493924 research-article2013
CRX42510.1177/0093650213493924Communication ResearchShen and Mercer Kollar
Article
Testing Moderators of Message Framing Effect: A Motivational Approach
Communication Research 2015, Vol. 42(5) 626–648 © The Author(s) 2013 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0093650213493924 crx.sagepub.com
Lijiang Shen1 and Laura Min Mercer Kollar1
Abstract A 2 (frame: gain vs. loss) × 2 (evaluative input: high vs. low) × 3 (target behavior: sunscreen, self-exam, and indoor tanning) quasi-experimental study (N = 452) was conducted to test moderators of message framing effect. The results showed that effectiveness of a frame is a function of its evaluative input. There was evidence that the gain frame is more effective for prevention behavior and the loss frame for detection behavior. There was no evidence that proscriptive vs. prescriptive behaviors moderated framing effect. Data also suggested that dispositional motivations can be a moderator of message framing effect, but might be contingent upon level of evaluative input and type of behavior. Keywords message framing, evaluative input, detection, prevention, prescriptive, proscriptive, BIS, BAS, skin cancer
The strategy of message framing has received substantial attention from both basic and applied communication research. Message framing refers to the persuasive strategy either to highlight benefits and rewards from compliance with the message advocacy or to emphasize the costs and punishments associated with noncompliance. While individual studies tend to yield inconsistent findings regarding the persuasive impact of the two message frames, meta-analyses have shown that the two frames were no different in their impact on persuasion outcomes (O’Keefe & Jensen, 2006) and that the grain frame might lead to a slightly more effortful message processing than the loss frame (O’Keefe & Jensen, 2008). 1University
of Georgia, Athens, GA, USA
Corresponding Author: Lijiang Shen, Department of Speech Communication, University of Georgia, 110 Terrell Hall, Athens, GA 30622, USA. Email:
[email protected]
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Scholars have also devoted considerable effort to investigate moderators of message framing effect, that is, to specify factors that regulate the effectiveness of messages presented in either frame. The most systematic and intense research in and testing of moderators of message framing effects has been carried out by Salovey and his associates (see Rothman, Bartels, Wlaschin, & Salovey, 2006; Salovey, Schneider, & Apanovitch, 2002). It was proposed that the gain frame is more effective to encourage prevention behavior, while the loss frame is more effective to promote detection behavior. There have been efforts, although not as systematic, from other researchers investigating possible moderators of message framing effect, including behavioral norm (e.g., Blanton, Stuart, & Vanden Eijnden, 2001), involvement (e.g., Cox & Cox, 2001; Martin & Marshall, 1999), affect (e.g., Keller, Lipkus, & Rimer, 2003), attitude ambivalence (e.g., Broemer, 2002), need for cognition (e.g., Zhang & Buda, 1999), and information processing (e.g., Smith & Petty, 1996). In their meta-analyses, O’Keefe and Jensen (2007, 2009) found that the slight edge of the gain frame regarding prevention behavior mainly came from studies on dental hygiene behaviors, and that of the loss frame regarding detection behavior was primarily due to studies on breast cancer self-exam behavior. The two frames were not different in their effectiveness for any other types of behaviors. No other significant moderator of message framing effect was detected in O’Keefe and Jensen’s series of meta-analyses. However, the heterogeneity in effect sizes in these meta-analyses indicated that there existed potential significant moderators. In their responses to O’Keefe and Jensen (2007), Latimer, Salovey, and Rothman (2007) called for more research on potential moderators, particularly motivational variables. That call was echoed by Rothman and Updegraff (2010). Rothman and Updegraff propose two general perspectives regarding moderators of message framing effect: (a) individuals’ construal of target health behavior (e.g., detection vs. prevention, Rothman et al., 2006; Salovey et al., 2002) and (b) individuals’ dispositional sensitivity to outcomes presented in gain/loss frame (e.g., Mann, Sherman, & Updegraff, 2004; Shen & Dillard, 2007; Yan, Dillard, & Shen, 2010). In this article, we strive to answer these calls by explicating and testing potential moderators of persuasive message framing effect from a motivational perspective. First, we explicate potential moderators of message framing effect that are motivational in nature and within the framework of regulatory fit theory (Higgins, 2000, 2005). Second, data from a quasi-experimental study will be presented to test these moderators. And third, implications for future studies will be discussed.
Motivational Forces in Message Framing The two general approaches to testing moderators of message framing effect proposed by Rothman and Updegraff (2010) suggest that we should turn to (a) the message content, that is, the nuances in target behavior, and the respective consequences from compliance or noncompliance (i.e., how each frame is constructed); and (b) individual differences that might shape their responses to the message content. In other words, we need to consider characteristics of both the message and the recipient. In the next
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section, hypotheses regarding potential moderators of message framing effect will be derived based upon this premise.
