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Risk Analysis, Vol. 31, No. 2, 2011 DOI: 10.1111/j.1539-6924.2010.01516.x

Interpersonal Amplification of Risk? Citizen Discussions

and Their Impact on Perceptions of Risks and Benefitsof a Biological Research Facility

Andrew R. Binder,1, Dietram A. Scheufele,2 Dominique Brossard,2

and Albert C. Gunther2

Much risk communication research has demonstrated how mass media can influence indi-vidual risk perceptions, but lacks a comprehensive conceptual understanding of another keychannel of communication: interpersonal discussion. Using the social amplification of risk asa theoretical framework, we consider the potential for discussions to function as amplifica-tion stations. We explore this possibility using data from a public opinion survey of residentsliving in potential locations for a new biological research facility in the United States. Con-trolling for a variety of key information variables, our results show that two dimensions ofdiscussionfrequency and valencehave impacts on residents perceptions of the facilitysbenefits and its risks. We also explore the possibility that an individuals overall attitude mod-erates the effect of discussion on their perceptions of risks and benefits. Our results demon-strate the potential for discussions to operate as amplifiers or attenuators of perceptions ofboth risks and benefits.

KEY WORDS: Interpersonal discussion; risk communication; social amplification of risk

1. INTRODUCTION

In spite of an abundance of empirical researchexploring how communication channels influence in-dividual perceptions of risk, our understanding ofmass media influences far outweighs our understand-ing of interpersonal influences. In fact, we know lit-tle about how discussions among citizens can impactperceptions directly or how they work in tandem with

other information and communication channels to

1 Department of Communication, North Carolina State Univer-sity, Raleigh, NC, USA.

2 Department of Life Sciences Communication, University ofWisconsinMadison, Madison, WI, USA.

Address correspondence to Andrew R. Binder, Department ofCommunication, North Carolina State University, Campus Box8104, Raleigh, NC 27695-8104, USA; tel: 919-513-2430; fax: 919-515-9456; [email protected].

shape various perceptual and cognitive outcomes. Inthis study, we focus on such influences as potentialamplification stations within the social amplificationof risk framework.

Originally, the framework was proposed to ad-dress why and how certain risks capture public at-tention and become either intensified (through anamplification process) or ignored (through an at-tenuation process).(1) The model concentrated on

two components of risk research: the difference be-tween technical definition and social experience ofrisk,(2) and the social mechanisms underlying thecommunication and reception of risk messages. In-formation about risks is transmitted through variousamplification stations, such as social groups, insti-tutions, media outlets, and, ultimately, the individ-ual.(3) Communication is therefore at the heart of thesocial amplification of risk, since people are exposed

324 0272-4332/11/0100-0324$22.00/1 C 2010 Society for Risk Analysis

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Interpersonal Amplification of Risk 325

to information about risks most commonly throughtheir use of mass media or through discussions withothers.

Similar to most of the risk communication litera-ture, interpersonal discussion has received relatively

little emphasis in research on the social amplificationof risk. A number of fields in the social sciences, suchas political communication, science communication,and social psychology, in contrast, have long researchtraditions dealing with the interpersonal antecedentsof perceptual and cognitive variables. This researchcan directly inform our thinking about the socialdynamics linking mass-mediated communication, in-terpersonal discussion, and perceptions of emergingtechnologies, and allow us to make concrete predic-tions about the role that interpersonal discussion canplay as an agent of risk amplification and attenuation.Our analyses rely on primary survey data from fivecommunities that were potential sites for a new bi-ological research facility, which would study deadlyanimal diseases such as hoof-and-mouth disease andAfrican swine fever. This context, which we describein greater detail below, proves ideal for exploring theinfluence of interpersonal discussion because citizensin all five communities were actively encouraged tovoice their opinions and take part in the decision-making process.

In conceptualizing talk as an amplification sta-tion, we emphasize two dimensions that may be par-ticularly relevant to evaluations of the risks and ben-

efits of this research facility, as well as how the directeffects of those dimensions may be moderated by anindividuals own attitude toward the facility.

1.1. Dimensions of Interpersonal Talk

The communication literature linking mass me-dia with interpersonal influences has focused on ahandful of key dimensions of interpersonal commu-nication. In order to situate these dimensions withinthe context of risk, it is also important to understandthe underlying functions of interpersonal discussion.

A useful typology provides at least five purposes: toinform and explain, to argue and persuade, to pro-vide emotional support, to tell stories, and to inter-pret information.(4) Much normative democratic the-orizing emphasizes the informational and persuasivefunctions and their relevance for public opinion anddecision making at the societal level.(58) Interper-sonal discussion has also been proposed as a key fac-tor in interpreting messages made salient through themass media,(9,10) particularly in the context of risk.(11)

While they are not the primary focus of this study,these functions underlie at least three dimensions oftalk that need to be distinguished when examiningsocial dynamics involving interpersonal communica-tion: content, frequency, and valence.

