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The Wisdom of Some: Do We Always Need HighConsensus to Shape Consumer Behavior?Michael R. Sciandra, Cait Lamberton, andRebecca Walker ReczekFrom the Food and Drug Administration’s efforts to prompt healthier eating to the Environmental ProtectionAgency’s desire to prompt people to engage in environmentally friendly behaviors, a wide range of policymakers aim to persuade consumers. To do so, they must decide how and whether to use information aboutthe behavior of other consumers as part of their persuasive message. In four experimental studies, theauthors demonstrate that the persuasive advantage of high- versus low-consensus information dependson the target consumer’s trait level of susceptibility to interpersonal influence (SII). Low-SII consumersdifferentiate between low- and high-consensus information, such that they are more persuaded byhigh-consensus information. In contrast, high-SII consumers find any cue about the behavior of otherspersuasive, regardless of whether it is high or low consensus. Importantly, this finding suggests that policymakers may find success motivating behavioral change even in low-consensus situations. The authors closeby reporting data from two broadscale correlational surveys that identify behavioral, psychographic, anddemographic characteristics related to consumer SII as well as domains in which low consensus currentlyexists, so that policy makers can identify and target these individuals and related issues.Keywords: persuasion, susceptibility to interpersonal influence, social norms marketing, consensus, social proofPolicy makers often want to persuade people to change their thoughts and behaviors. For example, politicians frequently stump for votes, advocate for change, and seeksupport for new policies or legislation. Similarly, state and localgovernmental agencies and nonprofit nongovernmental organizations (NGOs) continually encourage consumers to engagein a multitude of desirable behaviors, including eating healthyfoods, staying physically active, recycling, and conserving energy. As such, policy makers frequently employ persuasivemessages designed to encourage specific behaviors. For example, in May 2015, New York City launched a campaign designed to reduce consumer waste and generated awarenessthrough persuasive messages on buses and subways and indigital communications (Gerlat 2015).Prior research in psychology and marketing has indicated thatthe success of such persuasive messages is largely contingent ontwo factors: (1) the credibility of the message source (i.e., thepolicy maker or organization; Petty and Briñol 2008) and (2) thestrength or quality of the message argument (i.e., how stronga case is made for the advocated attitude or behavior; Petty andCacioppo 1984). In general, most policy makers are endowedwith source credibility by virtue of political power (in the case ofelected officials), legal authority (e.g., state and local government agencies), or expertise (attributable to people within agovernmental or nongovernmental agency as a result of formaleducation or experience with a particular societal issue). Furthermore, once established, source credibility cannot be easilyaltered within a given persuasive message.However, policy makers do have considerable control overthe nature and strength of the arguments advanced in thepersuasive communications they employ. One aspect of message strength has to do with the behavior of other people. Thatis, messages can provide high-consensus information, whichstates that a majority of people engage in a given behavior, orlow-consensus information, which states that a minority ofpeople do so. In general, high-consensus messages are perceived as providing a stronger argument. The normative endorsement of a majority of consumers presents a compellingargument that other people should behave in the same waybecause it is either objectively superior or socially desirable(Aarts and Dijksterhuis 2003; Cialdini, Kallgren, and Reno1990; Naylor, Lamberton, and West 2012). For example, highlighting the fact thatmost students do not engage in binge-drinkingbehavior effectively curbed the consumption of alcohol amongcollege students (Haines and Spear 1996). Similarly, informinghotel patrons that the majority of guests reuse their towels increased reuse of towels in a hotel field study (Goldstein, Cialdini,and Griskevicius 2008).However, what can be done when policy makers want toprompt behaviors that are presently low consensus—for example,Michael R. Sciandra is Assistant Professor of Marketing, Dolan Schoolof Business, Fairfield University (e-mail: [email protected]). CaitLamberton is Fryrear Faculty Fellow and Associate Professor of Marketing, Katz Graduate School of Business, University of Pittsburgh(e-mail: [email protected]). Rebecca Walker Reczek is AssociateProfessor of Marketing, Fisher College of Business, Ohio State University (e-mail: [email protected]). Barbara Bickart served as associateeditor for this article.© 2017, American Marketing Association Journal of Public Policy & MarketingISSN: 0743-9156 (print) Vol. 36 (1) Spring 2017, 15–351547-7207 (electronic) 15 DOI: 10.1509/jppm.14.123to make prosocial but atypical choices (e.g., considering laborpractices of manufacturers as an important determinant ofwhich brand to buy, carrying reusable grocery bags, donatingto charities) or act in ways that may improve well-being evenwhen others are not engaging in the behavior (e.g., gettinga yearly flu shot, eating the daily recommended servingsof fruits and vegetables)? To the extent that low-consensusmessages are viewed as weaker arguments for a given behavior,some research has argued that they may be unsuccessful atpersuading consumers. For instance, Gerber and Rogers (2009)note that persuasive messages indicating that voter turnout iscurrently low actually depressed, rather than increased, voting.In light of findings such as these, policy makers might concludethat they should only use high-consensus information in persuasive messages.However, we argue that all hope is not lost in cases with lowconsensus. In this article, we identify consumers who do notneed high-consensus information to shape their behavior. Inparticular, we explore the role of consumers’ susceptibility tointerpersonal influence (SII; Bearden, Netemeyer, and Teel1989; McGuire 1968)—a psychographic measure with reliabledemographic correlates—in determining sensitivity to persuasivemessages containing high- versus low-consensus information.Across four experiments, we demonstrate that whether highconsensus information is more effective than low-consensus information at persuading consumers depends on the consumers’level of SII. To do so, we hold the persuasion goal and the sourceof the persuasive message constant. We manipulate only argument strength by varying consensus information to be high orlow. We demonstrate that for low-SII consumers, the degree ofpersuasion is contingent on consensus level (i.e., whether it ishigh or low consensus), whereas high-SII consumers find anyconsensus information persuasive, regardless of its level. Asa result, high-SII consumers can even be persuaded by thepresence of low-consensus information.We close by reporting results from two broadscale correlational surveys that identify behavioral, psychographic, anddemographic characteristics related to consumer SII, as wellas behaviors likely to be desirable to policy makers that arecurrently low consensus. These results offer targeting directionfor the promotion of new or less popular behaviors, consistentwith previous work in the marketing and public policy domainsuggesting that psychographic differences can be used to informinterventions (Rose, Bearden, and Manning 1996; Wood 2012).As such, our work can provide hope in situations in whichdesirable behaviors are low consensus, prompting some segment of consumers to begin to build the consensus that maypersuade others to conform.Theoretical DevelopmentProcessing Persuasive MessagesPetty and Cacioppo’s (1986) elaboration likelihood model hasarguably