be assessed through three distinct steps: (a) integrating information into attitudes, (b) transforming attitudes into plans to carry out the behavior, and (c) transforming plans into actual behavior. At the first step, persuasion requires that the message processors engage the information (e.g., evidence, arguments, etc.) presented in the threat appeal and report attitudes toward the advocacy consistent with the cognitions. We had predicted (H6) that multi-motive processors, due to the conflicting nature of their motivations, would not successfully integrate information presented in the threat appeal into attitudes toward the message. The findings did not support the prediction. Consistent with validity seeking processing, both vaccinators and non-vaccinators produced significant positive correlations between dominant cognitive response and attitude reports. However, as suggested earlier, this successful integration does not insure persuasion.

At the second step, persuasion relies upon the transformation of attitudes toward the message advocacy into plans to carry out a behavior (i.e., behavioral intention). Again, we had predicted smaller correlations between attitude reports and behavioral intention for multi-motive processors; however, the results indicated significant positive correlations between the variables of interest for both vaccinators and non-vaccinators. While the correlations do suggest strong evidence of validity seeking orientations for both groups, behavioral intentions must still be transformed into behavioral actions before persuasion can be successful.

At the final step, message processors must transform plans to carry out the behavior into actual behaviors. Here, we had predicted that the conflicted nature of multi-motive processing would result in significantly lower correlations between behavioral intention and behavior as compared to single-motive, validity processors. For vaccinators, the analyses reveal a significant relationship between behavioral intention and behavior. With significant correlations at each step of the persuasion process, it appears that vaccinators are able to successfully integrate the information offered by the threat appeal and transform that information into a preventative health behavior. For non-vaccinators, this process is not realized, and we begin to see the influence of multi-motive processing evidenced at earlier stages of cognitive and affective processing. Non-vaccinators failed to transform their behavioral intentions into behaviors, as evidenced by the lack of a significant correlation between behavioral intention and behavior. However, this evidence is tempered by the lack of a significant difference between the correlations produced by vaccinators and non-vaccinators. We had anticipated that multi-motive processing would impact the integration of information and the transformation of attitudes into behavioral intention, but it appears that the multi-motive processing’s impact on the persuasion process occurs at the point which behavioral intentions are transformed into an actual health behavior. For non-vaccinators, despite moderate to strong relationships between dominant cognitive responses and attitudes, as well as attitudes and behavioral intention, they still fail to transform these reports into actual behaviors. This finding is not uncommon in the literature where we find that the correlation between behavioral intention and behavior is less than perfect (Fishbein & Azjen, 1975). The findings here lend some support to multi-motive processing as an explanation for these findings. It may be that behavioral intentions reported by message processors reflect evidence of validity processing, yet these intentions fail to get carried out due to conflict arising from concurrent defense motive processing in an effort to meet some other goal salient to the issue.

In general, our predictions here failed to receive support. However, the findings clearly indicate evidence of validity motive processing by both vaccinators and non-vaccinators. Furthermore, multi-motive processing may explain the lack of a significant relation between behavioral intention and behavior for non-vaccinators as the tension of concurrent defensive and validity seeking processing demands is realized at this point in the persuasion process. Thus, multi-motive processing may explain the limited effectiveness of persuasive message in garnering behavioral change. These findings suggest a series of implications for researchers and applied health researchers.

Implications of Multi-Motive Processing


First, it is apparent that controlling for behavioral commitments in persuasion research is important. For multi-motive processors, persuasive messages are processed under conflicting motives. As a result, the link between attitudes and behaviors is “unhitched.” This finding represents in important consideration, as much experimental research either manipulates processing motives or assumes that a validity seeking motive is in place. Researchers must take care to assess baseline attitudes and behaviors prior to message exposure.

Second, there is evidence that behavioral commitments—the very behaviors that researchers seek to change—limit the effectiveness of persuasive messages, and in this case, threat appeals. Interestingly, this failure does not appear to arise directly from defensive processing. Rather, the persuasion process seems to result in a feeling of helplessness (i.e., sadness, guilt) brought on by one’s seemingly futile attempt to strive toward multiple goals deemed salient by an individual processor. Thus, identifying means of limiting the salience of defensive processing or emphasizing the salience of validity seeking processing may represent important directions for future research. These steps might allow multi-motive processors to avoid the experience of sadness, guilt, biased cognitive processing, and the inability to transform attitudes into behaviors.

