Keep Calm and Read O n
The Interplay of Framing, Emotions and Prior Knowledge During a Public Health Crisis Author: Alexander Graf Strachwitz
Supervisor: Dr. Toni G. L. A. van der Meer
Graduate School of Communication Master’s Programme Communication Science
Master’s Thesis
Student ID: 11181230
Abstract
During the outbreak of an infectious disease, public health organizations (PHO s) are
emergency-responders, whose communication with the anxious public is aimed at producing specific behavioral responses. PHOs attempt to persuade their audience through message appeals, such as gains- versus losses- framing: The gains- frame emphasizes positive outcomes of compliance, whereas the losses- frame highlights negative outcomes in case of
non-compliance. Crisis events induce strong negative emotions making it essential for PHOs to manage the affective state of its public. Little is known about how the public responds to framed messages emotionally, how this affects their risk perception, and if the intensity of the emotional arousal varies with their amount of prior knowledge about the disease. The
proposed relationships are tested employing a two (framing: gains vs. losses) x two (prior knowledge: low vs. high) experimental design. A moderated mediation analysis shows that gains-framed messages successfully elicit positive emotions, reducing risk perception (less positive emotions and higher risk perceptions for the high prior knowledge group compared to the low prior knowledge group, indicating a moderation). Conversely, losses-framed
messages evoked a negative affective state, increasing risk perception. These findings highlight that risk perceptions as a response to crisis communication are, in large part,
determined by emotions. Therefore, PHOs must carefully manage the public’s emotional state through meticulously crafted message appeals, hinged on the public’s knowledge about the issue. More specifically, this study shows that PHOs can reliably elicit a hopeful affective state, consequently reducing the probability of irrational panic among the population.
Key words: Framing, emotions, knowledge, risk perception, crisis communication, health communication
Introduction
Safeguarding the public against the threat of infectious diseases through timely
communication by public health organizations (PHOs) is an urgent topic. This assertion holds true not only for academia (Dickmann, Biedenkopf, Keeping, Eick mann, & Becker, 2014), but also for societal actors. In her keynote speech at the G20 Health Ministers’ Meeting 2017, Margaret Chan, Director-General of the World Health Organization (WHO), warned that the international community is not nearly well enough prepared to tackle the next outbreak of an epidemic, due to a lack of effective communication among a variety ofinvolved actors. The present study investigates one crucial constituent of every public health crisis: The
communicative relationship between PHOs and its public.
In order for effective crisis communication to occur, PHOs must make risks salient to the public through their communication efforts (Coombs, 2007; Liu, Austin, & Jin, 2011; Wan & Pfau, 2004). The dissemination of relevant information, which enables the population to protect itself against the health threat (Glik, 2007; van der Meer & Verhoeven, 2014) is arguably the most important task of PHOs, when dealing with an infectious disease outbreak. Additionally, PHOs must make an effort to reassure the public that the crisis is under control, thereby reducing uncertainty and lowering the chance of panic among the public (Johnson, 2016).
A popular theoretical lens to analyze the effect of crisis communication on individuals is the concept of framing. To frame a written message means to make persistent the selection, omittance, or emphasis of specific information (Entman, 1993). O ne way of framing a text that has been frequently discussed and relates to public health communication, is to
distinguish between gains and losses (Tversky & Kahneman, 1981): O utcomes can be framed positively in terms of gains, or negatively by highlighting losses. The present study introduces gains- and losses- framing to the context of organizational crisis communication to better
understand the public’s response to different message frames, thus exploring its practical value for PHOs communicating with their audiences.
Furthermore, individuals also rely on their prior knowledge when assessing a health threat (Nabi, 2003). People familiar with a disease have different risk perceptions, compared to individuals, who are not accustomed with the disease (Lewan & Stotland, 1961). True to the motto “it’s unfamiliar, it must be risky” (Song & Schwarz, 2009, p. 1), the latter group often has higher risk perceptions because it lacks adequate knowledge to assess the crisis message (Rudisill, Costa-Font, & Mossialos, 2012). Altogether, this scarcity of reliable, detailed information about a health threat makes the public more susceptible to the framed information, because it cannot evaluate the magnitude of the crisis (Jin & Han, 2014).
Consequently, prior knowledge is conceptualized as a moderator of the frame-risk-perception-relationship.
However, conceptualizing the linkage of framing and prior knowledge with risk perception as a purely cognitive process ignores the powerful effect which emotions exercise on risk perception (Choi & Lin, 2007; Waters, 2008). It is surprising how little attention has been directed at the role of emotion in crisis communication, given that emotions have the capacity to influence important outcomes, such as judgment of an organizational crisis response strategy (K im & Cameron, 2011). Opening this ‘black box’, the present research paper proposes emotions as an explanatory mechanism, mediating the relationship between framing and risk perception (Xie, Wang, Zhang, Li, & Yu, 2011). Naturally, the public’s first reaction to a sudden and unpredictable event is emotional (Jin, 2010), bringing about the need for PHOs to carefully ‘manage’ the public’s affective state to avoid a ‘communicative
escalation’ of the crisis. Individuals exposed to messages about a threatening health risk are more likely to experience fear because they construe the crisis event as uncontrollable, resulting in higher risk perception (Choi & Lin, 2007; Tannenbaum et al., 2015). However,
much less is known about whether or not PHOs are also able to elicit positive emotions, which reassure the public, through their communication efforts. Thus, the present study, in addition to testing negative emotions as a causal mechanism connecting message appeal and risk perception, also explores the mediating role that positive emotions play in public health crises.
