The Wisdom of Some Do We Always Need HighConsensus to Shape.docx

The Wisdom of Some: Do We Always Need High
Consensus to Shape Consumer Behavior?
Michael R. Sciandra, Cait Lamberton, and
Rebecca Walker Reczek
From the Food and Drug Administration’s efforts to prompt
healthier eating to the Environmental Protection
Agency’s desire to prompt people to engage in environmentally
friendly behaviors, a wide range of policy
makers aim to persuade consumers. To do so, they must decide
how and whether to use information about
the behavior of other consumers as part of their persuasive
message. In four experimental studies, the
authors demonstrate that the persuasive advantage of high-
versus low-consensus information depends
on the target consumer’s trait level of susceptibility to
interpersonal influence (SII). Low-SII consumers
differentiate between low- and high-consensus information,
such that they are more persuaded by
high-consensus information. In contrast, high-SII consumers
find any cue about the behavior of others
persuasive, regardless of whether it is high or low consensus.
Importantly, this finding suggests that policy
makers may find success motivating behavioral change even in
low-consensus situations. The authors close
by reporting data from two broadscale correlational surveys that
identify behavioral, psychographic, and
demographic characteristics related to consumer SII as well as
domains in which low consensus currently
exists, so that policy makers can identify and target these
individuals and related issues.

Keywords: persuasion, susceptibility to interpersonal influence,
social norms marketing, consensus, social proof
P
olicy makers often want to persuade people to change
their thoughts and behaviors. For example, politicians
frequently stump for votes, advocate for change, and seek
support for new policies or legislation. Similarly, state and
local
governmental agencies and nonprofit nongovernmental orga-
nizations (NGOs) continually encourage consumers to engage
in a multitude of desirable behaviors, including eating healthy
foods, staying physically active, recycling, and conserving en-
ergy. As such, policy makers frequently employ persuasive
messages designed to encourage specific behaviors. For ex-
ample, in May 2015, New York City launched a campaign de-
signed to reduce consumer waste and generated awareness
through persuasive messages on buses and subways and in
digital communications (Gerlat 2015).
Prior research in psychology and marketing has indicated that
the success of such persuasive messages is largely contingent on
two factors: (1) the credibility of the message source (i.e., the
policy maker or organization; Petty and Briñol 2008) and (2)
the
strength or quality of the message argument (i.e., how strong
a case is made for the advocated attitude or behavior; Petty and
Cacioppo 1984). In general, most policy makers are endowed
with source credibility by virtue of political power (in the case
of
elected officials), legal authority (e.g., state and local govern-
ment agencies), or expertise (attributable to people within a
governmental or nongovernmental agency as a result of formal

education or experience with a particular societal issue). Fur-
thermore, once established, source credibility cannot be easily
altered within a given persuasive message.
However, policy makers do have considerable control over
the nature and strength of the arguments advanced in the
persuasive communications they employ. One aspect of mes-
sage strength has to do with the behavior of other people. That
is, messages can provide high-consensus information, which
states that a majority of people engage in a given behavior, or
low-consensus information, which states that a minority of
people do so. In general, high-consensus messages are per-
ceived as providing a stronger argument. The normative en-
dorsement of a majority of consumers presents a compelling
argument that other people should behave in the same way
because it is either objectively superior or socially desirable
(Aarts and Dijksterhuis 2003; Cialdini, Kallgren, and Reno
1990; Naylor, Lamberton, and West 2012). For example, high-
lightingthefactthatmoststudentsdonotengageinbinge-drinking
behavior effectively curbed the consumption of alcohol among
college students (Haines and Spear 1996). Similarly, informing
hotel patrons that the majority of guests reuse their towels in-
creased reuse of towels in a hotel field study (Goldstein,
Cialdini,
and Griskevicius 2008).
However, what can be done when policy makers want to
prompt behaviorsthat arepresently low consensus—for example,
Michael R. Sciandra is Assistant Professor of Marketing, Dolan
School
of Business, Fairfield University (e-mail: [email protected]).
Cait
Lamberton is Fryrear Faculty Fellow and Associate Professor of
Mar-
keting, Katz Graduate School of Business, University of