Motivational Forces From Message Content The content in the two message frames consists of two components: (a) the recommended behavior and (b) the consequences that come with adopting the behavior (presented in the gain frame), or the outcomes of failure to adopt the recommendation (presented in the loss frame). Correspondingly, they can function as two sources of motivational forces: (a) information regarding the consequences and (b) property of the recommended behavior. Motivational force from framed consequences. Prospect theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1981) is often considered as the intellectual origin of research in persuasive message framing. This might have been the cause of overlook regarding the potential differences between framing in prospect theory and persuasive message framing. The two frames in prospect theory have the exact same utility. For example, if a program is adopted, 200 out of 600 people will be saved versus 400 out of 600 people will die, or a steak is 10% fat versus 90% lean. On the other hand, the linguistic device in persuasive message framing can be constructed in multiple ways. The gain frame can be constructed as (a) obtaining reward, (b) avoiding punishment, or (c) the combination of the two; while the loss frame as (a) suffering punishment, (b) foregoing reward, or (c) the combination of the two (Brendl, Higgins, & Lemm, 1995; Salovey, et al., 2002, see also Levin, Schneider, & Gaeth, 1998 for typology of framing). Therefore, in persuasive framing, the two frames, and even the same frame, can be quite different in utility and evaluative information. Evaluative information of or evaluative input from a specific message frame can be defined as the magnitude of perceived utility associated with the consequences (from either compliance or noncompliance) presented in the message frame. Evaluative input can either be positive (i.e., from the gain frame) or negative (i.e., from the loss frame). Its magnitude increases when a positive value becomes more positive and a negative value becomes more negative (i.e., further away from zero). The literature in persuasive message framing research tends to consider all gains (or losses) as equal in utility and containing the same amount of evaluative information, as reflected in the fact that (a) not all studies performed induction check, and (b) when frame inductions were checked, only the valence of each frame was assessed and the potential differences in magnitude of evaluative input were oftentimes overlooked. Results from Idson, Liberman, and Higgins (2000) suggested that the perceived evaluative input could be very different when the same frame (gain or loss) is constructed differently. Specifically, regarding the hedonic intensity (i.e., the perceived negativity or positivity), Idson et al. showed that the form of reward is larger than the form of no punishment (both are of positive values) and the form of punishment is larger than the form of no reward (both are of negative values). Given that hedonic intensity is a function of magnitude of perceived utility, different forms of the frame would vary in evaluative input. Along with this line of argument, it was predicted that:
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Hypothesis1 (H1): Evaluative input of each frame is determined by the form of frame such that: Hypothesis1a (H1a): For the gain frame, the form of rewards has higher evaluative input than the form of no punishment. Hypothesis1b (H1b): For the loss frame, the form of punishments has higher evaluative input than the form of no reward. Theories tend to agree that evaluative information or expected utilities associated with the consequences of behaviors have motivational property and influences persuasion outcomes. The theory of reasoned action (TRA, Fishbein & Ajzen, 2010) proposes that attitude toward a behavior is determined by the likelihood of possible outcomes associated with the behavior and the evaluation of each outcome. The theory of protection motivation (Rogers & Prentice-Dunn, 1997) suggests that protection motivation is determined by (a) how negative and how likely the outcomes of a risky behavior might be (i.e., when not complying with the recommendation), and (b) if one can enact the recommend behavior and how effective the recommendation is in avoiding the negative consequences (i.e., if one complies with the message). Assuming the frames have the same or similar outcome likelihood, and consistent with these conceptualizations based on expectancy values, it is proposed that evaluative input presented in a message frame (gain or loss) has motivational forces; and that the frame with higher evaluative input should be more persuasive than the one with lower levels of evaluative input. Hence, a main effect of form of frame was predicted that: Hypothesis2 (H2): The form of frame with higher evaluative input is more effective than the form with lower evaluative input. The evaluative space model (Cacioppo & Berntson, 1994; Cacioppo, Gardener, & Berntson, 1997) suggests that nuances in evaluative input presented in various forms of message frames would influence the relative effectiveness of the two frames. This model proposes that positive and negative evaluative responses are governed by two distinctive motivational systems (see also Thayer, 1989; Watson, Wiese, Vaidya, & Tellegen, 1999). Each motivational system is defined as an activation function, which translates the value of separate and multifarious input information onto a common evaluative space. The activation functions for positivity and negativity are proposed to differ in important ways. The positive motivational system is characterized by the positivity offset, which is a tendency for the positive motivational system to respond more to comparable low levels of (weak or absent) evaluative input than the negative motivational system. In other words, when the evaluative input is low and at comparable levels, positive information has a larger impact on the individual than negative information. The negative motivational system is manifested as the negativity bias, which is a tendency for the negative motivational system to respond more intensely than the positive motivational system to comparable increases in evaluative input. In other words, as evaluative (both negative and positive) input increases, the negative system responds
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with greater relative increases in motivational output per unit of activation than the positive system. This is consistent with the rationale in prospect theory, as presented in the S-shape value function (Tversky & Kahneman, 1981, p.454). Given that the two message frames in persuasion can be in multiple forms (Brendl, et al., 1995; Salovey, et al., 2002), potentially they might be quite noncomparable in terms of evaluative input. The evaluative space model does not have specific predictions regarding how the two motivational systems would respond when the negative information and positive information input are not quite comparable. In addition, there are no specific criteria regarding the threshold for amount of evaluative input, above which the negativity bias will be at play and below which the positivity offset will be in effect. Ito and Cacioppo (2005) suggested that another way of conceptualizing the activation functions is as regression parameters, with the positivity offset representing a higher intercept value for the positive activation function and the negativity bias representing a steeper slope for the negativity activation function (p. 3). This suggests that the relative responsiveness of the two motivational systems is quite variable when the negative information and positive evaluative information are not comparable. Within the range of positivity offset, the positive motivational system might be more responsive even when there is less positive input than negative input. On the other hand, within the range of negativity bias, the negative motivational system might be more responsive even when there is less negative information than positive information. Overall, in the context of persuasive message framing, this provides little theoretical guidance regarding which frame might be more persuasive when they are not comparable in evaluative input. Hence, we asked a research question: Research Question1 (RQ1): Do different levels of evaluative input in the two frames moderate the framing effect? Specifically, (a) Is the gain frame more effective than the loss frame when evaluative input is low (i.e., positivity offset)? (b) Is the loss frame more effective than the gain frame when evaluative input is high (i.e., negativity bias)? Motivational forces from property of recommended behaviors. Motivational forces can also arise from how individuals construe an option and outcomes associated with that option, which then leads to differences in decision making (Kahneman & Tversky, 1979, 1984). Consistent with prospect theory, Rothman and colleagues (Rothman & Salovey, 1997; Rothman et al., 2006) argue that when the target behavior is perceived as risky and uncertain (i.e., detection behavior) the loss frame is more effective; while the gain frame is more effective when the target behavior is viewed as safe and certain (i.e., prevention behavior). The meta-analyses by O’Keefe and Jensen (2007, 2009) showed that such moderating effects were rather small, and mainly driven by two specific behaviors (breast cancer self-exam and dental hygiene behaviors). This inconsistency might have been caused by the fact that most of the studies have taken prevention versus detection behaviors at their face values, rather than how individuals have construed them. It