Lastly, the present study hypothesizes that prior knowledge, also moderates the
indirect effect of frames on perceived risk through positive and negative emotions (Averbeck, Jones, & Robertson, 2011; Nabi, 2003). Individuals, who are well- informed about a risk, are less likely to react strictly emotionally to the crisis message. Instead, they will rely on their prior knowledge about the disease to guide their message processing and keep their emotional arousal in check (Rothman & Salovey, 1997), thereby reducing risk perceptions. Hence, knowledge about the disease prior to being exposed to the message appea l is conceptualized as a boundary condition, altering the strength of the framing effect on perceived risk.
In sum, the following two research questions aim to shed light on the interplay between frames, emotions, prior knowledge and risk perception: How do gains- and losses-framed messages, communicated by PHOs, influence individuals’ risk perceptions during a public health crisis? Are emotions and prior knowledge about the risk an explanatory mechanism through which frames affect individuals’ risk perceptions?
Theoretical Frame work
Defining Risk Perception
This study defines risk perception as a subjective assessment of risk (Glik, 2007), which entails both a cognitive and an emotional component (Brewer et al., 2007). More specifically, risk perception regarding a crisis is conceptualized in line with Slovic’s (1987) risk characteristics. He asserts that individuals’ risk perceptions are determined by the extent to which a crisis is conceived as unknown, uncontrollable and dreadful (Choi & Lin, 2007;
Xie et al., 2011). A disease can be described as unknown, if there is a lack of scientific knowledge regarding the virus, due to its novelty and unpredictability. Furthermore, it can be characterized as uncontrollable, if the spread of the disease to other humans cannot be halted through known medical treatments. Lastly, a disease is recognized as dreadful if the
consequences of the infection are fatal and result in large-scale human losses (Leppin & Aro, 2009). An example of a pandemic, which scored high on all three dimensions, is the severe acute respiratory syndrome (SARS). Smith (2006) argues that the public appraised SARS as a “mystery disease, with the aura of being able to strike anyone, anywhere, anytime” (p. 3119). Taken together, these risk characteristics influence how individuals assess and, to a large extent, how they respond to health hazards (Latimer, Salovey, & Rothman, 2007; Rothman & Salovey, 1997).
The Role of Communication in Times of Crisis
Crises (e.g. disease outbreak) not only have real, observable origins, but are also constituted in communication amongst key actors (van der Meer, 2016). Commonly, the public first learns about a public health crisis through the news media or PHOs (Neuwirth, 2010). This points to the central role that the communicative process and mediated
information plays in dealing with crises by providing the tools to collectively make sense of a public health crisis, subsequently facilitating effective coordination between the involved actors (Patriotta, Gond, & Schultz, 2011; Weick, 2009). Glik (2007) contends that, “it is perception of risk, not actual risk, which determines how people respond to hazards” (p. 37). Thus, if PHOs are able to influence the public’s risk perception, they can determine the evolution and real- life impact of the crisis.
In midst of the initial outbreak, the public lacks knowledge about key aspects of a disease, such as transmission or symptoms, which are necessary to gauge the nature of the risk (Gesser-Edelsburg & Shir-Raz, 2015). Therefore, the population at risk will have
different risk perceptions compared to the on-site emergency responders. In order to reduce the chances of further crisis escalation, PHOs attempt to guide the public’s risk perception to the point where they comply with the recommended preventive measures (Rothman & Salovey, 1997). Traditionally, one way of effectively modeling the public’s risk perception through communication is framing (Banks et al., 1995; Johnson, Hershey, Meszaros, & Kunreuther, 1993). Entman (1993) defines framing as, “select[ing] some aspects of perceived reality and mak[ing] them more salient in a communicating text in such a way as to promote a particular problem definition, […] and/or treatment recommendation” (p.52). Organizational crisis communication research asserts that the frame-building process is fundamental for crisis de-escalation (van der Meer & Verhoeven, 2014). It is critical for organizations to participate in the framing process (Holladay, 2009) and forge the public’s risk perceptions in a way conducive to the PHOs’ cardinal interest of solving the crisis. In sum, the communicative mechanism connecting PHOs to its public in times of crisis is indispensable to deescalate the situation while the disease still remains a threat, keep the public informed and calm, and evade adverse consequences (Thelwall & Stuart, 2007).
Gains- and Losses-framing affecting Risk Perceptions
Public health crises caused by novel diseases, whose occurrence or consequences cannot yet be explained by health experts, constitute a chaotic situation. These complex issues can be framed in multiple ways, each emphasizing a specific problem definition and solution. As a result, this frame building process leads to a competition of conflicting frames (Chong & Druckman, 2007). This raises the issue of how PHOs can make sure that their crisis
interpretation is noticed and acted upon in the turmoil following the disease outbreak. One approach to framing, which has been found to effectively influence individuals risk perception is gains-and losses-framing (Tversky & Kahneman, 1981). Prospect theory assumes that there is a robust tendency for individuals to make different decisions based on
whether the outcomes are framed positively as gains, or negatively as losses (Cheung & Mikels, 2011). Public health communicators frequently use gains- framed messages to highlight the advantages of compliance with the sender’s recommendation, as opposed to loss- framed messages, which underline the disadvantages of noncompliance (O'Keefe & Jensen, 2007).
In their classical Asian Disease experiment,1 Tversky and Kahneman (1981) come to the conclusion that individuals tend to be risk-seeking if the health message is framed in terms of losses, and risk-averse if the information is framed in terms of gains. It can be assumed that the observed risk-seeking behavior of participants stems from lower levels of perceived risk regarding the hazard. The robustness of the framing effect on risk perception has been successfully replicated for a variety of different health behaviors (e.g., AIDS prevention: Brondino, 1997; vaccination against West N ile virus: Bartels, Kelly, & Rothman, 2010). In sum, different perspectives on two decision problems with formally equal outcomes have a powerful, persistent effect, which can be taken advantage of when disseminating persuasive health messages (Meyerowitz & Chaiken, 1987).