become the dominant model of persuasion in marketing (Petty, Cacioppo, and Schumann 1983; Shrum et al.2012) and public policy (Angst and Agarwal 2009; Rucker andPetty 2006) research. One of the central arguments of Petty andCacioppo’s theory is that messages can be processed centrally(i.e., carefully and with effort) or peripherally (i.e., with littlecare). Elaboration likelihood model research has suggested thatpeople are likely to centrally process most policy-related messages for two reasons. First, public policy messages are oftenrelated to important issues (e.g., health, financial security,safety) that people find personally relevant, a key determinant ofinvolvement (Celsi and Olson 1988), and prior research hasshown that increased involvement leads to more central routeprocessing (Petty and Cacioppo 1979; Petty, Cacioppo, andSchumann 1983). Second, many public service announcements(PSAs) from policy makers and NGOs use emotion-basedappeals (e.g., an anti–texting and driving campaign that usesa fear appeal), and emotions have also been shown to increasemotivation to process (Tiedens and Linton 2001).When consumers are processing centrally, both sourcecredibility (Petty and Briñol 2008) and argument strength (Pettyand Cacioppo 1984) have been identified as key elements thatdetermine the effectiveness of the persuasive message. In general, sources perceived as credible are more persuasive than lesscredible sources (Lirtzman and Shuv-Ami 1986; Maddux andRogers 1980; Ohanian 1991; Watts and McGuire 1964), andstrong arguments are more persuasive than weak arguments(Batra and Stayman 1990; Petty and Cacioppo 1984). Thus, wewould anticipate that strong arguments yield strong persuasion.However, we propose that this is not always true. Specifically,we argue that a psychographic characteristic, SII, influences howpeople integrate consensus information into their judgments ofargument strength; thus, SII affects an argument’s persuasiveness (Petty and Wegener 1998). That is, even when consumersare processing a persuasive message centrally, individual differences may direct different amounts of attention to variousmessage components, making them more or less important inpersuasion. In general, our proposition is consistent with researchsuggesting that individual difference measures can influencepersuasion (Cacioppo and Petty 1982; Cacioppo, Petty, andMorris 1983) and with previous research on the power ofconsensus showing that high consensus sometimes mattersand sometimes does not (Maass and Clark 1984; Moscovici1985; Wood et al. 1994). We detail our predictions in thefollowing section.SII and the Power of ConsensusSusceptibility to interpersonal influence is “the need to identifyor enhance one’s image with significant others through acquisition and use of products and brands, the willingness toconform to the expectations of others regarding purchasingdecisions, and/or the tendency to learn about products andservices by observing others and/or seeking information fromothers” (Bearden, Netemeyer, and Teel 1989, p. 474). Susceptibility to interpersonal influence has been shown to havea wide range of effects on consumer product preferences andmessage responses. For example, people high in SII prefer products that induce positive attributions from others (Netemeyer,Bearden, and Teel 1992) and that are socially visible (Batra,Homer, and Kahle 2001).Not surprisingly, prior work has argued that the persuasivepower of information about the behavior of others varies withSII (Bearden, Netemeyer, and Teel 1989; Martin, Wentzel, andTomczak 2008; McGuire 1968; Mourali, Laroche, and Pons2005). In general, people low in SII are motivated to makedecisions on the basis of what they believe to be “correct” andtherefore do not make decisions solely to fit in with others16 The Wisdom of Some(Batra, Homer, and Kahle 2001; Bearden, Netemeyer, and Teel1989; Netemeyer, Bearden, and Teel 1992; Wooten and Reed2004). Therefore, we predict that people low in SII will use boththe message source and the argument strength when evaluatingpolicy-relevant messages. Furthermore, we believe these consumers will differentiate between low- and high-consensusinformation when making a decision. This is due to the informational social influence of consensus information; lowerSII people are more likely to conform to high-consensus thanlow-consensus information because people assume that theactions of a majority of others reflect the more “correct”behavior (Thibaut and Kelley 1959). In general, social prooftheory acknowledges that people determine what is correct ina certain situation by looking to the behavior of others (Cialdini2009; Lun et al. 2007). Greater consensus will therefore beinterpreted as a stronger message argument on the basis of thesocial proof it provides (Cialdini, Kallgren, and Reno 1990,1991). Thus, we predict that, all else being equal, lower-SII peoplewill be more persuaded by information indicating that theadvocated behavior is a high- (vs. low-) consensus behavior.In contrast, high-SII people are primarily motivated to makesocially safe decisions (Wooten and Reed 2004). Those high inSII have been shown to easily trust the judgments and behaviorsof a single interpersonal source and to view this information asa reliable foundation of reality and sufficient for making decisions (Deutsch and Gerard 1955; Mourali, Laroche, and Pons2005). Therefore, we argue that even though they are processingcentrally, people high in SII will primarily attend to whetheranyone has engaged in the action rather than to the exact level ofconsensus provided. As a result, unlike low-SII people, wepredict that high-SII people do not need high-consensus information to be persuaded. The presence of even a small numberor proportion of supporters may be a strong argument to thisgroup. As such, they will be equally persuaded by either low- orhigh-consensus messages. This prediction is consistent with thedefinition of SII, which does not provide any guidance on howmany “others” are necessary to persuade or influence the behavior of those high in the SII trait. Thus, we predict that, ceterisparibus, high-SII people are just as persuaded by informationthat the behavior in the persuasive message is high consensus asthey are when informed the behavior is low consensus.While this prediction is consistent with a broader literaturebase acknowledging the potential for small groups or singleindividuals to wield considerable persuasive power (Maass andClark 1984; Moscovici 1985; Wood et al. 1994), our researchdiffers from this prior work in that we assess the influence of anunidentified minority (low consensus) or majority (high consensus) of people. In particular, prior research has found thatinformation on minorities is most influential when the identityof the minority is known. For example, information froma single individual, such as a market maven (Feick and Price1987) or opinion leader (Iyengar, Van den Bulte, and Valente2011; King and Summers 1970) can have a significant influenceon opinions and product adoption decisions. However, opinionleaders are often sought out for their expertise or influentialposition in a network (Feick and Price 1987); their identity isknown and part of the reason for their influence from a minorityposition. Similarly, the theory of reasoned action (Ajzen andFishbein 1980; Fishbein and Ajzen 1975) recognizes the role ofnorms in influencing behavioral intentions, noting that a singleindividual or small group can be particularly persuasive if thebeliefs of the minority are highly valued. However, again, fora minority to be influential, information about the identity of theminority individual or group is paramount. In contrast, we investigate situations in which consumers do not have any information about the identity of the minority or majority being usedin the persuasive message. Formally, we therefore propose:H1: When evaluating persuasive messages, as SII increases, therelative advantage of high-consensus information over lowconsensus information in generating persuasion decreases.We further predict that this