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always remains an empirical question if individuals in a particular study would perceive detection behavior as risky and uncertain and prevention behaviors as safe and certain. Also, there might be issues in such a dichotomy of behaviors. The distinction between the functions of detection and prevention behaviors might be a false one, as reflected in the term “preventive screening.” After all, both behaviors serve the function of maintaining and enhancing one’s health (Glanz, Rimer, & Viswanath, 2004). Research in motivation systems suggest that another relevant and important distinction among behaviors lies in their motivational direction. Persuasive campaigns typically ask individuals to change their behavior in one of two ways. Some promote actions such as exercise, product purchase, consumption of fruits and vegetables, or seat belt use. Other campaigns ask individuals to avoid drugs, stop smoking, limit their use of alcohol or reduce their exposure to the sun. Hence, we might say that there are two general forms of target behaviors. Prescriptive or action-oriented behaviors, encourage message recipients to do something. In contrast, proscriptive, or restraintoriented behaviors, advise the target audience not to do something (Yan et al., 2010). Higgins’ (2000, 2005) regulatory fit theory suggests this property of advocated behaviors in framed messages would have important implications for persuasion. Regulatory fit in persuasive message framing. Higgins’ (2000, 2005) regulatory fit theory emphasizes on the match (i.e., fit) between one’s motivational orientation and goal pursuit strategy. The individual feels right when the two match. The feeling right effect then leads to more persuasion. It should be noted that feeling right effect is independent of positive affect (Cesario, Higgins, & Scholer, 2008). Although promotion and prevention foci have been used as a primary vehicle for testing the persuasive impact of regulatory fit, regulatory fit can apply to any motivational orientation with a matching goal pursuit strategy. The message content and property of behavior advocated in framed persuasive messages suggest that there are two distinctive means in which regulatory fit can be produced. First, when a detection behavior is perceived as risky and uncertain and a prevention behavior as safe and certain, regulatory fit arises from matching in valence. There will be fit when the detection behavior is recommended in the loss frame and the prevention behavior in the gain frame. Second, the feeling right effect also can result from matching in motivational direction. In general, individuals are motivated to approach positive end states and to withdraw from negative end states. Hence, regulatory fit emerges when the loss frame is coupled with a proscriptive behavior (i.e., to refrain from doing something), and the gain frame with a prescriptive behavior (i.e., to do something). These two means of regulatory fit suggest the following two sets of hypotheses: Hypothesis3 (H3): Function of target behavior moderates message framing effect, specifically: Hypothesis3a (H3a): When encouraging detection behavior, the loss frame is more persuasive than the gain frame. Hypothesis3b (H3b): When advocating prevention behavior, the gain frame is more effective than the loss frame.
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Hypothesis4 (H4): Motivational direction of target behavior moderates framing effect, specifically: Hypothesis4a (H4a): When recommending proscriptive behavior, the loss frame is more persuasive than the gain frame. Hypothesis4b (H4b): When encouraging prescriptive behavior, the gain frame is more effective than the loss frame.
Motivational Forces from Dispositional Differences In addition to nuances in message content, individual differences in the two dispositional motivational systems can also influence individuals’ sensitivity and response to positive and negative information. Scholars have investigated the individual differences in dispositional motivations to approach favorable outcomes and to avoid unfavorable ones (e.g., Carver & White, 1994). Others have explored the notion that individuals differ in their sensitivity to the presence of positive outcomes and/or to the absence of negative ones (e.g., prevention focus and promotion focus, Higgins, 1999). It is believed that such individual differences are relatively stable across situations. For example, there are individual differences in chronic activations in the behavioral inhibition system (BIS), which is more responsive to negative information, and in the behavioral activation system (BAS), which is more sensitive to positive information (Carver & White, 1994). The function of the positive motivational system is to initiate goal-directed, approach behaviors; while the function of the negative motivation system is to inhibit certain action that might results in punishments, that is, withdrawal behaviors. Consider the nature of motivational systems and the information and types of behaviors advocated in the two message frames, there are two more means in which regulatory fit might be generated. First, there is regulatory fit between the motivational systems when they match with the end states presented in the message frames. That is, there will be regulatory fit when individuals dominant in the positive motivation system are exposed to the gain frame, and individuals dominant in the negative motivation system to the loss frame. There has been empirical evidence that an individual’s motivational systems could moderate processing of persuasive messages presented in gain versus loss frames. Specifically, higher scores in dispositional positive activation function are coupled with more responsiveness to the gain frame; while higher scores in dispositional negative activation function lead to more responsiveness to the loss frame (Shen & Dillard, 2007, 2009; Sherman, Mann, & Updegraff, 2006; Updegraff, Sherman, Luyster, & Mann, 2007; Yan, et al., 2010, also see Rothman and Updegraff, 2010 for a review). Increased strength of engagement in message processing activity is one of the principles of regulatory fit that could influence persuasion (Cesario et al., 2008; Lee & Aaker, 2004). Hence, both theory and empirical evidence lead to the following hypothesis: Hypothesis5 (H5): Dispositional differences in motivational systems moderate message framing effect such that:
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Hypothesis5a (H5a): Individuals high in dispositional positive activation function are more persuaded by the gain frame than the loss frame. Hypothesis5b (H5b): Individuals high in dispositional negative activation function are more persuaded by the loss frame than the gain frame. Existing studies on the moderating role of motivational systems also tend to overlook the nuances in how the two frames are constructed and the potential impact of variability in evaluative input. As the evaluative space model suggests, in the presence of external stimuli, the two activation functions vary at different levels of evaluative input: Positivity offset occurs at low levels and negativity bias emerges at increased levels. It is unclear; however, if activation of the two dispositional motivation systems would vary in the same pattern when the evaluative input is not comparable. Hence, we had the following research question: Research Question2 (RQ2): Is the moderating effect of motivational systems consistent across different levels of evaluative input? Second, regulatory fit can also occur when individuals dominant in positive motivation are asked to perform a prescriptive behavior and individuals dominant in negative motivation to perform a proscriptive behavior. There has been no direct empirical evidence for this means of regulatory fit. However, there is some evidence that the negative motivation system is associated with withdrawal/avoidance emotions, and the positive motivation system with approach emotions (Carver, 2004; Dillard & Anderson, 2004; Harmon-Jones, 2003, 2004; Harmon-Jones, Sigelman, Bohlig, & C. Harmon-Jones, 2003; Heponiemi, Keltikangas-Jarvinen, Puttonen, & Ravaja, 2003; Shen & Bigsby, 2010). This line of argument led to the following hypothesis: Hypothesis6 (H6): Dispositional differences in motivational systems and motivational direction of advocated behavior interact to determine message effects such that: Hypothesis6a (H6a): Individuals high in dispositional positive motivation system are more persuaded when they are encouraged to perform a prescriptive than a proscriptive behavior. Hypothesis6b (H6b): Individuals high in dispositional negative motivation system are more persuaded when they are asked to perform a proscriptive than a prescriptive behavior.