Translated to the context of this study, it is expected that emphasizing the negative consequences of non-compliance with the recommended behavior (losses-frame) will result in higher levels of perceived risk, than focusing on the positive outcomes of compliance (gains-frame).
H1: Losses-framed messages elicit higher risk perceptions than gains- framed
messages.
1
The Asian Disease experiment e mp loys a hypothetical scenario where partic ipants decide between two alternative progra ms to combat the outbreak of a virus, which is e xpected to kill 600 people. In the gains scenario, participants have the choice between saving 200 people (progra m A) or a one-third chance to save 600 people (progra m B). Most participants opt for progra m A, thus picking the certain option over the risky option. In the loss scenario, participants decide again between two progra ms. Progra m C re sults in the certain death of 400 people, whereas progra m D results in a one-third chance that no one will die. In this decision scenario, participants reverse their init ial preference and pic k the risky option (progra m D) over the sure loss (program C).
The Moderating Role of Prior Knowledge
Another contextual variable affecting individual risk judgment is risk familiarity (Jin & Han, 2014; Song & Schwarz, 2009). Risk familiarity is typically composed of personal experiences with and prior knowledge about a risk (Alba & Hutchinson, 1987). The present study only focuses on the latter factor, because communicated information can only change the public’s prior knowledge about, but not its personal experiences with, the disease.
People prefer certainty of information about future outcomes over uncertainty (Glik, 2007; Huurne & Gutteling, 2008). The outbreak of an unknown disease makes the public aware that there is a gap between its current knowledge and des ired knowledge (Kahlor, 2010). To reduce this ambiguity, individuals seek information about the risk (Huurne
& Gutteling, 2008). Health communication scholars argue that at this stage individuals prefer any answer over uncertainty (Gesser-Edelsburg & Shir-Raz, 2015). As a result, the public engages with PHOs and news media communication about the crisis, exposing themselves to framed information. People unfamiliar with the hazard are left with no other option but to ‘believe’ the health communicator’s narrative and to accept the promoted problem definition (Griffin, Dunwoody, & Neuwirth, 1999).
This means that the gains- or losses-frame advocated by the initial communicator primes the individual’s belief about the public health crisis. This acquired belief about the crisis, stored in the individual’s memory, will be accessed whenever the individual comes into contact with the specific issue (Tversky & Kahneman, 1973). In contrast, if individuals already have a preconceived notion about an issue, they will compare their current stock of knowledge against the proposed information from the message communicator. Put differently: Individuals, who already labeled a crisis event as extremely dreadful (because they read news stories explaining the horrific symptoms), are less likely to be persuaded by a message which argues that the disease is not as threatening as it seems. Thus, PHOs must already disseminate
health information at an early stage, where little reliable information is publicly available (Glik, 2007; Mullin, 2003). Timely crisis communication greatly increases the likelihood that the public will accept the PHO’s crisis definition and adjust its risk perception accordingly. In sum, the present study hypothesizes that the framing effect on risk perception is contingent on prior knowledge about the issue.
H2: Prior knowledge moderates the relationship between gains- and losses- framing
and risk perception: The effect of message framing on risk perception is stronger for individuals with low prior knowledge as compared to those with high prior
knowledge.
The Mediating Role of Emotions
Traditionally, how individuals process risk information was conceptualized as a purely cognitive process, which meant that the role of affect was largely ignored (Waters, 2008).2 However, recently, crisis communication researchers have called for a n emotion-based perspective to understanding how individuals deduct meaning from message content, which subsequently affects their crisis interpretation (Jin, 2010; Jin, Pang, & Cameron, 2007).
According to the Extended Parallel Processing Model (Witte, 2009), there are two different pathways for individuals to process information: The analytic system is utilized to assess the risk consciously, calculating a cost-benefit analysis and consequently making an objective evaluation based on facts and statistics. In contrast, the experiential system works unconsciously. It is dominated by images in the mind and feelings, and requires less effort to assess the threat (Choi & Lin, 2007). Both systems work in tandem to guide message
processing and contextualization of the new information (Epstein, 1994). Thus, individuals always employ emotions to some extent when extracting meaningful knowledge from a message.
2
In line with Choi and Lin (2007) the terms feelings, affect and emotions are used interchangeably. Emotions are defined as psychological responses to objects and or situations (Fredrickson, Tugade, Waugh, & La rkin, 2003).
Crisis communication practitioners can induce emotions via frames, which are
conducive to accepting the underlying message appeal. For example, framing the severity of a disease in frequentist terms (‘200 out of 1000 people will die’) instead of probabilistic terms (‘20% will die’), produces affect- laden, distressing images, eliciting higher risk perceptions (Slovic, Finucane, & MacGregor, 2013). This emotional arousal can also be achieved by employing gains-and losses- framing: Just replacing the word ‘die’ with ‘live’ generates positive feelings, even though both frames are formally equal in terms of information
conveyed. Consequently, risk perceptions are influenced not only by “what we think about an activity but also [how] we feel about it” (Dunlop, Wakefield, & Kashima, 2008, p. 58). In line with previous research (Xie et al., 2011), emotions are modeled as mediating the relationship between gains-and losses-framing and risk perception.
Losses-frame d Messages, Negative Emotions and Risk Perception
The link between negative emotions evoked and risk perception is well-established in the fear appeal literature (Dillard, 1994; Witte, 2009). Visschers et al. ( 2012) define fear appeal as a “fear-arousing message that intends to evoke an unpleasant state among receivers about the negative consequences of their behavior” (p. 260). The negative emotional arousal serves as a motivator to comply with the advocated health behavior, aimed at eliminating the unpleasant state. This means that conceptually, fear appeals are equivalent to loss- framed messages. Both paint a ‘vivid picture’ of the risk by emphasizing negative over positive outcomes in order to persuade the message recipient (Meyerowitz & Chaiken, 1987; van't Riet et al., 2016). Tannenbaum et al. (2015) reviewed 127 articles about fear appeals and asserted that messages inducing fright score high on severity and susceptibility and positively
influence risk perception. Furthermore, Cheung and Mikels (2011) contend that there seems to be “a dynamic relationship between affect and risk judgments” (p. 852), with positive affect toward an outcome corresponding with lower risk perception and negative affect
toward an outcome leading to higher risk perception.