1.1.1. Content of Discussion

Scholars of public deliberation have long distin-guished conceptually between goal-directed, mutu-ally informed discussions and casual conversationswithout a particular topical focus(7,12) and arguedthat deliberative societal decision making dependson the former. This conceptual distinction and theparticular importance of issue-specific, goal-orienteddiscussion among citizens for informed decision mak-ing was confirmed empirically in subsequent re-search.(8) More recent research has also highlightedthe need to develop more granular distinctions of theparticular issues that citizens may discuss and howthe content-specific focus of discussions is critical forunderstanding their effects.(13)

1.1.2. Discussion Frequency

A number of existing studies in the risk com-munication literature also propose that the amountof exposure to various communication situationsin addition to the content of discussionscan havean impact on individual risk perceptions. In these

studies, time is treated as the crucial factor: com-munication is proposed to have an influence on at-titudes through the number of hours exposed tomedia or the frequency of talk. The conceptualiza-tion of communication channels as competing in-fluences on personal and/or societal-level risk judg-ments (e.g., the impersonal impact hypothesis)(14)

dominate this literature. As Lehmkuhl(15) argues,this focus may follow from much political commu-nication research in the same vein, including thetwo-step flow of communication(16) and the so-calledlimited-effects paradigm of political communication

research.(17)

The findings from this line of risk research arerather equivocal regarding the overall impact of dis-cussion frequency. In some cases, frequency of dis-cussion has a significant and more powerful impacton risk perceptions than do mass media, for bothpersonal and societal risks(18) or subdimensions ofpersonal risks.(11) In another study, interpersonal dis-cussion frequency mediated the influence of massmedia on similar outcomes.(19) In some analyses,

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326 Binder et al.

researchers operationalize frequency of discussionwith different discussants as separate variables,which makes it difficult to evaluate unique relation-ships between overall frequency of discussion andperception of risk. This is not to say that the type of

discussion partner is a negligible influence, but ratherthat it is important to distinguish the overall fre-quency of discussion from other dimensions of talk.

Our conceptualization of potential discussion ef-fects differs from these studies in two importantways. First, we are less concerned with the impact ofcompeting communication channels than we are withthe net impact of discussion frequency on judgmentsof both risks and benefits. Thus, our focus has morein common with recent studies examining discussioneffects on attitudes related to science and technologymore generally, where evidence suggests discussionfrequency can have a direct impact on various attitu-dinal outcomes.(13,20,21)

Second, the studies reviewed above examine therelative influence of discussion frequency on per-sonal versus societal-level health risks. This distinc-tion seems less useful for perceptions of a biologicalresearch facility, which are relevant on both levels.At the same time, studies examining public opiniontoward hazardous waste sites and research laborato-ries, a number of which also use the social amplifi-cation of risk as a theoretical framework,(22,23) havenot explored the possible role of interpersonal com-munication. Since, to our knowledge, there are no

extant studies evaluating the effect of frequency ofdiscussion on both risk and benefit perceptions, thenature of such a relationship (in terms of magnitudeand direction) remains relatively unclear. We there-fore pose the following research question:

RQ1: What is the relationship between an indi-viduals discussion frequency and his or her percep-tions of (a) risks and (b) benefits of a biological re-search facility?

1.1.3. Most Recent Discussion Valence

As reviewed above, many studies exploring theinterpersonal influence on risk judgments and per-ceptions have focused solely on the frequency of dis-cussion. Of course, knowing how often one discussesan issue tells us little about the substance of suchconversations. We have therefore chosen to examineanother important dimension of interpersonal discus-sion: the valence of an individuals most recent dis-cussion. That is, if an individual recently discussed arisk-related issue, to what degree was his or her dis-

cussant concerned about it and how does the valenceof that discussion color his or her evaluations of risksand benefits?

Discussion valence is likely to have a direct im-pact on perceptions of risks and benefits for two rea-

sons. First, as studies in political communication haveshown in a variety of contexts, it is relatively rare forpeople to find themselves in nonlikeminded discus-sion networks.(24) It follows that people who perceivea higher level of risk may be more likely a priori todiscuss an issue more often with others who also per-ceive a higher level of risk. The same relationshipmay hold true for perceived benefits.

Second, the valence of previous discussions hasreceived much attention in research on public opin-ion, and in particular spiral of silence theory.(25)

It is unclear from this research whether we shouldexpect valence to have a direct impact on evalua-tions of risks and benefits, although recent conver-sations can have a direct causal impact on percep-tions of the surrounding opinion climate,(26) whichmay in turn influence ones own perception of risksand benefits. Regardless of the underlying theoreti-cal mechanismeither self-selection into likemindeddiscussion groups or an influence of the climate ofopinionit seems likely for there to be some corre-spondence between ones most recent discussion andevaluations of risks and benefits. We formalize thisexpectation with a second research question:

RQ2: What is the relationship between the va-

lence of an individuals most recent discussion andhis or her perceptions of (a) risks and (b) benefits ofa biological research facility?

1.2. Issue-Specific Attitude as a Moderatorof Discussion Effects

We have focused thus far on intrinsic factorsof conversation, that is, content, frequency, and va-lence, and their potential direct effects on percep-tions of risks and benefits. However, in addition todrawing direct connections between these dimen-

sions of discussion and attitudinal or cognitive out-comes, the literature also suggests that it is possiblethat extrinsic factors, such as preexisting character-istics of discussion participants, may shape outcomesof discussion. In particular, an individuals overall at-titude toward an object could moderate the relation-ship between discussion and more specific evalua-tions of the attributes of that object, and the linearrelationship between both frequency and valence ofdiscussion and perceptions of risks and benefits may

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therefore differ among different subgroups in a pop-ulation. Much of the research cited above, in fact,explores the influence of interpersonal discussion ascontingent on a variety of variables in this way.(24,26)

Moreover, as we stated at the outset, the so-

cial amplification of risk suggests dynamic processeswhereby individual judgments of risk have subse-quent impacts originating at the microlevel, continu-ing through the meso- and macrolevels, resulting insecondary and tertiary ripple effects.(27) We there-fore address several reasons why we may reason-ably expect interpersonal discussion to function asan amplification station and influence such dynamicprocesses.