Method Overview Experimental design. The study was a 2 (frame: gain vs. loss) ×2 (evaluative input: high vs. low) × 3 (behavior: sunscreen, self-exam, and indoor tanning) factorial design.
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Stimuli messages. The stimuli were 12 messages related to skin cancer, designed in a threat-recommendation format. Each one first described the risks associated with skin cancer (information adopted from the Skin Cancer Foundation), and then recommended a course of action that would alleviate such risks: use sunscreen, perform self-exam, and do not use indoor tanning salons. The topic of skin cancer was selected particularly because it is associated with a variety of target behaviors, which allows hypothesis testing related to behavior-related moderation effects. The three target behaviors varied in two dimensions: First, sunscreen use and avoiding indoor tanning can be considered as prevention behaviors; while self-exam is a detection behavior. Second, sunscreen use and self-exam are prescriptive behaviors; while avoiding indoor tanning is proscriptive in nature. The messages differed only in the recommendation component, where message frame manipulation occurred. The appendix presents the recommendation sections of the stimuli messages. There were two forms of the gain frame: one discussed the rewards one would receive and the other presented the punishments that one would avoid, if she or he complies and performs the target behavior. There were also two forms of the loss frame: one discussed the punishments one would suffer and the other presented the rewards that one would miss if she or he does not comply and fails to perform the target behavior. The different forms of each frame were designed to induce different levels of evaluative input.
Participants Participants were 452 students recruited from undergraduate classes in Communication at the University of Georgia. Participation in the study either fulfilled students’ course requirement, or earned them a small portion of extra credit. The participants ranged in age from 18 to 37 years (M = 19.44, SD = 1.55), with 81.4% describing themselves as White/Caucasian, 8.8% as being of Asian descent, 2.2% as Hispanic descent, 6.4% as African descent and 1.1% as other. Sixty-nine percent reported their sex as female and 39% as male. There were slight variations in the actual sample size in data analyses due to missing values.
Procedure Data collection took place online. The 12 stimuli messages and measurement instruments were set up on SurveyMonkey, resulting in 12 versions of the survey. A separate webpage was set up as the informed consent page. Clicking the “consent” button on that page activated an automated algorithm that randomly assigned the participants to one of the 12 versions of the survey, each corresponding to one cell out of the 12 experimental conditions. After being randomly assigned to one of the 12 surveys, the participants reported their previous behavior regarding the three target behaviors. Then they read the threat component of the skin cancer message, before they provided affective responses, and then listed whatever came to their mind when they were reading the message. Next, they responded
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to questions assessing their perceptions of the risk involved in skin cancer. Then they read the recommendation component of the message before completing the affective response and thought-listing task one more time. Next, they responded to items assessing their perceptions of the recommendation component, and of the overall message, before they reported their attitude toward the target behavior. Finally, they responded to the BIS/BAS scales (Carver & White, 1994) and provided demographic information. The entire session was about 35 minutes. Not all data were reported in this article.
Measures Induction check. The check for message framing manipulations was measured by four 7-point semantic differential items. The participants were asked to make a judgment about the emphasis of the message on the following word pairs: costs/benefits; loss/gain; advantages/disadvantages; and negative/positive outcomes (α = .92). This scale also served as the measure for evaluative input. Attitude toward the target behavior. Seven 5-point semantic differential items were used to measure attitude toward each target behavior. The word pairs were: good/ bad, wise/foolish, favorable/unfavorable, desirable/undesirable, beneficial/detrimental, positive/negative, and necessary/unnecessary. These seven items were averaged into an index for attitude toward target behavior (Cronbach’s α = .96). The composite score for indoor tanning was reverse coded and thus became attitude toward avoiding indoor tanning. Behavioral inhibition and activation systems. BIS was measured by seven and BAS by 13 Likert-type items using 5-point response scales anchored at both poles (1 = strongly disagree, 5 = strongly agree). Sample items from the BIS scale include “If I think something unpleasant is going to happen I usually get pretty worked up” and “I worry about making mistakes.” Sample items from the BAS scale include “When I get something I want, I feel excited and energized” and “When I want something I usually go all-out to get it.” Averages of these items were taken as the BIS (M = 3.78, SD = .66) and BAS (M = 3.76, SD = .54) scores respectively. Alpha reliabilities were .75 for BIS and .86 for BAS. Dispositional motivations have been measured with the difference between BAS and BIS, calculated as the BIS score subtracted from the BAS score (e.g., Mann et al., 2004; Yan et al., 2010). A positive value (i.e., BAS score > BIS score) means an individual is BAS-oriented and has a dominant dispositional positive activation function in motivation. A negative value (i.e., BAS score < BIS score) means an individual is BISoriented and has a dominant dispositional negative activation function in motivation. Previous behaviors. A single question asked the participants about their previous behaviors. The question for indoor tanning was, “in the past year, on how many days did you use tanning salons/parlors in an average week?” The question for sunscreen use was, “in the past year, how often did you apply sunscreen when you spent time
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outdoors?” (1 = never and 7 = always). The question for self-exam was a yes/no one: “in the past year, have you ever performed self-exam to look for possible signs of skin cancer?”