Third, applied contexts may benefit from what is known about cognitive consistency theories and social judgment theory. Given the lack of consistency obtained between the participants attitudes toward the message advocacy and enacted behaviors, social judgment theory, which advocate broadening of the range of attitude acceptance over time, may illustrate a means of strengthening attitudes. Thus, with further exposure to persuasive messages, participants might be sufficiently motivated to strive for consistency. The findings indicate that messages emphasizing the efficacy of the recommendations would be warranted.

Finally, for researchers interested in applied health contexts, the findings suggest a need to establish healthy behaviors during the initial attitude formation and behavioral development stage. In applied health contexts, persuasive messages targeting younger audiences should be more effective in shaping attitudes and behaviors consistent with the advocacy of messages concerned with healthy behaviors.


When faced with a persuasive message, such as a threat appeal, the multi-motive processing that can result appears to emotional arousal and biased cognitions. There is some evidence to suggest that the impact of this bias and arousal is later realized in the form of a gap between reported attitudes and behaviors. More specifically, multi-motive processors fail to transform behavioral intentions into attitude and intention consistent health behaviors. Thus, there is a need for researchers to recognize the importance of behavioral commitments and its impact on message processing in both experimental and applied health research.



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Table 1

Frequencies of Sex and Race for Non-Vaccinators and Vaccinators

                                                                                     Vaccination Behavior




Demographic                                                    Vaccinators                  Non-Vaccinators




            Male                                                                32                                21

            Female                                                             58                                67


            Caucasian                                                         81                                79

            African American                                                0                                  3

            Asian                                                                  4                                  6

            Hispanic                                                             4                                  0

            Other                                                                 1                                  0



Note. N=178 with 90 vaccinators and 88 non-vaccinators.


Table 2

Argument Descriptions and Means for Novelty, Believability, and Importance

                                                                                             Persuasiveness Dimension




Component                                                                    Novel       Believable      Importance




Consequences of the flu and prediction for rates              2.55               5.89                 5.67    

of infection in the forthcoming flu season.

Story about how a flu infection affected one                     4.22               4.96                 5.26

            student’s social and academic welfare.

Influenza and influenza-related pneumonia are      5.48               5.78                 5.26    

            the sixth leading cause of death in the U.S.

Discussion of viral mutations and flu pandemics.   6.15               5.19                 5.37


Benefits of flu vaccinations.                                             5.26               6.30                 5.89

Dangers of flu vaccinations for those who are      4.63               5.63                 4.48

            pregnant or allergic to eggs.      

Reasons for renewing flu vaccinations annually.    4.63               5.93                 5.63

How to take part in UHS’ flu vaccination program.          3.52               6.37                 5.70



Note. N=27. The argument descriptors reflect only a sample of the material presented for each argument and may not fully represent the entire argument.


Table 3

Means and Standard Deviations (In Parentheses) for Non-Vaccinators and Vaccinators

                                                                              Vaccination Behavior




Variables                                                Vaccinators                          Non-Vaccinators



Total Cognitive Responses                    11.20a    (4.06)                         10.84a     (5.29)

Dominant Cognitive Responses   2.60a    (2.86)                           1.60b    (2.98)

Perceived Likelihood                              3.98a      (.83)                           4.01a      (.91)

Perceived Severity                                  4.18a      (.69)                           4.10a      (.65)

Response-Efficacy                                  4.35a      (.63)                           4.11b      (.86)

Self-Efficacy                                           4.38a      (.82)                           4.32a      (.74)

Attitude Ambivalence                              2.67a      (.88)                           3.02    (.94)



Note. Means with the same subscript are not significantly different at p<.05.
Table 4

Mean Emotion Change Scores (Baseline minus Post-Threat Measures) and Standard Deviations (In Parentheses) for Non-Vaccinators and Vaccinators