Transcribed to the purpose of the present research, losses-framed messages function as fear appeals, which elicit negative emotions such as fear or anxiety. Furthermore, the framed health issue, which is now associated with an unpleasant emotional state, will evoke higher risk perceptions.
H3a: Negative emotions mediate the effect of gains- and losses-framed messages on
risk perception: Losses-framed messages elicit more negative emotions than gains-framed messages, resulting in higher risk perception.
Gains-framed Messages, Positive Emotions and Risk Perce ption
However, the experience of negative emotions when confronted with risk information is evitable (Xie et al., 2011). This study will also focus on positive emotions experienced during a public health crisis, such as hope or relief. A study by Choi and Lin (2009) found that parents experienced relief when they were informed that their children did not own a harmful toy, affected by a product recall. Liu and K im (2011) discovered that in the context of the H1N1 flu outbreak, organizations communicated the emotion of relief to the public in order to reduce public anxiety. Finally, Johnson (2016) argued, in his study about public risk perception during the Ebola outbreak in 2014, that when dealing with a high mortality
disease, it is best to focus on survival optimism to reduce the risk of panic. He suggested that emphasizing survival over human loss positively influences emotions such as hope, thus avoiding an anxious, frightened public.
These studies maintain that the public experiences positive emotions during a crisis and that organizations attempt to lower the risk perception of the public by emphasizing positive outcomes. The present study hypothesizes that gains- framed messages function as ‘hope appeals’ in the same way losses-framed messages function as fear appeals.
H3b: Positive emotions mediate the effect of gains- and losses- framed messages on
risk perception: Gains- framed messages elicit more positive emotions than losses-framed messages, resulting in lower risk perception.
Prior Knowledge as Moderating the Mediation
According to the Heuristic-Systematic Model (Chaiken, 1987), individuals with low prior knowledge are more likely to process message content through the experiential system, consequently eliciting emotional responses (Averbeck et al., 2011). This seems intuitive, because if people do not have any preconceived notions about a health issue, there is no schema to cognitively assess the health threat. Instead, they are forced to process the message content emotionally. Thus, individuals who are left with no alternative but to judge the message by “how it makes them feel” (Dunlop et al., 2008), become very susceptible to emotionally-charged communication (Lewan & Stotland, 1961). In fact, Jin and Han (2014) found that the framing effect on risk perception was larger for individuals with low
knowledge about a food safety issue, than for individuals with high knowledge. Averbeck, Jones and Robertson (2011) manipulated the amount of information students received about two topics (sleep deprivation and spinal meningitis). They found that participants in the low prior knowledge condition processed the fear appeal message heuristically. As predicted by the Heuristic-Systematic Model, college students with low issue related knowledge
experienced greater fear and rated their personal risk higher, than participants who were well-informed about the respective health issues.
Summarized, these experimental findings support the proposition that the indirect effect of frames on risk perception through emotions varies with the amount of information the individual holds concerning the health issue.
H4a: Individuals with low prior knowledge, as compared to those with high prior
knowledge, experience more negative emotion, as a result of exposure to losses rather than gains- framed messages, resulting in higher risk perception.
H4b: Individuals with low prior knowledge, as compared to those with high prior
knowledge, experience more positive emotion, as a result of exposure to gains rather than losses-framed messages, resulting in lower risk perception.
The moderated mediation model investigating the effect of gains- and losses- framed messages about an influenza outbreak on individual’s risk perception through positive and negative emotions, moderated by prior knowledge, is graphically depicted in Figure 1.
Figure 1. Moderated mediation model.
Method
To test the hypotheses outlined above, an online experiment using vignettes to simulate the outbreak of an unknown influenza-type disease, called the ISAR-Virus, was
conducted. Prior to the final online experiment, a pilot study (N= 30) was conducted to ascertain that the stimuli functioned as intended: An independent-samples t-test shows that participants exposed to the high prior knowledge condition (M = 2.89, SD = 0.88) knew significantly more about the disease than participants in the low prior knowledge condition (M = 4.31, SD = 0.82), t (28) = -3.38, p = .004. However, the risk perception of individuals reading the gains- framed message (M = 4.23, SD = 1.07) did not yet differ significantly compared to participants exposed to the losses- framed message (M = 5.11, SD = 1.07), t (28) = 1.67, p = .115. Based on this analysis, the press-releases were modified (e.g., factual information made bold) to make the framed information more salient in the final online experiment.
Participants
The data were collected from the 15th of May until the 1st of June using the research software Qualtrics. Participants were recruited via survey sharing platforms, social media and email. Participants, who provided their e- mail addresses, entered a raffle with the chance to win one of three Amazon gift cards worth 10 Euros each. This resulted in a non-representative convenience sample of 165 respondents. 58.5% was female and the mean age was 28 years (SD = 9.47). Most participants had a Master’s degree (49.1%) and 46.1% of respondents were German.
Experime ntal Design
The experimental design was a two (prior knowledge : low vs. high) x two (frames: gains vs. losses) between-subject factorial design. Participants were randomly assigned to one of the four experimental conditions.
Procedure and Manipulation
Individuals participated in the experiment by clicking the URL link provided by the researcher. First, participants received general information about the online experiment.
Before they were shown the first vignette, participants were asked questions about their health-specific risk attitudes. To control for attitudes toward the health message
communicator, participants were also asked how they perceived the WHO.