Results Induction Check and Hypothesis 1 To check if the frame manipulation successfully induced perceptions of gains versus loss and if form of frame led to variations in evaluative input (H1), a univariate generalized linear model (GLM) was estimated to predict perceived end states, with frame, form of frame and behavior as predictors, and age, sex, and previous behaviors as covariates. Pairwise comparisons were carried out via Bonferroni method to adjust for possible Type I error inflation due to multiple tests. With these parameters and a sample size of N = 450, assuming a = .05, the statistical power to detect an effect size equivalent to r = .20 exceeded .98. There was a significant main effect of frame: F (1, 429) = 15.12, p < .001, η2 = .027. Messages in the loss frame were perceived as focusing on negative end states (M = 3.20, SD = 1.75), while messages in the gain frame were perceived as emphasizing positive end states (M = 3.82, SD = 1.96). There was no interaction involving behavior and this pattern was consistent across the three target behaviors. It should be noted that the mean for the grain frame was below the midpoint of the scale, which suggested that the gain frame induction overall was not successful. Further analyses showed (see below) that induction failure was mainly due to the cell of low evaluative input. There was a significant interaction between message frame and form of frame: F (1, 429) = 11.14, p < .001, η2 = .021. In the gain frame, the form of reward led to higher score (M = 4.04, SD = 0.16), that is, more focus on positive end state, than the form of no punishments (M = 3.47, SD = 0.18), with an effect size equivalent to r = .85. In the loss frame, the form of loss led to lower score (M = 2.84, SD = 0.16), that is, more focus on negative end state, than the form of no gain (M = 3.19, SD = 0.18), with an effect size equivalent to r = .72. Assuming α = .05, both pairwise comparisons were significant. This showed that the form of reward led to higher (i.e., more positive) evaluative input than the form of no punishments for the gain frame; while the form of punishment led to higher (i.e., more negative) evaluative input than the form of no reward. Hence, H1 received support. That is, the form of gain/loss can be considered as increased/high level of evaluative input, and the form of no loss/no gain can be considered as low level of evaluative input. This enabled testing of Hypotheses 2 to 6 and answering RQs 1-2.
The Impact of Evaluative Input, Target Behavior, & Dispositional Motivation Hypotheses 2 to 6 concerned the impact of evaluative input, target behavior, and dispositional motivation on framing effect. And the research questions asked if the impact
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of target behavior and dispositional motivations would be further moderated by levels of evaluative input. A univariate GLM model was estimated to predict attitude toward target behavior, with message frame, level of evaluative input (i.e., form of frame), dummy code for detection versus prevention behaviors (self-exam = 2, sunscreen use = −1, and [avoiding] indoor tanning = −1), dummy code for proscriptive vs. prescriptive behaviors (self-exam = 1, sunscreen use = 1, and [avoiding] indoor tanning = −2), and dispositional motivation as predictors, and age, sex, and previous behaviors as covariates. Pairwise comparisons were carried out via Bonferroni method to adjust for possible Type I error inflation due to multiple tests. The main effect of message frame was nonsignificant: F (1, 412) = .39, p = .53, which is consistent with the meta-analysis (O’Keefe & Jensen, 2006). The mean for the gain frame (M = 4.33, se = 0.05) almost identical to that for the loss frame (M = 4.32, se = 0.07). There was a significant main effect of evaluative input: F (1, 412) = 4.55, p =.03, η2 = .011. Frames with high evaluative input (in the forms of gain or loss) led to more favorable attitude (M = 4.43, SD = 0.66) than frames with low evaluative input (in the forms of no loss or no gain, M = 4.30, SD = 0.62). The pattern was consistent across the two message frames. These results supported H2. The interaction between frame and evaluative input was nonsignificant: F (1, 412) = .59, p = .44. This suggested that evaluative input was not a moderator of framing effect (RQ1). The interaction between frame and (dummy code of) function of target behavior was significant: F (1, 412) = 5.04, p = .03, η2 = .012. When encouraging detection behavior, the loss frame was more effective (M = 4.40, SD = 0.57) than the gain frame (M = 4.22, SD = 0.51). Assuming α = .05, the pairwise comparison was significant (η2 = .024). When recommending prevention behavior, the gain frame (M = 4.44, SD = 0.61) was more persuasive than the loss frame (M = 4.25, SD = 0.78). Assuming α = .05, the pairwise comparison was also significant (η2 = .018). These results supported H3. The interaction between frame and (dummy code of) motivational direction of target behavior was nonsignificant: F (1, 412) = .14, p = .71. Hence, H4 did not receive support. The interaction between frame and dispositional motivation was nonsignificant: F (1, 412) = .25, p = .62. Therefore, H5 was not supported. However, the three-way interaction between message frame, dispositional motivation, and evaluative input was significant: F (2, 412) = 3.50, p = .03, η2 = .016. To answer RQ2, the two-way interaction terms between frame and dispositional motivation within high and low levels of evaluative input were assessed and decomposed. When the evaluative input was low (i.e., in the form of no loss vs. no gain), the interaction between message frame and dispositional motivation was nonsignificant: F (1, 233) = 2.45, p = .12. When the evaluative input was high (i.e. in the form of gain vs. loss), the interaction was significant: F (1, 198) = 4.18, p = .015, η2 = .016. Therefore, there seemed to be variations in the impact of dispositional motivation across different levels of evaluative input: The moderating effect of dispositional motivation on framing effect was nonsignificant when evaluative input was low, but significant when evaluative input was high.