1.2.1. Discussion and Persuasive Argumentation

A large body of research in social psychology has

explored small-group decision-making scenarios thatfeature two of the main functions of interpersonaldiscussion: explanation and persuasion. This researchhas focused mainly on explaining the phenomenonof group polarizationwhere a small group comesto a decision that is more extreme than the averageinitial position of its members(28)in terms of indi-vidual thought(29) and repeated expressions.(30) Thus,the resulting shift in the average attitude of the groupcan be explained, in part, as a function of individ-uals engaging in discussion about a topic, which in-creases the number of times they think about and ex-

press their attitude toward an object, which, in turn,strengthens their initial position.

In the context of risk amplification and atten-uation, such a process suggests that engaging morefrequently in discussions about a topic will reinforceones own initial attitude (of support or opposition)and have an impact on perceptions of both risks andbenefits. We formalize this expected moderation ef-fect with the following hypothesis:

H1: An individuals overall attitude toward a biolog-

ical research facility moderates the relationship

between frequency of discussion and perception

of the facilitys (a) risks and (b) benefits.

1.2.2. Discussion Congruency and the Potential

for Disagreement

While the valence of discussion might be di-rectly related to evaluations of risks and benefits, it isalso possible that this relationship differs dependingon how that valence corresponds to the individualsoverall attitude. In exploring this possibility, we fol-

low a long line of research in political communicationand a rare example of risk research(15) emphasizing asecond dimension: congruency of discussion, or thedegree to which discussants agree or disagree withone another.

In political communication, congruency hasmainly been explored in terms of its effects on learn-ing. Discussion can foster information-seeking(31)

and elaboration,(32) although disagreement withinones discussion network may hamper knowledgegain.(33) Regarding political attitudes, research sug-gests heterogeneous discussion networks can resultin ambivalent attitudes(34,35) while homogeneous dis-cussion may polarize attitudes.(21) The theme emerg-ing from all of these studies is that the presenceor absence of disagreement with discussion partnerscan differentially influence cognitive complexity andthought regarding a given topic. Similarly, we mightexpect an individuals own attitude to interact withthe tenor of his or her more recent discussion (i.e.,congruency or incongruency), resulting in differencesin perceptions of risks and benefits. For example, asupporter discussing an issue with another supportermay perceive less risk than if they discussed the sameissue with an opponent. With this reasoning, we pro-pose a second hypothesis:

H2: An individuals overall attitude toward a biolog-

ical research facility moderates the relationship

between the congruency of that individuals last

discussion and his or her perception of the facil-itys (1) risks and (2) benefits.

1.3. Context of Inquiry: The National Bio- andAgro-Defense Facility

As we mentioned briefly above, this study is partof a larger project focusing on public opinion andmedia coverage surrounding the site-selection pro-cess by the U.S. Department of Homeland Secu-rity for the National Bio- and Agro-Defense Facility(NBAF). The facility is intended to be a state-of-the

art biosafety level 4 facility that will conduct researchon highly contagious foreign animal pathogens.(36)

Five communities were short-listed as sites for hous-ing the facility in diverse locations throughout theUnited States: Athens, Georgia; Butner, North Car-olina; Flora, Mississippi; Manhattan, Kansas; and SanAntonio, Texas. The process was the subject of in-tense debate, and elected officials and policymak-ers encouraged citizens to attend numerous townhall meetings and debate the merits of hosting the

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328 Binder et al.

facility in their community. The site-selection processfor the NBAF therefore provided an ideal context forexploring the role of interpersonal discussion in thesocial amplification of risk framework.

2. METHODS

2.1. Data

Public opinion surveys were carried out bythe University of Wisconsin Survey Center be-tween November 2008 and January 2009 accord-ing to the tailored design method.(37) An identi-cal mail questionnaireadjusted for location-specificwordingwas sent to a probability sample of peopleresiding within 25 miles from each of the five pro-posed sites for the NBAF.3 Response rates, basedon the American Association for Public Opinion Re-

search (AAPOR) Standard Definition RR1,(38)

var-ied by community but were in an acceptable range fora mail survey with the general public: Athens, Geor-gia (28.5%); Butner, North Carolina (28.7%); Flora,Mississippi (26.0%); Manhattan, Kansas (34.4%);and San Antonio, Texas (25.1%). For our analysesthe separate samples were pooled (N = 1,716).4

2.2. Measures

First, to control for potential between-communities differences in our dependent variables,dummy variables were created for four of the five

communities: Athens (20.4%), Butner (20.6%),Flora (18.3%), and Manhattan (24.1%). San Anto-nio served as the excluded category in our regressionanalyses.