Afterwards, participants were exposed to the first stimulus, which manipulated their amount of prior knowledge about the mock- up disease.3 Each participant read one factsheet. Participants were instructed to imagine they came across the presented message in real life and to assume the information provided to be factual. The factsheet briefed participants about the risk the ISAR-Virus posed to their health and contained information about transmission, case-fatality rate, incubation time of the virus and population at risk, symptoms, vaccination possibilities, and recommended protective measures (see Appendix A). Both messages contained equivalent information, albeit the high prior knowledge group received
significantly more details (115 words compared to 277 words). For example, the low prior knowledge condition was only informed that the virus spreads to other humans via direct contact, whereas the high prior knowledge condition was additionally informed that the virus spreads via material and surfaces contaminated with body fluids of infected people.
Consequently, participants indicated how they would rate their knowledge of the ISAR-Virus (manipulation check 1).
After this, respondents were exposed to the second stimulus: A press release by the WHO informing the public about the outbreak of the ISAR-Virus in Europe, either framed in terms of gains or losses (see Appendix B).4 Each participant only read one press-release. Again, participants were briefed to imagine reading this message in real life and the
information to be factual. The message clearly indicated that the WHO was the sender. Each message consisted of one headline and four subsections. The gains-framed headline read “WHO advises to comply with preventive measures to avoid infection with the ISAR-Virus in
3
Prior knowledge was coded as dummy variable (lo w prior knowledge = 0, h igh prior knowledge = 1).
Europe,” while the losses-framed headline stated, “WHO warns that failing to comply with preventive measures could result in infection with the ISAR-Virus in Europe.” The press release provided information about the recent outbreak in Southeast Asia, transmission of the virus to Europe, protective measures, and vaccination possibilities. To give an example of factual information being manipulated: The gains- framed message about vaccination
possibilities communicated that one vaccine undergoing human trials “immunizes 800 out of 1000 people,” as opposed to the losses-framed message, which contended that the vaccine undergoing human safety testing “fails to immunize 200 out of 1000 people.” Both messages are equivalent in factual information and approximately similar in length (450 words in the gains-frame and 478 words in the losses- frame). Subsequently, respondents encountered the second manipulation check and indicated if the message focused on positive outcomes or negative outcomes.
Following, participants responded to a battery of questions about the emotions they felt while reading the message and how risky they perceived the ISAR-Virus. Next,
respondents answered demographic questions regarding their gender, age, education and nationality. Finally, they were debriefed and the experiment ended.
Measure ment of Key Variables
The bivariate correlations, descriptives (means and standard deviations) and reliability tests for all variables subsequently used are presented in Appendix C. Unless otherwise noted, the response categories are always 7-point Likert-scales, ranging from strongly disagree (=1) to strongly agree (=7). The results of the factor analyses for emotions can be found in
Appendix D. New scales were computed by taking the arithmetic mean of all respective items.
Negative emotion. The negative emotion scale used in this study consisted of fear and
and anxiety were operationalized using Izard’s (1993) Differential Emotions Scale (Fredrickson, Tugade, Waugh, & Larkin, 2003) and adapted to the context of the present research. Participants indicated whether the crisis message made them feel “afraid, scared, fearful,” measuring fear (e.g., “The crisis message made me feel afraid”) and “nervous, anxious, worried,” measuring anxiety (e.g., “The crisis message made me feel nervous”). Reliability of the six items was good (Cronbach’s α = .94), and they were combined into a single negative emotion scale (M = 4.20, SD = 1.21).
Positive emotion. The scale measuring positive emotion experienced in times of crisis
consisted of hope and relief. The positive emotions hope and relief were selected from Izard’s (1993) Differential Emotions Scale (Fredrickson et al., 2003) and adapted to the context of this research. Hope was measured with two items: “The crisis message made me feel… (1) hopeful; (2) optimistic.” Relief was measured using two items: “The crisis message made me feel… (3) calm; (4) secure.” Cronbach’s α = .83 indicated satisfactory reliability for the four items, thus they were averaged, and a new positive emotions scale was computed ( M = 3.62, SD = 1.08).
Risk perception. Risk perception was operationalized following Slovic’s risk
characteristics (1987): “Risky, fatal, catastrophic, uncontrollable and dreadful.” Participants answered items such as “I perceive the ISAR-Virus outbreak as catastrophic.” The
measurement was reliable (Cronbach’s α = .87) and all five items were combined into one risk perception variable (M = 4.39, SD = 1.19).
In addition to demographic variables, two potential extraneous variables were controlled for: Participant’s health specific risk attitudes and their perception of the WHO (see Appendix C).
Manipulation Checks
condition (N = 83, M = 3.23, SD = 1.00) had a significantly lower amount of prior knowledge about the ISAR-Virus than participants in the high prior knowledge condition (N = 82, M = 4.29, SD = .80); t (156.09) = -7.55, p < .001.5 It should be noted that the assumption of equal variances in the population was violated, Levene’s F = 4.17, p = .043. The mean difference of -1.06 between both groups was large (Cohen’s d = 1.17) (Cohen, 1988).
An independent samples t-test revealed that participants exposed to the gains-framed message (N = 80, M = 5.28, SD = 1.14) perceived the message as significantly more positive-outcome- focused than participants reading the losses- framed message (N = 85, M = 3.59, SD = 1.42), t (159.05) = -8.46, p < .001. However, it should be noted that the assumption of equal variances in the population was violated, Levene’s F = 7.29, p = .008. The difference in means of -1.69 between both conditions, as indicated by Cohen’s d = 1.31, was large (Cohen, 1988).