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Figure 1. Message framing effect on attitude as a joint function of dispositional motivation and evaluative input.
H5 was also tested using a different approach since BIS/BAS were measured postexposure and could have been activated as a result of individuals’ exposure to strong situational variations (i.e., message frames). The regression approach (Shen & Dillard, 2009) was adopted in the alternative analyses, where BIS/BAS were regressed onto attitude in three sets of regression models, with the same set of covariates controlled for: (a) within each frame, (b) within each frame when evaluative input was high, and (c) within each frame when evaluative input was low. Neither BIS nor BAS emerged as significant predictors of attitude in any of these regression models. Examination of the marginal means (Figure 1) revealed different patterns across high and low levels of evaluative input. On one hand, when the evaluative input from the message frames was high, the interaction between frame and dispositional motivation was significant and in the form predicted by H5: BAS-oriented individuals were more persuaded by the gain frame (M = 4.49, SD = 0.56) than the loss frame (M = 4.29, SD = 0.78). Assuming α = .05, the pairwise comparison was significant, η2 = .024. And BIS-oriented individuals were more persuaded by the loss frame (M = 4.70, SD = 0.59) than the gain frame (M = 4.40, SD = 0.60). Assuming α = .05, the pairwise comparison was also significant, η2 = .059. On the other hand, although the interaction between message frame and dispositional motivation was nonsignificant when the evaluative input was low, the pattern in marginal means suggested that positivity offset might be at work: The two frames had almost the exact same impact on BIS-oriented individuals (M = 4.37 for the grain frame and M = 4.34 for the loss frame); while the gain frame (M = 4.43) had stronger impact than the loss frame (M = 4.20) on BAS-oriented individuals. The difference, however, was nonsignificant. The interaction between dispositional motivation and motivational direction of recommended behavior (prescriptive vis-à-vis proscriptive) did not reach significance: F (1, 412) = 1.44, p = .06, η2 = .008. Analysis of simple main effects showed BAS-oriented individuals were significantly more persuaded when encouraged to perform a prescriptive behavior (M = 4.47, SD = 0.55) than when asked to perform a proscriptive behavior (M = 4.16, SD = 0.60). Assuming α = .05, the pairwise
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comparison was significant, η2 = .089. Therefore H6a received support. On the other hand, for BIS-oriented individuals, encouraging prescriptive (M = 4.31, SD = 0.56) or proscriptive behaviors (M = 4.36, SD = 0.58) did not make much difference. Hence H6b did not receive support.
Discussion In this article, we set out to explicate and test motivational variables that potentially moderate persuasive message framing. It was proposed that moderators of message framing effect arise from variables that influence how individuals construe the framed persuasive messages: (a) message content that consists of the recommended behaviors as well as the consequences associated with compliance or noncompliance, and (b) dispositional motivation that makes individuals more sensitive to either positive or negative information. Results showed that there was no main effect of message frame, which is consistent with O’Keefe and Jensen (2006). However, the level of evaluative input mattered, and forms of frame with higher evaluative input were more persuasive than forms of frame with lower evaluative input. There was evidence that function of target behavior (detection vs. prevention) moderated framing effect; however, there was no evidence that motivational direction of target behavior (prescriptive vs. proscriptive) was a moderator of message framing effect. The evidence for the moderating role of dispositional motivation was not as clear: (a) It emerged when the evaluative input was high, but nonsignificant when the evaluative input was low; and (b) it might moderate the effectiveness of messages encouraging prescriptive vis-à-vis proscriptive behaviors. These findings have to be interpreted with the potential limitations of the current study in mind. In addition to the variety of target behaviors relevant to skin cancer, the topic was selected because it was relevant to the college population. However, there are two limitations associated with this methodological choice. First, when it comes to skin cancer related target behaviors, the college population might be more concerned with appearance rather than health consequences (e.g., Jones & Leary, 1994), while the consequences in the frame manipulation focused on health consequences. The results might have been more biased than the possible differences between a college sample and a general public sample as documented in Peterson (2001). Second, three target behaviors associated with a single health risk helped to avoid potential confounding effects. For example, the differential framing effect between breast cancer self-exam and sunscreen use could be due to function of behavior, as well as due to types of cancer. This means that internal validity was enhanced. However, there was a trade-off between internal and external validity. Given that all messages were on the same topic of a single heath risk (i.e., skin cancer), the findings in this study were rather limited in generalizability. Third, only attitude was assessed as the outcome in this study. There was no evidence such message effects could materialize in changes in intention or actual behavior. Fourth, induction of the gain frame was not successful in the cell of low evaluative input. Although this highlights the importance of investigating evaluative input and the merit of the current study, it also could have biased the
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results in unknown ways. We would have much stronger confidence if induction in that cell had worked like the other cells.
Evaluative Input Consistent with meta-analysis (O’Keefe & Jensen, 2006), the two frames were equally persuasive in this study. However, evaluative input due to different forms of frame did affect the effectiveness of both frames: The gain frame with high evaluative input (i.e., in the form of reward) was more effective than with low evaluative input (i.e., in the form of absence of punishment); and likewise, loss frame with high evaluative input (i.e., in the form of punishment) was more effective than with low evaluative input (i.e., in the form of lack of reward). Examination of the marginal means; however, suggested a potential explanation for a significant main effect of message frame: When the two frames compared in a particular study was not balanced (i.e., gain with high evaluative input [as reward] vs. loss with low evaluative input [i.e., as no reward], or vice versa), framing effect would be more likely to emerge. That is, the frame with higher evaluative input tends to be more persuasive (H2).