failure to distinguish betweenpersuasive messages containing information about high- andlow-consensus behaviors is because high-SII people are primarily vigilant in detecting the presence or absence of anyconsensus information but have a weaker focus on the level ofconsensus provided. That is, even though high-SII consumersare processing policy-related messages centrally, they treat consensus information more as a peripheral cue, focusing only onwhether it is there and not on the detail of whether it is high orlow. Note that this prediction is congruent with dual processtheories of persuasion, which acknowledge that peripheral cuescan be used in central route processing (Chaiken, Liberman, andEagly 1989; Eagly and Chaiken 1993). Thus, we are proposingthat attentional differences in the processing of consensus information drive the differences we predict for high- and low-SIIconsumers. This effect should therefore be moderated as follows:H2: When evaluating persuasive messages, attentional cues highlighting whether information about others’ behavior is highversus low consensus moderate the effect of SII on persuasionsuch that (a) when no attentional cue is given, as SII increases,the relative advantage of high-consensus information over lowconsensus information in generating persuasion decreases, and(b) when an attentional cue is given, the relative advantage ofhigh-consensus information over low-consensus information ingenerating persuasion is preserved.Overview of StudiesNext, we report the results of four experiments and twobroadscale surveys designed to test our predictions about SIIand the (un)importance of high consensus in certain segments;we then highlight the practical utility of our findings. Consistentwith recent recommendations (Murayama, Pekrun, and Fiedler2014; Schmidt 2009), we test our hypotheses in multiple contexts that may be relevant to policy makers and marketers,using both lexical and quantitative manipulations of consensuslevel and testing for replication in projected and real behavior.In Study 1a, we probe the interplay between consumer SII andconsensus information using an ethically based purchase decision. In Study 1b, we investigate the impact of consensusinformation on healthy eating habits. Study 2 assesses theimpact of consensus information and SII on environmentallyfriendly behaviors. Finally, Study 3 evaluates charitable contribution decisions and examines the impact of attentional cuesemphasizing high-consensus and low-consensus information,demonstrating process through moderation as advocated bySpencer, Zanna, and Fong (2005). To isolate the effect of SIIon susceptibility to persuasion by messages containing highversus low-consensus information, in all of our studies wemanipulate only argument strength, as reflected in consensusinformation. Within each study, we keep both the persuasiongoal of the message (i.e., the desired or advocated behavior) andJournal of Public Policy & Marketing 17the source of the persuasive message constant. Finally, we report the results of two correlational studies that isolate demographic and psychographic characteristics associated with SIIand identify low consensus issues for which our findings maybe most helpful. These findings can help policy makers segmentthe market using SII and choose consensus-based messages appropriately for segments with high versus low levels of SII.Study 1aStudy 1a examines consumers’ reactions to lexically describedhigh- and low-consensus information in a car-buying scenario.We adapt the conjoint procedure and ethical decision-makingstimulus employed by Irwin and Naylor (2009) to quantify reactions to high- versus low-consensus information for respondents at different levels of SII. In this study, we focus onbehavior that is prosocial and policy relevant: taking the laborpractices of the manufacturer into account when making a carpurchase decision.MethodSeventy-six participants (43% female; Mage = 36.5 years,age range 19–69 years) recruited using Amazon MechanicalTurk (MTurk) participated in Study 1a in exchange for a smallmonetary incentive. Study 1a employed an SII × consensus information (high vs. low) between-subjects design, whereby SIIwas a measured continuous variable and consensus informationwas manipulated. Before beginning the study, participantscompleted an instructional manipulation check (IMC) toidentify people who did not follow directions (Oppenheimer,Meyvis, and Davidenko 2009).1 All participants passed theIMC and were included in the analysis.Participants imagined that they were in the market for a newautomobile and were asked to share their opinions on a varietyof cars that differed on three main attributes: price, performance,and an ethical labor attribute. Participants were informed thatthe cars they would be evaluating did not differ in any waysother than these three attributes:• Price: The final negotiated cost of the car.• Performance: Performance ratings for the car, from a leadingconsumer magazine. The performance ratings range from 1 to10, with 10 being the highest rating.• Labor practices of the car manufacturer: The manufacturersdiffer in the treatment of their workers. The best measure of thistreatment is the number of lawsuits brought by employeesagainst the management.The explanation of the labor practices of the car manufacturer clearly stated that the number of lawsuits against management had no bearing on the quality of the car, only thetreatment of employees. Participants were then provided witha recommendation from a casual acquaintance on how toproceed with their car search. In the high-consensus information condition, participants were provided with the followingrecommendation:Most people consider the labor practices of car manufacturers whenpurchasing a car since that provides a good indication of how ethicalthe organization is. Like everyone else, you should consider laborpractices as an important factor in your decision.In the low-consensus information condition, participants sawthe following recommendation:Most people don’t consider the labor practices of car manufacturerswhen purchasing a car. However, unlike everyone else, you shouldconsider labor practices as an important factor in your decision sincethat provides a good indication of how ethical the organization is.After participants saw this recommendation, they viewed andrated all possible car combinations that could be formed usingthe aforementioned attributes. Because each attribute had threelevels, participants viewed and evaluated 27 different cars(labeled from car A through car AA). The three levels of theprice attribute were $15,977, $18,385, and $20,793. The threelevels of the performance attribute (on a ten-point scale) were6.0, 7.75, and 9.5. Consistent with Irwin and Naylor (2009), thethree levels of the ethical labor attribute were “fewer thanaverage,” “average,” and “more than average.” These categoriescorresponded with the following descriptions: “one or twolawsuits every few years,” “five to ten lawsuits per year,” and“many complaints, including assault charges.” Finally, aftercompleting a short filler task, participants completed Bearden,Netemeyer, and Teel’s (1989) 12-item measure of SII, whichwas indexed for analysis (a = .92; M = 3.02, SD = 1.17).ResultsTo be certain that our manipulation of consensus informationdid not influence our measure of SII, we first assessed SII withinboth the high- and low-consensus information conditions. Therewere no differences in SII between the high-consensus information condition (M = 2.94) and the low-consensus informationcondition (M = 3.07; F(1, 74) = .24, p > .60). Given this finding,we next discuss our focal analysis.We applied a sequential process to analyze the data, obtainingconjoint weights for each participant and then testing whetherthe weights were dependent on consensus information and SII.Following Irwin and Naylor (2009), we converted negativeslopes to zeros for the second part of our analysis.2 Given thatparticipants were advised to take labor practices into accountwhen evaluating the vehicles, we investigate labor practiceweights