We measured respondents age (M = 51.57,SD = 16.47) by asking them for their year of birth andsubtracting it from 2008, the year the survey was con-ducted. Respondents also indicated their sex (40.6%female). Educational attainment was measured with

3 A total of 1,400 individuals were contacted in each of the com-munities. Names and addresses were purchased from GENESYSSampling Systems, a subsidiary of Marketing Systems Group. Toencourage responses, a $2 bill was included as an incentive in theinitial contact mailing.

4 We report AAPOR Response Rate 1 because the address-basedsampling procedure (vs. a named sampling procedure) we em-ployed essentially makes ours a random survey, which is notspecifically discussed in the AAPOR standards. With this design,it is difficult, if not impossible, for us to define eligible versus in-eligible households, which would be required to report more ro-bust, less restrictive response rates. We therefore report RR1,with the caveat that it is very likely the most conservative esti-mate of responses to our survey.

the question: What is the highest level of educa-tion you have completed? (1 = Some high school,5 = Completed a graduate or professional degree;Mdn = 4, Completed four-year college with a bach-elors degree). We measured household income with

the question: Please estimate your total 2007 house-hold income before taxes, combining income from allhousehold members, from all sources (1 = $20,000or less, 6 = $100,001 or more; Mdn = 3, $40,001 $60,000). These two indicators (r= 0.45, p < 0.001)were standardized and averaged into a two-item in-dex ofsocioeconomic status.5

Predispositions were each constructed as multi-ple item indices. Religiosity (M = 3.61, SD = 1.28;r = 0.71, p < 0.001) was measured with two ques-tions: (1) How much guidance does religion giveyou in your everyday life? (1 = No guidance atall, 5 = A lot of guidance) and (2) How of-ten do you attend religious services? (1 = Everyweek, 5 = Never; reverse-coded). Political ideol-ogy (M = 3.26, SD = 0.98; r = 0.62, p < 0.001) wasalso measured with two questions: (1) In terms ofeconomic issues, would you say you are . . . and Interms of social issues, would you say you are . . .(1 = Very liberal, 5 = Very conservative).

Respondents attention to news media was oper-ationalized as separate variables along two contentdistinctions. The first focused on a four-item indexof overall attention to public-affairs news (M = 3.62,SD = 0.82; Cronbachs = 0.80), within two content

domains (i.e., national government & politics andlocal affairs & politics) and two types of media (i.e.,on television and in newspapers, either in printor online). All four items were measured on a five-point scale (1 = None, 5 = A lot). We also eval-uated respondents specific attention to NBAF news(M = 2.32, SD = 1.10; r = 0.70, p

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average of the two (M = 3.47, SD = 0.84; r = 0.61,p < 0.001). Two questions tapped dimensions ofinterpersonal discussion about the NBAF. The firstindicator focused on issue-specific NBAF discussionfrequency: How often do you have discussions with

other people about the National Bio- and Agro-Defense Facility (1 = Never, 5 = Very often;M= 1.79, SD = 0.86). The second focused on NBAFdiscussion valence: If you have talked about theNBAF with other people, was your last discussionwith . . . . Responses were in one of three categories:Someone who was in favorof the facility (23.2%),Someone who was opposed to the facility (16.9%),and I have not discussed the facility with anyone(59.8%). For the analysis, this valence variable wasrecoded on a three-point ordinal scale with values1 (discussion with opponent), 0 (no discussion),and 1 (discussion with supporter).

To test our moderation hypotheses, we also in-cluded an indicator of overall support for NBAF:Different people have different opinions when itcomes to the new facility. How about you? Doyou favor or oppose the new facility? (M = 3.39,SD = 1.05). Responses were measured on a five-point scale (1 = Strongly oppose, 5 = Stronglysupport).

Our two dependent variables tapped respon-dents evaluations of different dimensions of the fa-cility. Perception of risks (M = 2.87, SD = 0.89)was measured with four items: (1) The NBAF will

have a negative impact on the safety of my commu-nitys water supply, (2) Because of the type of re-search going on at the facility, my community maybe the target of a terrorist attack, (3) The facil-ity will increase the risk of environmental contami-nation in this community, and (4) People in thiscommunity may have a higher risk of contractingdangerous diseases because of the NBAF. The fouritems, which demonstrated high reliability (Cron-bachs = 0.86), were averaged to form the finalvariable.

Perception of benefits (M= 3.50, SD = 0.77) was

also measured with four items: (1) The NBAF willprovide more jobs for my community, (2) If the fa-cility is built in this community, it will have a pos-itive impact on our economy, (3) The construc-tion of the NBAF will have positive effects on theinfrastructure of my community (such as road condi-tions), and (4) The research conducted at the fa-cility will benefit local farmers. Based on their re-liability (Cronbachs = 0.83), the four items wereaveraged into a single variable.

2.3. Analytic Framework

Our analyses were conducted using hierarchicalordinary least-squares (OLS) regression. The analy-sis proceeded in two steps. First, each block of vari-ables was entered into the equation according totheir assumed causal order (e.g., first the block ofdummy variables controlling for community differ-ences, then demographics and predispositions, etc.).The final block contains a single variable (risk orbenefit perception), which controls for the interre-lationships between perceptions of risks and bene-fits. The block-by-block approach allows us to eval-uate the variance explained by each set of variablesas they are entered as predictors. The before-entrystandardized betas allow us to evaluate the main ef-fects of each variable controlling for those alreadyentered into the equation,(39) and are especially use-

ful for evaluating the independent effects of variablesthat may be highly correlated with one another (inthis case, for example, the effects of public-affairs vs.issue-specific news attention and discussion).