Target Behavior This study also explored the potential impact of target behavior along two dimensions: its function (detection vs. prevention) and motivational direction (prescriptive vs. proscriptive). The evidence from the data was in favor of the function approach (H3), which is consistent with Rothman et al. (2006) and Salovey et al. (2002), but contrary to the results from O’Keefe and Jensen (2007, 2009). That is, other than dental hygiene behaviors and breast cancer self-exam, function of target behavior does not moderate framing effect. Besides sampling error, there are two potential reasons for such a discrepancy. First, it has been argued that it is the construal of behaviors, rather than behaviors per se, that underlies the moderation effect (Rothman & Updegraff, 2010; Salovey et al., 2002). There has been evidence that this moderation effect would be more robust when the perception of detection and prevention behaviors (i.e., risk) was manipulated (Rothman & Updegraff, 2010). Taking detection and prevention behaviors for granted and at surface level probably introduces noises into the data, and hence potential biases in the results. On the other hand, prospect theory suggests that whether individuals are risk averse or risk approach is not determined by frame alone, but rather frame and uncertainty combined. Level of uncertainty in the outcomes of recommend behaviors has seldom been manipulated and/or measured in persuasive message framing. Future studies that measure perceived risk and uncertainty associated with target behaviors would be in a better position to assess this possibility. Second, there might be potential confounding between the health risks and type of behaviors. O’Keefe and Jensen (2007, 2009) found that the advantage of gain frame over loss frame in encouraging prevention behavior could be attributed to dental hygiene behaviors only; and that the advantage of loss frame over gain frame in encouraging detection behavior could be attributed to breast cancer self-exam only. This suggests
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there could be confounding effects that make the picture rather blurred. For example, when framing effect in encouraging breast cancer self-exam (detection) is compared to framing effect in encouraging flossing (prevention), as in a meta-analysis, the potential differences could be due to the health risks (breast cancer vs. gum disease), gender (mostly female vs. both genders), or uncertainty (the perceived prevalence of breast cancer vs. gum disease). Better physical control in future studies should help reduce such confounding effects. More feasible solution might lies in enhancing statistical control by specifying and measuring mediating factors, which has received relatively less attention in the literature, including the current study (Rothman & Updegraff, 2010). The results from this study did not yield support for the motivational direction perspective. However, we hesitate to reject that approach. First, Higgins’ (2000, 2005) regulatory fit theory suggests that a particular message frame will be more effective when there is “fit” between its behavioral advocacy and end states and there exist multiple ways in which regulatory fit can be produced. Results in this study showed that there might be regulatory fit when positive dispositional motivation was matched with approach behavior (proscriptive behavior). Second, it is possible that linguistic recommendation of prescriptive and proscriptive behaviors might not be sufficient to arouse the approach and withdrawal action tendencies. It might require the individuals performing aversive or approach tasks to suffice (e.g., Heponiemi KeltikangasJarvinen, Puttonen, & Ravaja, 2003), as suggested in the embodied cognition literature (Mahon & Caramazza, 2008; Wilson, 2002). Alternatively, message-irrelevant affect might also activate the approach and withdrawal tendencies (Yan et al., 2010; Yan & Dillard, 2010). Future studies that apply these methodologies to enhance BIS/BAS activation levels before message exposure should be better able to test this motivational direction perspective.
Dispositional Motivation Dispositional motivation was the individual differences moderator assessed in this article. There was no evidence for the moderating effect of dispositional motivation. It seemed that the impact of dispositional motivations varied across different levels of evaluative input (RQ2). When evaluative input was high, across all three target behaviors, BAS-oriented individuals were more persuaded by the gain frame than the loss frame, and the pattern was reversed for BIS-oriented individuals. This is consistent with, and adds to a growing body of research (see Rothman & Updegraff, 2010 for a review). When the evaluative input was low, there was no such interaction. This suggested that the two-way interaction between dispositional motivation and message frame might be attributed to frames with high evaluative input only. When the evaluative input is low, a single message might not be sufficient to activate the positive or negative evaluative functions, which are believed to underlie the interaction. The left panel in Figure 1 also suggested that the activation of evaluative functions (positivity offset and negativity bias) might be a joint function of message content and dispositional motivation. Without considering the dispositional motivation, there was no significant interaction between frame and evaluative input, and no pattern emerged
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consistent with either positive offset or negativity bias (results from RQ1). When the dispositional motivation was taken into consideration, there was negativity bias for BIS-oriented individuals and positivity bias for BAS-oriented individuals in the high evaluative input condition. Although the interaction was nonsignificant in the low evaluative input condition, a positivity offset pattern emerged for BAS-oriented individuals. On the other hand, there was no pattern of negativity bias for BIS-oriented individuals. Consistent with the literature, results from H6 also suggested that BIS/ BAS might be responsive to motivational direction involved in the target behaviors. We might want to take caution in interpreting these findings as well. While the structure of BIS is relatively simple, there are three subscales in the BAS scale: reward responsiveness, fun seeking, and drive. There are evidence that the three BAS subscales loaded on a single factor on a higher order (e.g., Campbell-Sills, Liverant, & Brown, 2004; Heubeck, Wilkinson, & Cologon, 1998); as well as evidence that they should be used as three separate constructs (e.g., Muris, Meesters, de Kanter, & Timmerman, 2005; Ross, Millis, Bonebright, & Bailley, 2002). Recent work by Dillard and his associates (e.g., Voigt et al., 2009) suggests that different subscales of BAS are associated with different types of risky health behaviors. There also has been new development in the BIS/BAS literature. Berkman, Lieberman, & Gable (2009) found that (a) BAS was sensitive to both unconditioned and conditioned rewards, but mainly driven by the reward subscale; (b) BIS was selectively associated with responses when there is conflict and uncertainty; and (c) BIS and BAS interacted with each other during conflict when one of the response option was associated with a desired instrumental outcome. The current study obviously overlooked such nuances in the disposition motivations. Future studies that examine the BAS subscales and the newly emerged nuances in BIS/BAS would further our understandings of how the dispositional motivation systems would moderate message framing effect.