Plan of Analysis
The present study constructs two models. The basic model 1 (see Appendix E) examines if there is a significant direct effect of gains- and losses- frame on risk perception (H1), and if prior knowledge moderates this relationship (H2). The extended model 2 (see
Figure 1 and Appendix F) extends the first model by including the mediators positive and negative emotions, resulting in a moderated mediation analysis. The extended model tests if there is an indirect effect of gains- and losses- framing on risk perception through negative (H3a) and positive emotions (H3b) and if prior knowledge moderates the paths from gains- and
losses- framing to positive and negative emotions (H4a, H4b).
All tests were conducted at α = .05. Both regression models controlled for age, gender, health-risk attitudes and trust in the WHO. The basic model was tested using the PROCESS
5
The prior knowledge scale employed in this study is adapted fro m Nabi (2003) and contains six items. Examp le statements are, “I have a sufficient amount of info rmation about the ISAR-Virus” and, “I don’t know as much as I’d like to know about the ISAR-Virus” (reverse coded). Cronbach’s α = .82 was reliab le, therefore all six items were co mputed into one single scale (M = 3.76, SD = 1.05).
macro Model 1 for SPSS Version 22 (IBM Corp., 2013) (Model 8 for the extended model) with 5000 bootstrapping samples (Hayes, 2013). Prior to the moderated mediation analysis, the values of gains-and losses- frame, high and low prior knowledge and negative and positive emotions were mean-centered. For the regression analysis, the F-value, the unstandardized b-value for the predictors, the corresponding t-and p-b-value and bias-corrected 95% CIs and R2 will be reported.
Results Hypotheses Testing
Model 1 (see Appendix E), consisting of gains- and losses-frame, prior knowledge and the interaction term of frame and prior knowledge, significantly predicts risk perception, F (7, 157) = 4.31, p < .001 (see Table 1). Model 1 explains 14.78% variance in risk perception (R2 = .15). H1, which states that losses- framed messages elicit higher risk perceptions than
gains-framed messages, is supported (direct effect), b = -0.80, t (157) = -4.48, p < .001, 95% CI [-1.15, -0.44]. On average, the risk perception of individuals exposed to the losses- framed message increases by .80 scale points compared to individuals exposed to the gains- framed message. However, H2, proposing that prior knowledge moderates the relationship between
gains-and losses- frame and risk perception, is rejected, b = 0.12, t (157) = 0.34, p = .737, 95% CI [-0.59, 0.84]. The effect of message appeal on risk perception is not significantly
moderated by prior knowledge. Also, there is no significant direct effect of gains- and losses-framing on risk perception (see Table 1).
Model 2 (see Figure 1 and Appendix F), predicting risk perception with gains- and losses- framing, prior knowledge, positive and negative emotions and the interaction terms of frame and prior knowledge, is significant, F (9, 155) = 13.18, p < .001 (see Table 2). The extended model explains 43.70 % of variance in risk perception (R2 = .44), which is substantially more than the basic model 1 (ΔR2 = .29), indicating a better fit.
Table 1.
Comparing regression models 1 and 2 predicting risk perception.
Risk perception
Model 1 (basic model) Model 2 (extended model)
Independent variables Coeff. SE p Coeff. SE p
Constant 4.59 0.66 < .001 3.41 0.65 < .001
Gains-and losses- frame -0.80 0.18 < .001 -0.18 0.16 .268
Prior knowledge -0.21 0.18 .244 -0.09 0.15 .563 Frame*prior knowledge 0.12 0.36 .737 -0.01 0.31 .962 Positive emotion - - - -0.23 0.09 < .001 Negative emotion - - - 0.49 0.08 < .001 Age -0.01 0.01 .417 0.01 0.01 .466 Health-risk attitudes -0.13 0.09 .159 -0.01 0.07 .902 Gender 0.19 0.19 .320 0.06 0.15 .695 Trust in WHO -0.02 0.08 .841 -0.08 0.07 .280 R2 = .15 R2 = .44 F(7, 157) = 4.31, p < .001 F(9, 155) = 13.18, p < .001
Table 2 shows that there is a significant framing effect on positive emotion (b = 0.94, t (157) = 6.29, p < .001, 95% CI [0.65, 1.24]) and negative emotion (b = -0.80, t (157) = -4.45, p < .001, 95% CI [-1.16, -0.45]). Next, negative emotion (b = 0.49, t (155) = 6.32, p < .001, 95% CI [0.34, 0.64]) and positive emotion (b = 0.23, t (155) = 2.62, p < .001, 95% CI [0.40, -0.06]) significantly predict risk perception. The total indirect effect (value = -0.40) of the losses- framed message on risk perception via negative emotions is significant, 95% CI [-0.63, -0.21] (H3a).6 The negative emotions elicited through the loss-framed messages increased risk
perception by .40 scale points. On the contrary, the significant total indirect effect (value = .19) of gains framed messages on risk perception via positive emotions (95% CI [0.42,
6
The significance of the indirect effect is determined by testing if the 95% CI includes 0. Hayes (2013) argues that interpreting the 95% CI is a better method to infer if the indirect effect is statistically significant than analyzing the Sobel z-test statistic.
0.07]) indicates that the positive emotions evoked via gains- framed messages reduced risk perception by .19 units (H3b). Thus, H3a and H3b are supported. Interestingly, with the
addition of positive and negative emotions to the model, the direct effect of gains- and losses-framing on risk perception becomes non-significant, b = -0.18, t (155) = -1.11, p = .268, 95% CI [-0.51, 0.14], indicating a full mediation of the framing effect on risk perception through positive and negative emotions (see Table 2).