as a proxy for persuasion. Greater weights on the laborattribute indicated greater persuasion as a result of the recommendation provided. We conducted a regression analysiswith contrast-coded consensus information, consumer SII (meancentered for analysis), and the SII × consensus informationinteraction as predictors of weights for the labor attribute. Therewas no main effect of whether the recommendation was highconsensus or low consensus (t(1) = _1.68, p > .05). However,there was a main effect of SII (t(1) = 2.20, p < .05, b = .17), suchthat an increase in SII resulted in greater persuasion as a result of1In our IMC, participants viewed a page that consisted of a title, directions, andone multiple-choice question asking which factors they considered when makinga purchase. The directions clearly stated that people should not answer the questionshown on the page. Instead, to demonstrate that they were paying attention, participants were told to leave the question blank, click on the title at the top of the page(which turned green once clicked), and then click continue. If a participant did notfollow these directions they were taken to the same screen, which prompted them to“Please Read the Directions.” If they failed a second time, they saw a note in allcapitalized letters and red font again advising to “Please Read the Directions.” 2Results remain consistent without converting negative slopes to zeros.18 The Wisdom of Somethe recommendation. Although this was not the focus of ourcentral hypothesis, this result makes sense given that previousresearch has supported the notion that high-SII consumerswould be particularly interested in complying with a recommendation from an acquaintance because an acquaintance ismore likely to be viewed as a “significant other.” More importantly, a significant interaction between consensus informationand consumer SII emerged (t(1) = 2.10, p < .05, b = .16).To understand the interaction between consensus information and consumer SII, we applied a floodlight analysis(Hayes and Matthes 2009). A floodlight analysis shows therange of values for which a simple effect is and is not significant(Spiller et al. 2013). Therefore, in the context of our study, thefloodlight analysis identified the range of consumer SII valuesfor which there is a significant difference in labor attribute weightbetween the high- and low-consensus information conditionsand the range of consumer SII values for which there is not asignificant difference in labor attribute weight between thehigh- and low-consensus information conditions.This procedure revealed that participants scoring below anaverage value of 2.96 on the seven-point SII scale weighted thelabor attribute more heavily in the high-consensus informationcondition compared with the low-consensus informationcondition (ps < .05). However, the high-consensus cue did notgenerate different weighting of the labor attribute for peoplehigh in SII (participants scoring above 2.96 on the SII measure; ps > .05). Figure 1, Panel A, provides a graphical representation of these results, and Table 1 captures the crossovervalues for all studies, beginning with this set of results. Thisfinding provides support for H1 and illustrates high-SII consumers’ tendency to be persuaded by a persuasive messagebacked by either high- or low-consensus information aboutothers’ behavior.DiscussionStudy 1a demonstrates that consumers’ response to highconsensus versus low-consensus information is dependent onSII. We found that lower-SII consumers were more persuadedby a recommendation backed by high-consensus information relative to one backed by low-consensus information.In contrast, higher-SII participants showed similar levelsof persuasion when a recommendation was accompanied byeither high- or low-consensus information. The results ofStudy 1a therefore suggest that the high-SII segment of consumers may be a particularly attractive segment for policymakers and marketers to target when advocating a new prosocial behavior (e.g., installing low-flow shower heads toconserve water) or attempting to build support for a newprosocial initiative (e.g., charitable or recycling programs).Targeting these people initially can be an effective way toultimately build the majority support needed to persuadelow-SII consumers.While Study 1a provides initial support for H1, these findingsare subject to an alternate explanation: psychological reactanceon the part of low-SII consumers. Psychological reactance occurs when a person feels threatened by a recommendation andis therefore motivated to do the opposite of the recommendation in a bid to regain freedom that has been lost or threatened(Brehm 1966). It is possible that low-SII people are particularlylikely to experience reactance to a low-consensus recommendation, viewing this communication as a strong threat to theirability to make a free choice, especially given that the lowconsensus information suggests a low-quality argument to thosehigh in SII. Therefore, the purpose of Study 1b is to provideadditional evidence for our demonstrated effect and dig deeperinto the process driving the result, including ruling out thisalternative explanation empirically. We also use a different operationalization of high and low consensus. The operationalization of low consensus used in Study 1a requires consumers tomake the inference that while “most people don’t” considerlabor practices, some people do (and, thus, there is low consensus). Given that we expect high-SII consumers to primarilyattend to whether any consensus is present and not to devoteadditional processing resources beyond simply noting whetherit is present, we use a simpler operationalization of low consensus (that does not require an inference) in Study 1b and allsubsequent studies.Figure 1. Consumer SII and Consensus Information forStudies 1a and 1bLabor Attribute Weight0 0.51.01.52.02.53.0+2 SDSIIHigh consensus Low consensus–2 SD 2.96A: Study 1aVegetables on Grocery List00.51.01.52.02.53.03.54.04.5–2 SD +2 SDSIIHigh consensus Low consensus2.60B: Study 1bJournal of Public Policy & Marketing 19Study 1bIn Study 1b, we aim to replicate the effect demonstrated inStudy 1a and provide additional support for H1 using a differentproduct category and consensus cue manipulation. In our firststudy, we used lexical cues (i.e., “most people”). In this study,we use quantitative consensus cues similar to the method usedby Goldstein, Cialdini, and Griskevicius (2008). An additionalgoal of this study is to provide initial evidence that attentionaldifferences are driving this observed effect.MethodIn Study 1b, 241 participants recruited through MTurk completed the study for a small monetary incentive. Study 1b usedan SII × consensus information (high vs. low) between-subjectsdesign, in which SII was a measured continuous variable andconsensus information was manipulated. Congruent with Study1a, participants completed an IMC to detect whether theyfollowed directions (Oppenheimer, Meyvis, and Davidenko2009). Eight participants failed the IMC three times and weredropped from the analysis. Thus, Study 1b had a final sample of233 participants (50% female; Mage = 36.7 years, age range18–83 years).To begin Study 1b, participants completed Bearden,Netemeyer, and Teel’s (1989) 12-item measure of SII (a = .92,M = 3.21, SD = 1.16). Measuring people’s SII before theycompleted the focal task helped ensure that our experimentalmanipulations did not influence this measure. After completing approximately ten minutes of unrelated filler tasks,participants were asked to imagine that they had just arrivedat their local grocery store to purchase some food items forthe week. On entering the grocery store, participants imaginedseeing a PSA produced by the Organization for Healthy Eating,a fictitious public health organization. In the high-consensusinformation condition, participants read the following recommendation from the organization:Almost 76% of American grocery shoppers eat five servings ofvegetables a day. We recommend you join your fellow shoppers andconsume at least five servings of vegetables