Second, because our hypotheses focus on theinteractive effects of two dimensions of interper-sonal discussion and respondents own attitude to-ward the facility, we tested two two-way interactionterms. These multiplicative terms were calculated af-ter standardizing the original variables, a transforma-tion that helps reduce the impact of multicollinearityin estimating the interaction effect.(40,41) Each inter-

action term was entered separately in the final blockof the regression equation.

3. RESULTS

Prior to reporting the results of our hypothesistests, we first provide an overview of the main effectsof several control variables. All main-effect regres-sion estimates are reported in Table I.

3.1. Main Effects

In the final equation, only one of the demo-graphic and predispositional variables was a signifi-cant predictor of either risks or benefits: higher levelsof socioeconomic status were associated with slightlyhigher perceptions of benefits. Overall, demographicvariables explained more variance than predisposi-tions. Issue-specific attention to news media was sig-nificantly and negatively related to perceptions ofrisks, but positively related to perceptions of benefits,before they were entered into the regression model.

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Table I. Ordinary Least-Squares (OLS) Regression Predicting Perceived Risks and Perceived Benefits of NBAF

Perceived Risks Perceived Benefits

Before-Entry Final Before-Entry Final

Block 1: Community dummies

Athens, Georgia 0.00 0.04 0.03 0.05

Butner, North Carolina 0.04 0.05 0.02 0.04Flora, Mississippi 0.07 0.01 0.16 0.05Manhattan, Kansas 0.06 0.04 0.14 0.10

Incremental R2 (%) 1.1 3.2

Block 2: DemographicsAge 0.03 0.02 0.01 0.01Sex (female) 0.13 0.02 0.12 0.01Socioeconomic status 0.13 0.02 0.18 0.06


Block 3: PredispositionsReligiosity 0.02 0.03 0.08 0.02Ideology 0.08 0.00 0.11 0.03


Block 4: News media attentionPublic-affairs news attention 0.01 0.05 0.10 0.03NBAF news attention 0.11 0.05 0.18 0.02


Block 5: Interpersonal discussion and issue attitudeFrequency of public-affairs discussion 0.01 0.03 0.12 0.07

Frequency of NBAF discussion 0.07 0.04 0.06 0.08

Valence of last NBAF discussion 0.38 0.13 0.33 0.10

Own support for NBAF 0.62 0.52 0.55 0.45


Block 6: Risk/benefit perceptionPerceived risks 0.07 Perceived benefits 0.07


Final Equation R2 (%) 40.7 39.2

Note: p < 0.05, p < 0.01, p < 0.001. N= 1,716. All coefficients are standardized betas. Block 1 includes dummy variable representingfour of the communities, with San Antonio excluded. For this block, the left-hand column contains upon-entry standardized betas.

Not surprisingly, there was a strong relationshipbetween respondents overall support for the facil-ity and their perceptions of its risks and benefits.The more respondents supported the facility, the lesslikely they were to perceive a high level of risk andthe more likely they were to perceive a high level ofbenefits. Finally, perceptions of risk and benefit weresignificantly (at the 0.05 level) related to each other

when entered as predictors in the last block of theirrespective equations. The coefficient, while negative,is relatively small in magnitude.

Regarding Research Question 1, frequency ofissue-specific discussion was positively related toboth perception of risks and benefits before-entry(i.e., controlling for all other variables entered prior).For each of these variables, greater levels of discus-sion corresponded to a slight increase in levels of per-ceived risks and benefits, providing some insight into

the overall influence of discussion frequency on theseoutcome variables. Research Question 2 focused onthe valence of respondents most recent discussion.Valence was significantly related to perceptions ofboth risks and benefits. The direction of this relation-ship was such that, if respondents last spoke with asupporter, they were more likely to report lowerlev-els of risk but higher levels of benefit perceptions.

The reverse was true if they last spoke to an oppo-nent.

3.2. Interaction Effects

Our hypotheses focused on the moderatinginfluence of ones own support for the facilityon the effects of interpersonal discussion. Theinteraction tests provide evidence supporting a

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Table II. Results of Two-Way Interactions Testing HypothesizedModeration Effect of Own Support on Effects of Last Discussion

Valence and Discussion Frequency

Perceived PerceivedBlock 7: Two-Way Interactions Risks Benefits

Last discussion valence Own support 0.04 0.02Discussion frequency Own support 0.08 0.08

Note: p < 0.05, p < 0.01, p < 0.001. N = 1,716. Coefficientsare upon-entry betas.

frequency-amplification hypothesis but not acongruency-amplification hypothesis (see Table II).