H4a and H4b propose that prior knowledge moderates the relationship between gains-
and losses- framing and positive and negative emotions, and thus influences risk perception. Based on the results presented in Table 3 H4a is rejected, because the 95% CI [-0.35, 0.35] of
the conditional indirect effect includes 0. Prior knowledge does not moderate the indirect effect of gains- and losses- frame on risk perception via negative emotions. However, the 95% CI [0.01, 0.36] for the conditional indirect effect (value = .14) of framing through positive emotion on risk perception does not include 0 (see Table 3), indicating a marginally
significant moderated mediation (Hayes, 2015). Figure 2 suggests that the indirect effect of gains-framing compared to losses-framing on risk perceptions through positive emotions seems to increase from low to high prior knowledge. This effect corresponds to the
expectations formulated in H4b, which predicted that individuals with low prior knowledge,
experience more positive emotion, as a result of exposure to gains-framed messages rather than the loss- framed message, resulting in (0.14 units) lower risk perceptions, as compared to those with high prior knowledge. Thus, H4b is supported.
Table 2.
Regression Coefficients, Standard Errors, and Model Summary Information for the Risk Perception Moderated Mediation Model Depicted in Figure 1.
Dependent variables
Positive emotion (mediator 1) Negative emotion (mediator 2) Risk perception (model 2)
Independent variables Coeff. SE p Coeff. SE p Coeff. SE p
Constant 3.51 0.65 < .001 4.01 0.69 < .001 3.41 0.65 < .001
Gains-and losses- frame 0.94 0.15 < .001 -0.80 0.18 < .001 -0.18 0.16 .268
Prior knowledge 0.11 0.15 .474 -0.20 0.18 .265 -0.09 0.15 .563 Frame*prior knowledge -0.59 0.30 .053 0.00 0.36 .995 -0.01 0.31 .962 Positive emotion - - - -0.23 0.09 < .001 Negative emotion - - - 0.49 0.08 < .001 Age -0.02 .01 .038 -0.00 0.01 .734 0.01 0.01 .466 Health-risk attitudes 0.22 0.09 .011 -0.15 0.09 .119 -0.01 0.07 .902 Gender 0.04 0.16 .802 0.28 0.20 .152 0.06 0.15 .695 Trust in WHO -0.04 0.08 .612 0.11 0.09 .212 -0.08 0.07 .280 R2 = .29 R2 = .17 R2 = .44 F(7, 157) = 8.49, p < .001 F(7, 157) = 4.56, p < .001 F(9, 155) = 13.18, p < .001
Table 3.
Results of Testing for Moderated Mediation. Mediator Index of moderated indirect effect SE 95% CI LL UL Positive emotions 0.14 0.09 0.01 0.36 Negative emotions 0.00 0.18 -0.35 0.35
Note: CI = confidence interval; LL = lower limit; UL = upper limit.
Figure 2. Conditional indirect effect of gains-and losses- frame on risk perception at values of the moderator prior knowledge through positive emotion.
-0.10 -0.05 -0.29 -0.15 -0.57 -0.32 -1 -0,9 -0,8 -0,7 -0,6 -0,5 -0,4 -0,3 -0,2 -0,1 0 C on d it ion al i n d ir ec t ef fe ct of ga in s-an d los se s-fr am e o n ri sk p er ce p ti on at v al u es of th e m od er at or p ri or k n ow le d ge t h rou gh p os it ive e m ot ion Prior knowledge 95% CI Upper Limit Conditiona l Indirect Effect 95% CI Lower Limit
Conclusion and Discussion
The objective of the present research was to attain a more nuanced understanding of how gains- and losses- framed messages about an influenza outbreak affect individuals’ risk perceptions. The study focused on the intermediary role of emotions, and conceptualized prior knowledge as a boundary condition, magnifying or limiting the framing effect.
The current investigation supported the proposition that gains- and losses- framing is a valuable method of changing people’s risk perception through positive and negative emotions elicited. The important role of emotions in crisis communication (Jin et al., 2007) is
demonstrated by the fact that emotions fully mediated the effect of gains-and losses- framed crisis messages on risk perception. Hence, this research presents compelling evidence that emotions serve as causal mechanism, which explicates how heuristically processing crisis-message content influences risk perception. Members of the public do not respond to factual information apathetically. Instead, their emotional state subtly guides their message
processing (Averbeck et al., 2011) and comprehension of the crisis situation. These findings substantiate that the emotion-based perspective in crisis communication offers valuable insights into the ‘black box’ of message appeals (Jin et al., 2007; Jin, 2010; Jin, Park, & Len-Rios, 2010; van der Meer, Verhoeven, Beentjes, & Vliegenthart, 2014).
The results obtained in this study are in line with the fear appeal literature (Dillard, 1994; Witte, 2009): Losses- framed messages about a public health crisis essentially function as fear appeals, which increase risk perception. Accordingly, losses-framed messages remain a compelling way of communicating about health hazards and represent a powerful tool for PHOs to persuade its audience to comply with recommended measures (Tannenbaum et al., 2015).
However, in crisis situations PHOs must also be able to reassure the public that the disease is contained or induce hopeful emotional states to avoid an anxious public (Glik,
2007; Johnson, 2016). Usually, the news media play the role of the doomsayer, reporting about negative events, such as disease outbreaks and other disasters (Galtung & Ruge, 1965; Harcup & O'Neill, 2001). Thus, there may be crisis phases, where PHOs need to counter-argue fear- inducing news reports and act as a ‘calming force.’ The present study indicates that gains-framed messages are an effective method to elicit positive emotions, consequently reducing risk perception. Furthermore, gains- framed messages also lower the intensity of negative emotions experienced in times of crisis. Previous studies imply that, after a certain point, ever- increasing levels of fear may continue to induce higher risk perceptions, but not higher compliance intentions (Hoog, Stroebe, & Wit, 2007; Ruiter, Kessels, Peters, & Kok, 2014). As a result, extreme fear appeals fail to provide further motivation for the public to act in accord with recommended behavior. Instead, individuals exposed to an existential threat try to cope with the problem by simply ignoring it, as argued by persuasive argument theory (Burnstein & Vinokur, 1975). This type of behavioral response is clearly not conducive to adopting preventive measures as advocated by PHOs. Thus, gains- framed messages offer an alternative style of communicating with the public in times of crisis, taking into account the PHO’s role as a ‘calming force’ and the limited usefulness of fear appeals.