Pittsburgh
(e-mail: [email protected]). Rebecca Walker Reczek is
Associate
Professor of Marketing, Fisher College of Business, Ohio State
Uni-
versity (e-mail: [email protected]). Barbara Bickart served as
associate
editor for this article.
© 2017, American Marketing Association Journal of Public
Policy & Marketing
ISSN: 0743-9156 (print) Vol. 36 (1) Spring 2017, 15–35
1547-7207 (electronic) DOI: 10.1509/jppm.14.12315
mailto:[email protected]
http://dx.doi.org/10.1509/jppm.14.123
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123&domain=pdf&date_stamp=2017-04-01
to make prosocial but atypical choices (e.g., considering labor
practices of manufacturers as an important determinant of
which brand to buy, carrying reusable grocery bags, donating
to charities) or act in ways that may improve well-being even
when others are not engaging in the behavior (e.g., getting
a yearly flu shot, eating the daily recommended servings
of fruits and vegetables)? To the extent that low-consensus
messages are viewed as weaker arguments for a given behavior,
some research has argued that they may be unsuccessful at
persuading consumers. For instance, Gerber and Rogers (2009)
note that persuasive messages indicating that voter turnout is
currently low actually depressed, rather than increased, voting.
In light of findings such as these, policy makers might conclude

that they should only use high-consensus information in per-
suasive messages.
However, we argue that all hope is not lost in cases with low
consensus. In this article, we identify consumers who do not
need high-consensus information to shape their behavior. In
particular, we explore the role of consumers’ susceptibility to
interpersonal influence (SII; Bearden, Netemeyer, and Teel
1989; McGuire 1968)—a psychographic measure with reliable
demographic correlates—in determining sensitivity to
persuasive
messages containing high- versus low-consensus information.
Across four experiments, we demonstrate that whether high-
consensus information is more effective than low-consensus in-
formation at persuading consumers depends on the consumers’
level of SII. To do so, we hold the persuasion goal and the
source
of the persuasive message constant. We manipulate only argu-
ment strength by varying consensus information to be high or
low. We demonstrate that for low-SII consumers, the degree of
persuasion is contingent on consensus level (i.e., whether it is
high or low consensus), whereas high-SII consumers find any
consensus information persuasive, regardless of its level. As
a result, high-SII consumers can even be persuaded by the
presence of low-consensus information.
We close by reporting results from two broadscale correla-
tional surveys that identify behavioral, psychographic, and
demographic characteristics related to consumer SII, as well
as behaviors likely to be desirable to policy makers that are
currently low consensus. These results offer targeting direction
for the promotion of new or less popular behaviors, consistent
with previous work in the marketing and public policy domain
suggesting that psychographic differences can be used to inform
interventions (Rose, Bearden, and Manning 1996; Wood 2012).
As such, our work can provide hope in situations in which

desirable behaviors are low consensus, prompting some seg-
ment of consumers to begin to build the consensus that may
persuade others to conform.
Theoretical Development
Processing Persuasive Messages
Petty and Cacioppo’s (1986) elaboration likelihood model has
arguably become the dominant model of persuasion in mar-
keting (Petty, Cacioppo, and Schumann 1983; Shrum et al.
2012) and public policy (Angst and Agarwal 2009; Rucker and
Petty 2006) research. One of the central arguments of Petty and
Cacioppo’s theory is that messages can be processed centrally
(i.e., carefully and with effort) or peripherally (i.e., with little
care). Elaboration likelihood model research has suggested that
people are likely to centrally process most policy-related mes-
sages for two reasons. First, public policy messages are often
related to important issues (e.g., health, financial security,
safety) that people find personally relevant, a key determinant
of
involvement (Celsi and Olson 1988), and prior research has
shown that increased involvement leads to more central route
processing (Petty and Cacioppo 1979; Petty, Cacioppo, and
Schumann 1983). Second, many public service announcements
(PSAs) from policy makers and NGOs use emotion-based
appeals (e.g., an anti–texting and driving campaign that uses
a fear appeal), and emotions have also been shown to increase
motivation to process (Tiedens and Linton 2001).
When consumers are processing centrally, both source
credibility (Petty and Briñol 2008) and argument strength (Petty
and Cacioppo 1984) have been identified as key elements that
determine the effectiveness of the persuasive message. In gen-
eral, sources perceived as credible are more persuasive than less
credible sources (Lirtzman and Shuv-Ami 1986; Maddux and