In order to interpret the significant interactionfor frequency of NBAF-related discussion, we di-vided the sample into subgroups according to the re-sponse categories and plotted the observed means

on the dependent variable for each. As shown inFig. 1, respondents who opposed the facility weremore likely to perceive a higher level of risk; as theydiscussed the facility more often, however, their per-ception of risk was likely to increase significantly.Similarly, those who reported feeling neutral (i.e.,neither supporting nor opposing the facility) werealso likely to feel neutral about risks related to the fa-cility if they never discussed it. Among respondentsin this group, as their frequency of discussion in-creased, their neutrality also shifted toward a percep-tion of increased risk. Finally, supporters perceived

fewer risks than the other two groups, and as theirfrequency of discussion increased their level of per-ceived risks decreased.

In the equation predicting perceived benefits, asimilar pattern emerged (see Fig. 2). As with per-

Fig. 1. Two-way interaction effect of frequency of interpersonaldiscussion and individuals overall attitudes toward the NationalBio- and Agro-Defense Facility (NBAF) on perceived risks.

Fig. 2. Two-way interaction effect of frequency of interpersonaldiscussion and individuals overall attitudes toward the NationalBio- and Agro-Defense Facility (NBAF) on perceived benefits.

ceived risks, supporters and opponents of the facil-

ity fall unambiguously on either side of the neutralpoint (3 on the y-axis). As their frequency of dis-cussion increases, their levels of perceived benefitsshift positively and negatively, respectively. Interest-ingly, people who reported feeling neutral about thefacility initially reported higher than neutral levels ofbenefit perceptions, and perceived benefits increasedas their level of discussion increased.

4. DISCUSSION

This study explored interpersonal influences on

individual perceptions of risks and benefits. Concep-tualizing interpersonal discussion as a potential am-plification station, we found that frequency of dis-cussion functions to amplify and attenuate both riskand benefit perceptions, although this effect is con-tingent on an individuals overall level of support andcanin factpolarize perceptions between groupswith different initial perceptions. Our second hypoth-esis, that discussion valence would operate in a sim-ilar manner, found no support. Before we explorehow these results inform social theories of risk ingreater detail, we outline some limitations of our

study.

4.1. Limitations

First, our analyses relied on cross-sectional data,which cannot account for potential reverse causationas an explanation for our results. As part of our largerstudy, we collected data in a two-wave panel designin three of these five communities. Unfortunately,however, it was beyond the scope of this study to

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replicate the measures for the analyses presentedhere across both waves, even among the panel com-munities. We therefore do not formally test our the-oretically derived assumptions about causal influ-ences among variables, and our results should be

interpreted in light of this constraint. Nonetheless,studies that have shown that discussion frequencycan have a greater influence on attitudes than atti-tudes have on discussion frequency(21) increase ourconfidence that the causal arrow runs in a similardirection.

Second, we have tested our hypothesized rela-tionships on a single issue at a single point in time.In particular, we were faced with a tradeoff betweenconducting a detailed, contextualized study of a sin-gle issue or a more superficial inquiry across differ-ent issues. We opted in favor of the former, and thisraises questions about the ecological validity of ourfindings. We would argue, however, that the com-plexity of our study design in terms of data collec-tion and also the ability to develop an in-depth un-derstanding of the context of inquiry far outweighpotential concerns about focusing on one particularissue.

Finally, as one anonymous reviewer observed,there are many other features of interpersonal dis-cussion about risk that we have not taken into ac-count with this study. Among these are the sizeand composition of discussion networks, which haveplayed a large role in the study of political discussion

among citizens.(42,43) In order to appreciate fully therole that these other dimensions may play in ampli-fying or attenuating risk perceptions in different seg-ments of the populace, future research should focuson appropriate methodologies and measures for as-sessing those influences. Our own effort to tap thevalence of discussion is one promising step in thisdirection, although more attention needs to be paidin future research to developing good measures foruse in public opinion surveys. Addressing more fine-grained aspects of discussionfor example, the qual-ity of discussion or the process ofnegotiating meaning

based upon available news coveragemay be bettersuited to methodologies employing ethnography orin-depth interviews.(44)

4.2. Contributions to Risk Communication

Overall, the results presented here offer noveland more nuanced ways of understanding the un-derexplored influence of interpersonal discussion onperceptions of risks. Since past research has focused

on interpersonal discussion as one of the many com-peting communication channels,(15) this study firstevaluated the overall impact of frequency of discus-sion on both risk and benefit perceptions. Our resultssuggested a small positive influence in both cases;

however, we further hypothesized that such a directinfluence may hide differential effects for differentsubgroups within a population. This moderation ef-fect was supported, and we discuss its implications inmore detail below.

Similarly, we explored the influence of an indi-viduals most recent discussion. For this dimension ofinterpersonal talk, the direct impact was unambigu-ous: respondents most recent discussion had a strongimpactsecond in magnitude only to their overallattitudein predicting risk and benefit perceptionsof the biological research facility. Moreover, this re-lationship did not differ depending on whether or nottheir own support was congruent or incongruent withtheir discussant.