a day.Conversely, in the low consensus information condition,participants read the following recommendation from theorganization:Almost 26% of American grocery shoppers eat five servings ofvegetables a day. We recommend you join your fellow shoppers andconsume at least five servings of vegetables a day.After reading the PSA, participants were asked to put together a grocery shopping list for their trip. Each participantcompiled a list of 15 items from a total of 60 available groceryitems. Ten of the 60 available grocery items were vegetables(potatoes, broccoli, carrots, tomatoes, celery, corn, cucumbers,lettuce, onions, and peppers).3 We used the number of vegetableitems included on the list as our focal dependent variable.In our conceptualization, we have argued that high-SIIconsumers attend to the fact that consensus information ispresent but place relatively less weight on the degree of consensus provided. If those high in SII indeed place less emphasison consensus information, we would anticipate that they wouldhave a more difficult time recalling the consensus informationpresent in the PSA when compared with those lower in SII.Therefore, after completing the grocery shopping task and several filler items unrelated to the current hypotheses, all participants were asked to recall the consensus information shown inthe PSA by typing in the percentage they saw in the PSA. Wemeasured memory for this information to serve as a proxy forparticipants’ attention given that greater attention should result ina stronger memory trace (Baddeley et al. 1984). Responses werecoded as either correct or incorrect.Finally, given that the results of Study 1a could be explainedby reactance (Brehm 1966) on the part of low-SII people, participants completed an eight-item measure of reactance to therecommendation from the Organization for Healthy Eatingadapted from Hong and Faedda (1996), which was indexed foranalysis (a = .90, M = 2.48, SD = 1.10; sample items: “I resistedthe attempt of the OHE to influence me”; “The recommendationfrom the OHE restricted my freedom of choice”; “I consideredthe recommendation from the OHE to be an intrusion”).ResultsWe first conducted a regression analysis with contrast-codedconsensus information, consumer SII (mean-centered forTable 1. Consumer SII Crossover Values for Floodlight AnalysesSignificant SII CrossoverValues from the Floodlight Analyses Average SII Value for StudyStudy 1a £2.96 3.02Study 1b £2.60 3.21Study 2: No control £3.44 3.28Study 3: Low attentional emphasis £2.18 3.38Study 3: High attentional emphasis ‡5.06 3.38Notes: The crossover values indicate the value of SII at which the floodlight test reached statistical significance for each study. The floodlight test highlights a rangeof values of our continuous predictor SII and demonstrates for which values the group differences (high- vs. low-consensus information) are significant(Spiller et al. 2013). For example, in Study 1a, this table indicates that for SII values less than or equal to 2.96, there is a significant difference in laborattribute weight between the high- and low-consensus information groups. At SII values greater than 2.96, there was no significant difference in laborattribute weight between the experimental groups.3Although some of these products may be better classified as fruits froma scientific perspective, they are typically classified as vegetables by U.S.consumers (Rupp 2015).20 The Wisdom of Someanalysis), and the SII × consensus information interaction aspredictors of the number of vegetables included on participants’shopping lists. There was no main effect of whether the recommendation in the PSA was high or low consensus (t(1) = _.95,p > .30). Furthermore, there was no main effect of SII (t(1) = .20,p > .80). While inconsistent with Study 1a, this result is notsurprising given that organizations are less likely to be perceived as significant others. However, consistent with the resultsof Study 1a, we found a significant interaction between consensus information and consumer SII (t(1) = 2.40, p < .05, b = .25).To better understand this interaction, we again used afloodlight procedure (Hayes and Matthes 2009). This procedure revealed that participants scoring below an averagevalue of 2.60 on the seven-point SII included more vegetableson their shopping lists in the high-consensus informationcondition compared with the low-consensus information condition (ps < .05). However, participants scoring above 2.60 onthe SII measure showed no difference in the number of vegetables included between the high- and low-consensus conditions(ps > .05). Figure 1, Panel B, provides a graphical representationof the results. This finding offers additional support for H1 andfurther demonstrates that people high in SII can be influencedin the face of both high- and low-consensus information.We next conducted a logistic regression analysis withcontrast-coded consensus information, consumer SII (meancentered for analysis), and the SII × consensus informationinteraction as predictors of correctly recalling the consensusinformation. There was no main effect of consensus information and no interaction between SII and consensus information(ps > .10). However, there was a main effect of SII (c2(1) = 4.00,p < .05, b = _.24), such that a one-unit increase in SII corresponds to a 22% decrease in the odds of correctly recallingthe consensus information, in support of our theorization thathigher-SII consumers are less focused on the exact nature of theconsensus information. We also recorded the time participantsspent reading the PSA (M = 13 seconds) and found that timespent processing the PSA (p > .10) did not differ by SII. This isconsistent with our theory that the differences in attention are notdriven by the overall amount of attention paid to a persuasivemessage but how this attention is allocated, with those high inSII devoting less attention to the nature of whether consensusinformation is high or low.Finally,we conducted aregression analysis with contrast-codedconsensus information, consumer SII (mean-centered for analysis), and the SII × consensus information interaction as predictorsof reactance. All effects were nonsignificant (all ps > .50). Furthermore, a process mediation analysis (Hayes 2013) confirmedthat reactance did not fully or partially mediate the focal effect.DiscussionThe results of Study 1b replicate Study 1a’s effects using adifferent consensus information manipulation and consumercontext. We again demonstrate that consumers’ response tolow- and high-consensus information is dependent on SII. Furthermore, Study 1b demonstrates that this effect holds whenthe consensus information is presented in quantitative rather thanlexical form. Finally, the results of Study 1b provide initialinsight into the process driving these results. Previously, weargued that people high in SII actively process message information in the same way as people lower in SII, but the formerare more vigilant to the presence of consensus informationrather than the exact level of consensus. Consistent with ourargument, we observe that people higher in SII process for similarlengths of time as do people lower in SII but are in fact less likelyto accurately recall consensus information, irrespective of thenature of the consensus information (i.e., high or low). In addition, we ruled out reactance as a driver of the observed effect.We return to the role of attention in Study 3. However, in our nextstudy we dig deeper into the role that consensus informationplays in high-SII consumers’ perceptions of argument strength.Study 2The purpose of Study 2 is to tease apart the influence of themessage source and consensus cues in the persuasion process.Previously, we have argued that any consensus informationinfluences high-SII consumers’ perceptions of argumentstrength. However, we have not demonstrated the importance ofconsensus information beyond a simple source recommendation. Therefore, in Study 2 we include a control condition inwhich no consensus information is provided.MethodTwo hundred forty-nine participants recruited