No evidence was found for the moderating role of prior knowledge for the frame-risk perception-relationship (conditional direct effect). It seems that during a public health crisis, individuals are emotionally sensitive (van der Meer & Verhoeven, 2014). Therefore, the information people gathered from reading the press-release about the disease outbreak was processed primarily through emotions (experiential system), rather than cognitively, using prior knowledge (Choi & Lin, 2007). This observation points toward the conclusion that times of crisis are equated with danger, which automatically activate the evolutionary, built- in affective system. The affective system quickly appraises the threat and guides the subsequent behavioral response (Dillard, 1998).
Similarly, there was no significant interaction between frame and prior knowledge on negative emotions. When communicating about a dangerous disease, prior knowledge does not function as a barrier, inhibiting the effect of losses-framed message on risk perception. Expressed differently, this finding indicates that the slightest bit of information about an imminent crisis is already sufficient to create an anxious public (Jin et al., 2007). Whether the public only has a rudimentary understanding of the health risk, or is highly familiar with the disease, has no measurable effect on the negative emotions experienced reading losses- framed messages. Yet, PHOs can choose to ‘take advantage’ of the negative emotional state using losses- framed messages aimed at presenting a health threat, which cannot be ignored without experiencing adverse consequences (Meyerowitz & Chaiken, 1987; van 't Riet et al., 2016). This type of communication is persuasive, independent of prior knowledge about the disease (Johnson, 1993; Slovic, 1993).
However, the present study did find a marginally significant conditional indirect effect of frames on risk perception through positive emotions. The gains-framed message, compared to the losses- framed message, elicited lower risk perception for individuals with low prior knowledge, compared to the high prior knowledge group, because the former group
experienced more positive emotions. One possible explanation for this result is the severity of the hypothetical disease employed in this study. The ISAR-Virus was depicted as extremely fatal (e.g., case fatality rate 50%), so individuals in the high knowledge condition already had an accurate understanding of the severity of the threat. This factual knowledge about the health threat probably trumped any positive effect of reduced uncertainty through high prior knowledge, eliciting less positive emotion. As a result, individuals with high prior knowledge - instead of feeling relieved to have at least some information about the crisis (Johnson, 2016) - appraised the threat as existential, rendering the gains- framed information aimed at
positive emotions suggests that the susceptibility to the framing effect is contingent on the individual’s familiarity with the health issue (Jin & Han, 2014; Rothman, Bartels, Wlaschin, & Salovey, 2006). Thus, there is reason to believe that individuals’ prior knowledge serves as a boundary condition for the effectiveness of PHOs’ gains-framed message appeals, aimed at evoking positive emotions.
Though the present study offers valuable insights into the realm of framing- and risk perception research, certain limitations should be discussed. To test the causal relationship of the variables of interest, a fictitious crisis scenario was constructed and the collected data are based on a convenience sample. These circumstances limit the generalizability of the findings to a larger population and still leave open the possibility that individuals react differently when faced with a real life crisis. Although participants were instructed to pretend the crisis was real, there is reason to argue that ‘artificial’ emotional arousal results in less intense feelings than exposure to the real threat.
A novel finding of this study is the presence of positive ly valenced emotions in the public’s affective repertoire during a crisis. The general tendency of emotion-based crisis communication research to focus on the influence of discrete emotions (e.g., hope, relief) (Jin, Liu, Anagondahalli, & Austin, 2014; Lerner & Keltner, 2000) instead of valenced emotions (e.g., positive) could not be matched, because crisis situations elicit almost exclusively discrete negative emotions (Jin, 2010). However, gains-framed crisis messages can elicit positive emotions; thus future research should continue examining which discrete positive emotions are elicited (Jin et al., 2014), and how crisis information needs to be specifically framed in order to elicit the desired discrete positive emotion.
Furthermore, other moderators and mediators should be examined to provide a more refined understanding of the mechanism behind the framing effect. For example, future research could explore if individuals’ self-efficacy attitudes mediate the effect between
message appeal and risk perception. Crisis communication scholars could probe whether communication medium (e.g. social media posts) or source of the information (e.g., government, N GOs) alter the strength of the gains- and losses-framed appeals on risk perception.
A public health crisis attracts a considerable amount of media attention because of its real- life implications for a broad audience (Liu & K im, 2011). As a result, there are a myriad of different frames, offering diverging crisis interpretations and solutions (Chong
& Druckman, 2007). To capture the ‘frame overload’ that today’s media-rich landscape generates, future research could expose participants to multiple framed messages (within-subject experimental design) and analyze how competing frames affect emotional states and risk perception.
Finally, this study made a first, fruitful attempt to introduce the concept of gains- and losses- framing to organizational crisis communication. Follow-up studies should be
conducted, which test if this type of framing also affects other dependent variables of interest, such as organizational reputation, while also varying the context of crisis communication (e.g., product-recall, natural disasters).
Despite these limitations, the present study asserts that a thorough understanding of the interplay of framing, prior knowledge and emotions is crucial to comprehending the ‘black box’ of communication during a crisis. The last word on the role of prior knowledge has not been spoken, but PHOs should certainly never neglect the affective state of their audience (Liu et al., 2011), because ‘emotions matter’ in times of crisis (K im & Cameron, 2011). After all, one well- worded press release acknowledging the crisis could make the difference between an informed and calm public, complying with recommended protective behavior, and a distressed and anxious population, paralyzed by fear.
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