There are two possible explanations, both ofwhich merit attention in future research, for why thismight be the case. First, it seems that when peo-ple discussed the facilityand roughly half our sam-ple did not discuss it at allpeople mostly confinedthemselves to like-minded environments. It was be-yond the scope of this study to examine potentialmotivations or constraints that made it more or lesslikely for citizens to discuss the facility and withwhom, and answering these questions will be the

logical next step for research in this area. Second,the circumstances did not differentially impact peo-ples overall evaluations of risks and benefits, evenfor respondents within an incongruent discussionenvironment. That is, discussions neither increasednor decreased (i.e., amplified or attenuated) lev-els of these evaluations. Both of these explanationshave normative implications regarding democraticdecision-making processes surrounding risk-relatedfacilities and the influence of public opinion onpolicymakers.(22)

Going beyond direct main effects of discussion,

our interaction tests suggest that, depending on onesown overall attitude, frequency of discussion ampli-fies or attenuates judgments of risk. In this sense,engaging in discussion more frequently does appearto work as an amplification station within the so-cial amplification of risk framework. Notably, themechanism applies equally well to perceptions ofbenefits. Perhaps the most intriguing aspect of ourresults is the influence of frequency of discussionon neutrals, or those respondents who reported

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neither supporting nor opposing the new researchfacility. For these individuals, perceptions of bothrisks and benefits were amplified through more fre-quent discussion.

One explanation for this phenomenon could

be the amount of exposure to different viewpointswithin these discussions. For example, if an indi-vidual is neutral but discusses the facility in anopinion climate of majority supporters, one wouldexpect his or her evaluation of risks to decrease andbenefits to increase. This explanation does not fit ourresults, however, because upon making judgmentsof both risks and benefits associated with the re-search facility, neutrals were pulled in a slightly pos-itive direction. That is, in the present case, discus-sion frequency appears to have amplified both riskand benefit perceptions in strikingly similar ways.Thus, rather than functioning to shore them up assupporters and opponents, as shown in experimen-tal and survey research,(21,28) discussion seems to am-plify their already existing ambivalence or neutralitytoward the NBAF. To the degree that heightened at-titudinal ambivalence is related to decreased levelsof participation,(35) such a phenomenon also has con-sequences for democratic decision making regardingthe NBAF specifically, as well as for the implementa-tion of risk-related policies more generally.

Even with the contributions of this study to ourunderstanding of how interpersonal discussion canimpact on individual judgments of risks and ben-

efits, it seems evident that much more conceptualand empirical work needs to be done. Among otherthings, researchers must expand risk communicationresearch beyond the notion that mass media competewith discussion. The current study and other recentresearch(15) demonstrate that widening the concep-tual scope of risk communication can lead to fruitfulnew insights.

ACKNOWLEDGMENTS

A previous version of this article was awarded

first place in the Risk Communication SpecialtyGroup ExxonMobil student paper competition at the2009 annual meeting of the Society for Risk Analysis.This material is based upon work supported by theUniversity of WisconsinMadison Graduate School(Grant No. 090012) and the National Science Foun-dation (Grant No. SES-0820474). Any opinions, find-ings, and conclusions or recommendations expressedin this material are those of the authors and do notnecessarily reflect the views of the National ScienceFoundation.

REFERENCES

1. Kasperson RE, Renn O, Slovic P, Brown HS, Emel J, GobleR, Kasperson JX, Ratick S. The social amplification of risk: Aconceptual framework. Risk Analysis, 1988; 8(2):177187.

2. Renn O, Burns WJ, Kasperson JX, Kasperson RE, SlovicP. The social amplification of risk: Theoretical foundationsand empirical applications. Journal of Social Issues, 1992;48(4):137160.

3. Pidgeon NF, Kasperson RE, Kasperson JX. The Social Ampli-fication of Risk. New York: Cambridge University Press, 2003.

4. Greene JO, Burleson BR. Handbook of Communication andSocial Interaction Skills. Mahwah, NJ: Erlbaum, 2003.

5. Delli Carpini MX, Cook FL, Jacobs LR. Public deliberation,discursive participation, and citizen engagement: A review ofthe empirical literature. Annual Review of Political Science,2004; 7:315344.

6. Fishkin JS. Democracy and Deliberation. New Haven: YaleUniversity Press, 1991.

7. Schudson M. Why conversation is not the soul of democracy.Critical Studies in Mass Communication, 1997; 14(4):297309.

8. Scheufele DA. Talk or conversation? Dimensions of inter-personal discussion and their implications for participatory

democracy. Journalism and Mass Communication Quarterly,2000; 77(4):727743.9. Scheufele DA. Examining differential gains from mass media

and their implications for participatory behavior. Communi-cation Research, 2002; 29(1):4665.

10. Scheufele DA. Democracy for some? How political talk bothinforms and polarizes the electorate. Pp. 1932 in Hart RP,Shaw D (eds). Communication and U.S. Elections: NewAgendas. Lanham, MD: Rowman and Littlefield Publishers,2001.

11. Dunwoody S, Neuwirth K. Coming to terms with the impactof communication on scientific and technological risk judg-ments. Pp. 1130 in Wilkins L, Patterson P (eds). Risky Busi-ness: Communicating Issues of Science, Risk, and Public Pol-icy. Westport, CT: Greenwood, 1991.

12. Gutmann JO, Thompson D. Democracy and Disagreement.Cambridge, MA: Harvard University Press, 1996.

13. Binder AR. Routes to attention or shortcuts to apa-thy? Exploring domain-specific communication pathwaysand their implications for public perceptions of contro-versial science. Science Communication, in press. DOI:10.1177/1075547009345471.

14. Tyler TR, Cook FL. The mass media and judgments of risk:Distinguishing impact on personal and societal-level judg-ments. Journal of Personality and Social Psychology, 1984;47(4):693708.