using MTurkparticipated in Study 2 in exchange for a small monetary incentive. Study 2 employed an SII × consensus information (highvs. low vs. control) between-subjects design, in which SII wasa measured continuous variable and consensus informationwas manipulated. Consistent with Studies 1a and 1b, participantscompleted an IMC to identify whether they were followingdirections (Oppenheimer, Meyvis, and Davidenko 2009). Twoparticipants failed the IMC three times and were removed fromthe analysis, leaving a useable sample of 247 people (46% female;Mage = 35.0 years old, age range 19–68 years).We designed Study 2 following one of the most compellingrecent demonstrations of the effectiveness of consensus information in a policy relevant setting, Goldstein, Cialdini, andGriskevicius’s (2008) hotel field study. That research demonstrated that using high-consensus information in a persuasivemessage increased hotel guests’ participation in an environmental conservation program (by giving guests informationabout the number of previous guests who reused their towels)when compared with traditional proenvironmental appeals.To begin this study, all participants completed Bearden,Netemeyer, and Teel’s (1989) 12-item measure of SII (a = .90,M = 3.28, SD = 1.08). After completing approximately tenminutes of unrelated filler tasks, participants were asked toimagine that they were staying seven nights in a hotel. Furthermore, participants were asked to imagine that on enteringthe hotel room they notice some literature provided by the hotelchain related to environmentally responsible behaviors. In thehigh-consensus information condition, participants saw the following recommendation from the hotel chain:Almost 75% of our guests participate in our resource conservationprogram by reusing their towels more than once. Please join yourfellow guests in this program to help save the environment byreusing your towels during your stay.In the low-consensus information condition, participants readthe following recommendation from the hotel chain:Journal of Public Policy & Marketing 21Almost 25% of our guests participate in our resource conservationprogram by reusing their towels more than once. Please join yourfellow guests in this program to help save the environment byreusing your towels during your stay.Finally, participants in the control condition viewed the following recommendation from the hotel chain:Our hotel has a resource conservation program that involves reusingtowels more than once. Please help save the environment by reusingyour towels during your stay.After the presentation of the hotel’s literature on environmentally responsible behaviors, participants indicated theirlikelihood of reusing their towels for two nights on a scale from0 (“very unlikely”) to 100 (“very likely”).ResultsWe measured how long participants spent reading the hotel’sstatement about environmentally responsible behaviors. Onaverage, participants spent 24 seconds reading, and, as in Study1b, processing time did not differ by level of SII (p > .70).No Control GroupWe first tested for replication of our prior results, using contrast codes to compare the low-consensus and high-consensusconditions. We conducted a regression analysis with contrastcoded consensus information, consumer SII (mean-centered foranalysis), and the interaction between SII and consensus information as predictors of towel reuse likelihood. We founda main effect of consensus information (t(1) = _2.35, p < .05,b = _5.17) such that the likelihood of towel reuse was significantly lower in the low-consensus condition when comparedwith the overall study mean. Consistent with Study 1b, therewas no main effect of SII (t(1) = _.87, p > .30), which again isnot unexpected given that organizations are less likely to fill therole of significant others. Most importantly, and consistent withthe results of Studies 1a and 1b, a significant interaction between consensus information and consumer SII emerged (t(1) =2.26, p < .05, b = 4.78).To understand the interaction between consensus information and consumer SII, we again used the floodlight procedure advocated by Hayes and Matthes (2009). This procedurerevealed that participants scoring below an average value of3.44 on the seven-point SII scale were more likely to reuse theirtowels in the high-consensus information condition comparedwith the low-consensus information condition (ps < .05).However, high-SII people (participants who scored above 3.44on the SII measure; ps > .05) showed no difference in theirlikelihood of reusing towels between the high- and lowconsensus conditions. Figure 2, Panel A, provides a graphicalrepresentation of the results. This finding provides additionalsupport for H1 and again affirms the persuasive influence of bothhigh- and low-consensus information for people high in SII.With Control GroupNext, we analyzed the results with the control group. We applied a regression analysis with two contrast-coded consensusinformation variables (“high consensus”: control group vs. highconsensus information; “low consensus”: control group vs.low-consensus information), consumer SII (mean-centered foranalysis), and the interaction between SII and the two consensus information variables (high consensus and low consensus) as predictors of towel reuse likelihood.No main effect of the low-consensus contrast code (t(1) = .46,p > .60) or consumer SII (t(1) = .84, p > .35) appeared. However,a significant main effect of the high-consensus contrast codeemerged (t(1) = 2.74, p < .01, b = 6.22) such that, regardless ofSII, participants showed higher likelihoods of towel reuse whenviewing the high-consensus message compared with the control message. This result is consistent with prior research onsocial proof and high-consensus effects (Cialdini 2009; Cialdini,Kallgren, and Reno 1990; Goldstein, Cialdini, and Griskevicius2008) and further demonstrates the power of high-consensusmessages compared with standard control messages.We find that the interaction between the high-consensuscontrast code and consumer SII was not significant (t(1) = _.12,p > .90). However, we did find a significant interaction betweenthe low-consensus contrast code and consumer SII (t(1) = 2.19Figure 2. Consumer SII and Consensus Information forStudy 2A: No Control ConditionB: With Control Condition0102030405060708090100–2 SD +2 SDLikelihood of Reuse Two NightsSIIHigh consensus Low consensus3.440102030405060708090100–2 SD +2 SDLikelihood of Reuse Two NightsHigh consensusSIILow consensus Control22 The Wisdom of Somep < .05, b = 4.52). This result demonstrates that as consumerSII increases, participants showed a greater likelihood of reusingtheir towels when viewing the low-consensus message whencompared with the control message. Figure 2, Panel B, providesa graphical representation of the results.DiscussionThe results of Study 2 provide several important insights. First,we replicate the general effect found in Studies 1a and 1b. Wealso find that consistent with previous research, both low- andhigh-SII people found a high-consensus message more persuasive than a control message. However, high-SII people alsofound the low-consensus message significantly more persuasivethan the control message. This result reaffirms our conceptualization and suggests that high-SII people are sensitive toconsensus elements in a message (beyond a simple recommendation) but do not differentiate between high and low consensus levels.Study 3We have argued that high-SII people pay relatively less attention to whether consensus information is high or low andfocus instead on the fact that any consensus information ispresent in a message. If high-SII consumers’ failure to differentiate between high- and low-consensus information is drivenby a weaker attention to the nature of consensus information,externally drawing focus to the level of consensus informationshould change the way that high-SII people react to high- versuslow-consensus cues. That is, increasing the salience of the specific level of consensus information will signal to higher-SII peoplethat this information is