Conclusion A quasi-experiment was conducted to test moderators of message framing effect. The results showed that effectiveness of a frame was a function of its evaluative input. There was evidence that gain frame is more effective for prevention behavior and the loss frame for detection behavior. Data also suggested that dispositional motivations might be a moderator, but its moderating role might be contingent upon level of evaluative input; and that it might interact with the motivational direction of the target behavior to produce message effects. Combined, the findings regarding the impact of evaluative input, target behavior, and dispositional motivation indicate that search for moderators of message framing effect might be complicated. It is possible that although some factors do not moderate the framing effect directly, they might determine the condition under which other factors might emerge as moderators. For example, the moderating effect of motivational direction of target behavior (prescriptive vs. proscriptive) might require that the dispositional motivations be activated at certain level, and that of dispositional motivations, in turn, might require the evaluative input from the message frames be elevated.
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Appendix: Recommendations in the Stimuli Messages Indoor Tanning Messages Loss Frame/punishment: Avoid the use of indoor tanning beds/booths. If you use, you will sustain skin cell damage. No matter what you may hear at tanning salons, the cumulative damage caused by UV radiation can lead to premature skin aging, as well as increased risk of skin cancer. Loss Frame/no reward: Avoid the use of indoor tanning beds/booths. If you use, your skin is probably not moisturized, firm, radiant, young, or attractive. If you use tanning beds/booths, you are unable to avoid the harmful effects from the UVA rays; you won’t be able to reduce your risk of skin cancer. Gain Frame/reward: Avoid the use of indoor tanning beds/booths. By not using, you will have a healthier body that is not exposed to the harmful effects linked to tanning beds. This will also leave your skin moisturized, firm, radiant, young, and attractive. Gain Frame/no punishment: Avoid the use of indoor tanning beds/booths. By not using, you can avoid a known cause of cancer. It will also prevent sunburn/redness, irritation, blisters, peeling skin, and swelling, seepage of fluids, photo aging, sun spots, skin damage, and wrinkles.
Self-Exam Messages Loss Frame/punishment: Make sure to routinely examine your skin for signs of skin cancer. If you fail to perform skin self-exam every month, you run the risk of letting skin cancer go undetected. Failure to detect skin cancer early on means that skin cancer will progress to later, worse stages, and more difficult to treat, which increases the risks of disfiguration and other terrible consequences of skin cancer. Loss Frame/no reward: Make sure to routinely examine your skin for signs of skin cancer. If you fail to perform skin cancer self-exam monthly, you won’t be able to increase your knowledge about your skin, or the likelihood that skin cancer can be detected early on. This means that you won’t have better treatment options, a greater success rate, or timely repair of skin damage from the sun. Gain Frame/reward: Make sure to routinely examine your skin for signs of skin cancer. By routinely self-examining your body, you increase your own knowledge about your body and increase the likelihood that skin cancer can be detected early on. This allows for better treatment options, a greater success rate, and can help you in taking steps to repair damage from the sun. Gain Frame/no punishment: Make sure to routinely examine your skin for signs of skin cancer. By routinely self-examining your body, you reduce the risk of letting skin cancer go undetected. Detecting skin cancer early can help to prevent continued growth of the skin cancer, which may spread to other parts of the body in some cases, and decrease fatality rates.
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Sunscreen Messages Loss Frame/punishment: Routinely use sunscreen whenever you are outside for an extended period of time. If you don’t, you greatly increase your risk of sun burn. You are more likely to suffer from redness, irritation, pain, blisters, peeling skin, swelling, seepage of fluids, photo aging, sun spots, skin damage, and wrinkles. Your risk of skin cancer increases substantially. Loss Frame/no reward: Routinely use sunscreen whenever you are outside for an extended period of time. If you don’t, you won’t be able to protect your skin from UV rays; therefore, fail to ensure that your skin stays healthy. You won’t able to enjoy your time outside while keep your skin stay moisturized, firm, radiant, young, attractive, and have a lower risk of skin cancer. Gain Frame/reward: Routinely use sunscreen whenever you are outside for an extended period of time. By doing so, you are able to protect your skin from UV rays and ensure that it stays healthy. This helps your skin stays moisturized, radiant, young, attractive, and have a lower risk of skin cancer. You will be able to enjoy your time outside more. Gain Frame/no punishment: Routinely use sunscreen whenever you are outside for an extended period of time. By doing so, you can avoid sun burn/redness, irritation, pain, blisters, peeling skin, swelling, seepage of fluids, photo aging, sun spots, skin damage, and wrinkles. You will lower your risk of skin cancer. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
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Author Biographies Lijiang Shen (PhD, University of Wisconsin-Madison), is an associate professor in the Department of Communication Studies at the University of Georgia. His primary area of research considers the impact of message features and audience characteristics in persuasive health communication, message processing, and the process of persuasion/resistance to persuasion; and quantitative research methods in communication. His research has been published in major communication and related journals. Laura Min Mercer Kollar, is a doctoral student in the Department of Communication Studies at UGA. Her research interests focus on the role of communication in health interventions and understanding how and why these interventions are successful. Currently, she conducts research surrounding alcohol-related sexual risk behaviors. Before joining the UGA community, she worked as a research project coordinator at Michigan State University (Nursing), Northwestern University (Emergency Medicine), and Emory University (Public Health).
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