15. Lehmkuhl MJ. Congruency within rural social networks as anindicator of interpersonal influence on risk judgments: Thegreat stir caused by BSE in a village in northern Germany.Public Understanding of Science, 2008; 17(4):485502.

16. Katz E, Lazarsfeld PF. Personal Influence. Glencoe, IL: FreePress, 1955.

17. Klapper JT. The Effects of Mass Communications. Glencoe,

IL: Free Press, 1960.18. Coleman CL. The influence of mass media and interpersonalcommunication on societal and personal risk judgments. Com-munication Research, 1993; 20(4):611628.

19. Morton TA, Duck JM. Communication and health beliefs:Mass and interpersonal influences on perceptions of risk toself and others. Communication Research, 2001; 28(5):602626.

20. Southwell BG, Torres A. Connecting interpersonal and masscommunication: Science news exposure, perceived ability tounderstand science, and conversation. Communication Mono-graphs, 2006; 73(3):334350.

21. Binder AR, Dalrymple KE, Brossard D, Scheufele DA. Thesoul of a polarized democracy: Testing theoretical linkages

7/27/2019 j.1539-6924.2010.01516.x

11/11

334 Binder et al.

between talk and attitude extremity during the 2004 presiden-tial election. Communication Research, 2009; 36(3):315340.

22. Flynn J, Peters E, Mertz CK, Slovic P. Risk, media, and stigmaat Rocky Flats. Risk Analysis, 1998; 18(6):715727.

23. Metz WC. Historical application of a social amplificationof risk model: Economic impacts of risk events at nuclearweapons facilities. Risk Analysis, 1996; 16(2):185193.

24. Huckfeldt R, Sprague JD.Networks in context: Thesocial flowof political information. American Political Science Review,1987; 81(4):11971216.

25. Noelle-Neumann E. The spiral of silence: A theory of publicopinion. Journal of Communication, 1974; 24(2):4351.

26. Scheufele DA, Shanahan J, Lee E. Real talk: Manipulating thedependent variable in spiral of silence research. Communica-tion Research, 2001; 28(3):304324.

27. Renn O. Risk communication and the social amplification ofrisk. Pp. 287324 in Kasperson RE, Stallen PJ (eds). Com-municating Risks to the Public. Dordrecht, The Netherlands:Kluwer, 1991.

28. Moscovici S, Zavalloni M. The group as a polarizer of at-titudes. Journal of Personality and Social Psychology, 1969;12:125135.

29. Tesser A, Conlee MC. Some effects of time and thought on

attitude polarization. Journal of Personality and Social Psy-chology, 1975; 31(2):262270.30. Brauer M, Gliner MD, Judd CM. The effects of repeated

expressions on attitude polarization during group discus-sions. Journal of Personality and Social Psychology, 1995;68(6):10141029.

31. Scheufele DA, Nisbet MC, Brossard D, Nisbet EC. Socialstructure and citizenship: Examining the impacts of socialsetting, network heterogeneity, and informational variableson political participation. Political Communication, 2004;21(3):315338.

32. Eveland WP. The effects of political discussion in producinginformed citizens: The roles of information, motivation, andelaboration. Political Communication, 2004; 21(2):177193.

33. Feldman L, Price V. Confusion or enlightenment? How expo-sure to disagreement moderates the effects of political discus-sion and media use on candidate knowledge. CommunicationResearch, 2008; 35(1):6187.

34. Huckfeldt R, Mendez JM, Osborn T. Disagreement, ambiva-lence, and engagement: The political consequences of hetero-geneous networks. Political Psychology, 2004; 25(1):6595.

35. Mutz DC. The consequences of cross-cutting networks forpolitical participation. American Journal of Political Science,2002;46(4):838855.

36. Johnson JV. National Bio- and Agro-Defense Facility: Fi-nal Environmental Impact Statement. Washington, DC:U.S. Department of Homeland Security, 2008. Availableat: http://www.dhs.gov/files/labs/gc 11877346776.shtm#1, Ac-cessed on August 3, 2009.

37. Dillman DA, Smyth JD, Christian LM. Mail and Internet Sur-veys: The Tailored Design Method, 3rd ed. New York: Wiley,2008.

38. AAPOR. Standard Definitions: Final Dispositions of CaseCodes and Outcome Rates for Surveys, 6th ed. Lenexa, KS:AAPOR, 2009.

39. Cohen J, Cohen P. Applied Multiple Regression/CorrelationAnalysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ:

Erlbaum, 1983.40. Eveland WP. Interactions and nonlinearity in mass communi-cation: Connecting theory and methodology. Journalism andMass Communication Quarterly, 1997; 74(2):400416.

41. Aiken LS, West SG. Multiple Regression: Testing and Inter-preting Interactions. Thousand Oaks, CA: Sage, 1991.

42. Huckfeldt R, Sprague JD. Citizens, Politics, and Social Com-munication: Information and Influence in an Election Cam-paign. New York: Cambridge University Press, 1995.

43. Eliasoph N. Avoiding Politics: How Americans Produce Apa-thy in Everyday Life. New York: Cambridge University Press,1998.

44. Babbie E. The Practice of Social Research, 11th ed. Belmont,CA: Thomson Wadsworth, 2007.

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