relevant in their decision making.For this study, we therefore take an experimental causal-chainapproach to demonstrate our proposed process. By manipulatingboth our independent variable and the proposed processmeasure,focal attention, this approach enables us to make inferencesabout the mechanism driving our results (Spencer, Zanna, andFong 2005).Furthermore,inthis study,we examine people’s actualdonation behaviors to an environmental charitable organization.MethodA total of 200 participants recruited using MTurk participated inStudy 3 in exchange for a small monetary incentive. Study 3employed an SII × consensus information (high vs. low) ×attentional emphasis (low vs. high) between-subjects design, inwhich SII was a measured continuous variable and consensusinformation and attentional emphasis were manipulated. Consistent with our previous studies, participants first completedan IMC to identify whether they were following directions(Oppenheimer, Meyvis, and Davidenko 2009). Three participants failed the IMC three times and were removed from thesample, leaving 197 participants (47% female; Mage = 33.9 yearsold, age range 19–69 years).To begin this study, all participants completed Bearden,Netemeyer, and Teel’s (1989) 12-item measure of SII (a = .93,M = 3.38, SD = 1.25). After completing the measure of SII,participants began the study, which consisted of several different tasks. First, participants were informed that theywould evaluate a series of charitable organizations. Each participant read about five charitable organizations (the UnitedWay, Feeding America, the Task Force for Global Health, theAmerican Red Cross, and the American Cancer Society) andevaluated each organization. This task was meant to orientparticipants and prepare them for our focal investigation. Afterrating these charitable organizations, participants completedapproximately 15 minutes of filler tasks. Finally, participantswere informed that they would be asked to evaluate one lastcharitable organization. As a thank you for evaluating this finalcharitable organization, participants were provided with a $.25bonus. Each participant then evaluated the American Woodland Foundation (AWF), a fictional charity, which was modeled after the American Forest Foundation. Participants saw thecharity’s web page, which included information on the mission,vision, and values of the AWF, as well as a recommendationfrom the manager of AWF advising each person to makea donation.In the high-consensus information condition, participantswere informed: “In previous studies, 75% of people madea donation to this charitable organization. We recommendyou consider making a donation.” By contrast, in the lowconsensus information condition, participants saw the following information: “In previous studies, 5% of people madea donation to this charitable organization. We recommend youconsider making a donation.”In addition to manipulating the consensus information, wealso manipulated attention to the high- or low-consensus information by changing the spatial and visual presentation of themessage. Recent research has acknowledged the considerabledifference that ad positioning makes in garnering attention ina digital environment (Sharethrough 2015; Stambor 2013). Forexample, using eye-tracking technology, Sharethrough (2015)found that native online advertisements (i.e., ads using the sameformat and positioning as the focal content of a website) received 52% more attention than banner advertisements in theperiphery. Similarly, advertisements placed in-stream on a webpage showed click-through rates 45 times greater than displayads on the right margin of the page (Stambor 2013). Given thesefindings, in the high–attentional emphasis condition, the highconsensus or low-consensus information was placed in the webpage stream (vs. on the right margin of the page) and was alarger font than the rest of the web page. Furthermore, the numerical information was in red, boldface font to create a visualcontrast (see Appendix A). In contrast, in the low–attentionalemphasis condition, the high- or low-consensus informationwas placed in the right margin of the AWF web page and useda font that was similar in size to the rest of the web page, andthe red font was not used for the numerical information (seeAppendix A).After reviewing the web page, participants were asked if theywould like to donate any of their $.25 bonus to AWF. Theinstructions were clear that the decision was up to the participant, who could choose if and how much (s)he wanted todonate. We used the amount donated to the AWF charity as ourdependent variable in this experiment. Participants who donateda portion of their bonus to the charity received the remainingbalance (if any) as their bonus.ResultsConsistent with prior studies, we measured the time participantsspent reading the AWF web page. Participants spent 40 secondsJournal of Public Policy & Marketing 23on average reading the information shown on the AWF site,which did not differ by level of SII (p > .90).We conducted a regression analysis with the contrast-codedconsensus information, contrast-coded attentional emphasis,consumer SII (mean-centered for analysis), and all possibleinteractions as predictors of donations. All main effects and twoway interaction effects were nonsignificant (ps > .15). However,as we predicted, results revealed a significant three-way interaction among consensus information, attentional emphasis, andSII (t(1) = _2.95, p < .01, b = _.16).To further investigate this three-way interaction, we separately examined the impact of consensus information and consumer SII within the low– and high–attentional emphasisconditions. In both conditions, we conducted separate regressionanalyses with consensus information, consumer SII, and the SII ×consensus information interaction as predictors of donations.In the low–attentional emphasis condition, the effect ofconsensus information (t(1) = _.58, p > .50) and consumer SIIwere not significant (t(1) = _.35, p > .70). However, we founda significant interaction between consensus information andSII (t(1) = 2.23, p < .05, b = .02). A floodlight analysis (Hayesand Matthes 2009) revealed that participants scoring below anaverage value of 2.18 on the seven-point SII scale donatedmoremoney to AWF in the high-consensus information conditioncompared with the low-consensus information condition (allvalues less than 2.18; ps < .05). In contrast, the high- and lowconsensus cues did not generate different donation amounts forhigh-SII participants (people who scored above 2.18 on the SIImeasure; ps > .05). Figure 3, Panel A, provides a graphicalrepresentation of the interaction. This result is conceptuallyconsistent with the findings of prior studies and provides support for H2a.In the high–attentional emphasis condition, the effect ofconsensus information (t(1) = _.69, p > .40) and consumerSII were not significant (t(1) = _.36, p > .70). However,we found a significant interaction between consensus information and SII (t(1) = _1.92, p = .05, b = _.01). Mostimportantly, a floodlight analysis (Hayes and Matthes 2009)revealed that participants scoring above an average value of5.06 on the seven-point SII scale donated more to AWF inthe high-consensus information condition compared withthe low-consensus information condition (all values greaterthan 5.06; ps < .05). Figure 3, Panel B, provides a graphicalrepresentation of the results. This finding provides supportfor H2b, demonstrating that when attentional emphasis isadded to consensus information, people higher in SII caneffectively differentiate between high- and low-consensusinformation.DiscussionStudy 3 demonstrates that attentional emphasis highlightingconsensus information moderates the interactive effect ofSII and consensus information on persuasion. By showingthis moderation, the results of Study 3 provide evidence ofour proposed process (Spencer, Zanna, and Fong 2005), insupport of our theory that attentional differences drive highSII consumers’ failure to differentiate between high- andlow-consensus information. The results of Study 3 thereforesuggest that if policy makers want to discourage a givenbehavior by pointing out how few people engage in thebehavior (e.g., smoking), it is critical that they use these typeof attentional cues to ensure that high SII consumers respond in the desired manner. Importantly, public policymakers can easily implement an attentional intervention inthe type of PSA used as the stimulus for this study if the goalis to ensure that high-SII consumers recognize the differencebetween the behavior of a majority and that of a minority.Although this particular study was done in an online context, similar techniques for emphasizing consensus information (e.g., with different font sizes and color) can be usedin offline contexts.While the results of Study 3 demonstrate the importance ofattentional cues to help high-SII consumers effectively differentiate between high- and low-consensus information, we notethat our manipulation seems to eliminate low-SII consumers’differentiation among consensus information levels. It is possible that highlighting consensus information may have activated a strong persuasion knowledge schema (Friestad andFigure 3. Consumer SII and Consensus Information forStudy 3A: Low Attentional EmphasisB: High Attentional Emphasis0.05.10.15.20.25–2 SD +2 SDAmount Donated ($)SIIHigh consensus Low consensus2.180.05.10.15.20–2 SD +2 SDAmount Donated ($)SIIHigh consensus Low consensus5.0624 The Wisdom of SomeWright 1994) in low-SII consumers, leading them to be skepticalof the message.Study 4aAlthough work that focuses on an individual difference asa moderator often provides important theoretical insights, onecritique is that it is challenging for this type of research to shapepractice. How can policy makers or charities reach people ofvarious trait characteristics? To support a legitimate discussionof the practical implications of our work for policy makers,NGOs, and managers, we sought reliable demographic andpsychographic correlates of SII—observable variables or behaviors that could allow for the identification and targeting ofpeople at different levels of SII. To do so, we conducteda survey of 582 consumers, using MTurk, who completed thesurvey in return for a nominal payment (Mage = 31.3 years, agerange 18–70 years; 44% female). To begin, all participantscompleted Bearden, Netemeyer, and Teel’s (1989) 12-itemmeasure of SII, which was indexed for analysis (a = .92, M =3.12, SD = 1.15). Next, participants completed items capturinga wide variety of focal constructs. We chose these behavioral,psychographic, and demographic constructs for both practicaland theoretical reasons, described next. The measures usedappear in Appendix B.Money ManagementIf better or worse money managers consistently vary inSII, managers of various types of products (i.e., debt collection services vs. wealth-management services) orpolicy makers (who may be attempting to motivate peoplewith poor money management skills) might choose to useconsensus information and/or attentional cues differently.Prior research has established that consumers’ financialknowledge and constraints affect both their financialplanning (Morrin, Broniarczyk, and Inman 2012; Xiao et al.2011) and the type of products they purchase (Cheemaand Soman 2006; Soman and Cheema 2002). Similarly,financial management has been linked to different demographic and personality characteristics (Norvilitis et al.2006); thus, we included a measure of money managementto determine whether it is significantly related to consumerSII.Political Orientation and NationalismA rich store of research has established the geographic dispersion of people of different political orientations. For example,states are commonly known as more “red” (conservative) or“blue” (liberal). Furthermore, data on political orientation canalso be captured longitudinally and at district or local levels.Thus, if people of different SII levels can be identified on thebasis of political orientation, we have a large amount of information that can be used to geotarget messages and shapepersuasive communications accordingly. In addition, understanding whether SII is related to political orientation mayenable us to make contributions directly to the literature onpolitical communications, an arena in which consensus information has been used with varying degrees of success(Gerber and Rogers 2009). We therefore measured individualpolitical orientation and nationalism.Responsible Consumerism and ResourceConservationPolicy makers and marketers are increasingly recognizingenvironmentalism and social responsibility as an importantdomain both for the introduction of new products and forencouraging positive behavior change (Banerjee, Iyer, andKashyap 2003; Kronrod, Grinstein, and Wathieu 2012; Luchset al. 2010; Prothero et al. 2011). In addition, some work hasacknowledged environmental conservation attitudes as a useful profiling and segmentation basis (Laroche, Bergeron,and Barbaro-Forleo 2001; Straughan and Roberts 1999). Furthermore, given the importance of consensus information inencouraging conservation behaviors (as in Goldstein, Cialdini,and Griskevicius 2008), our findings may have implications forhow prior research findings are implemented. Accordingly, weincluded measures of responsible consumerism and resourceconservation.Individual LifestylesPrior work has established the importance of individual lifestyles and habits in both marketing (Burroughs and Rindfleisch2002) and public policy domains (Verplanken andWood 2006).Therefore, we also included measures of individual lifestyle,religious habits, life satisfaction, and health perceptions, including information such as frequency of exercise, frequency ofdining out, hours spent pursuing a hobby, smoking habits, generalfeelings of stress, feelings of religiosity, and general satisfactionwith life, to determine whether any of these values, activities, andlifestyles are significantly correlated with consumer SII.Media HabitsBoth marketers and policy makers identify and target individual consumers on the basis of these consumers’ media habitsand consumption. It is still imperative to use both traditionalchannels of communication (i.e., television and print advertising) and newer channels (i.e., the Internet and social networks) to deliver content and messages to a relevant and highlyselective market segment. Therefore, we also included measuresof media usage such as television and Internet habits.DemographicsFinally, we also included measures of demographic variables.These included age, gender, marital status, and educationalbackground.ResultsThe average score on SII was 3.12 in the overall (n = 582)sample. To provide background on the pervasiveness of highSII in the general population, we assessed the average SII in thetop quintile (n = 123) of SII. The average SII value for these 123people was 4.81 (SD = .46; range 4.25–6.33). Furthermore,37% of our sample fell above an SII value of 3.44, the highestcrossover value from our experiments. These findings suggest that higher-SII consumers are not an inconsequential groupJournal of Public Policy & Marketing 25and make up a substantial number of people in the generalmarketplace.Next, we discuss the results of our associative analysis between SII and the demographic, psychographic, and behavioralmeasures previously highlighted. For continuous measures, weconducted a regression analysis with responses to the aforementioned measures as predictors of consumer SII. To assessmulticollinearity, we evaluated variance inflation factors for allcontinuous predictors in the model. All variance inflationfactors were well below a value of 10, indicating no issues withmulticollinearity (Kutner et al. 2005).For categorical measures, we ran analyses of variance witha Bonferroni correction for multiple comparisons. Resultsrevealed that higher SII is associated with better ability tomanage money (b = .12, p < .01), a more liberal as opposed toconservative political orientation (b = _.07, p < .05), consumerbehaviors that are environmentally responsible (b = .16, p

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