Rogers 1980; Ohanian 1991; Watts and McGuire 1964), and
strong arguments are more persuasive than weak arguments
(Batra and Stayman 1990; Petty and Cacioppo 1984). Thus, we
would 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
how
people integrate consensus information into their judgments of
argument strength; thus, SII affects an argument’s persuasive-
ness (Petty and Wegener 1998). That is, even when consumers
are processing a persuasive message centrally, individual dif-
ferences may direct different amounts of attention to various
message components, making them more or less important in
persuasion. In general, our proposition is consistent with
research
suggesting that individual difference measures can influence
persuasion (Cacioppo and Petty 1982; Cacioppo, Petty, and
Morris 1983) and with previous research on the power of
consensus showing that high consensus sometimes matters
and sometimes does not (Maass and Clark 1984; Moscovici
1985; Wood et al. 1994). We detail our predictions in the
following section.
SII and the Power of Consensus
Susceptibility to interpersonal influence is “the need to identify
or enhance one’s image with significant others through ac-
quisition and use of products and brands, the willingness to
conform to the expectations of others regarding purchasing
decisions, and/or the tendency to learn about products and
services by observing others and/or seeking information from
others” (Bearden, Netemeyer, and Teel 1989, p. 474). Sus-
ceptibility to interpersonal influence has been shown to have
a wide range of effects on consumer product preferences and
message responses. For example, people high in SII prefer prod-
ucts 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 persuasive
power of information about the behavior of others varies with
SII (Bearden, Netemeyer, and Teel 1989; Martin, Wentzel, and
Tomczak 2008; McGuire 1968; Mourali, Laroche, and Pons
2005). In general, people low in SII are motivated to make
decisions on the basis of what they believe to be “correct” and
therefore do not make decisions solely to fit in with others
16 The Wisdom of Some
(Batra, Homer, and Kahle 2001; Bearden, Netemeyer, and Teel
1989; Netemeyer, Bearden, and Teel 1992; Wooten and Reed
2004). Therefore, we predict that people low in SII will use
both
the message source and the argument strength when evaluating
policy-relevant messages. Furthermore, we believe these con-
sumers will differentiate between low- and high-consensus
information when making a decision. This is due to the in-
formational social influence of consensus information; lower-
SII people are more likely to conform to high-consensus than
low-consensus information because people assume that the
actions of a majority of others reflect the more “correct”
behavior (Thibaut and Kelley 1959). In general, social proof
theory acknowledges that people determine what is correct in
a certain situation by looking to the behavior of others (Cialdini
2009; Lun et al. 2007). Greater consensus will therefore be
interpreted as a stronger message argument on the basis of the
social proof it provides (Cialdini, Kallgren, and Reno 1990,
1991). Thus, we predict that, all else being equal, lower-SII
people
will be more persuaded by information indicating that the

advocated behavior is a high- (vs. low-) consensus behavior.
In contrast, high-SII people are primarily motivated to make
socially safe decisions (Wooten and Reed 2004). Those high in
SII have been shown to easily trust the judgments and behaviors
of a single interpersonal source and to view this information as
a reliable foundation of reality and sufficient for making de-
cisions (Deutsch and Gerard 1955; Mourali, Laroche, and Pons
2005). Therefore, we argue that even though they are processing
centrally, people high in SII will primarily attend to whether
anyone has engaged in the action rather than to the exact level
of
consensus provided. As a result, unlike low-SII people, we
predict that high-SII people do not need high-consensus in-
formation to be persuaded. The presence of even a small number
or proportion of supporters may be a strong argument to this
group. As such, they will be equally persuaded by either low- or
high-consensus messages. This prediction is consistent with the
definition of SII, which does not provide any guidance on how
many “others” are necessary to persuade or influence the be-
havior of those high in the SII trait. Thus, we predict that,
ceteris
paribus, high-SII people are just as persuaded by information
that the behavior in the persuasive message is high consensus as
they are when informed the behavior is low consensus.
While this prediction is consistent with a broader literature
base acknowledging the potential for small groups or single
individuals to wield considerable persuasive power (Maass and
Clark 1984; Moscovici 1985; Wood et al. 1994), our research
differs from this prior work in that we assess the influence of an
unidentified minority (low consensus) or majority (high con-
sensus) of people. In particular, prior research has found that
information on minorities is most influential when the identity
of the minority is known. For example, information from
a single individual, such as a market maven (Feick and Price

1987) or opinion leader (Iyengar, Van den Bulte, and Valente
2011; King and Summers 1970) can have a significant influence
on opinions and product adoption decisions. However, opinion
leaders are often sought out for their expertise or influential
position in a network (Feick and Price 1987); their identity is
known and part of the reason for their influence from a minority
position. Similarly, the theory of reasoned action (Ajzen and
Fishbein 1980; Fishbein and Ajzen 1975) recognizes the role of
norms in influencing behavioral intentions, noting that a single
individual or small group can be particularly persuasive if the
beliefs of the minority are highly valued. However, again, for
a minority to be influential, information about the identity of
the
minority individual or group is paramount. In contrast, we in-
vestigate situations in which consumers do not have any infor-
mation about the identity of the minority or majority being used
in the persuasive message. Formally, we therefore propose:
H1: When evaluating persuasive messages, as SII increases, the
relative advantage of high-consensus information over low-
consensus information in generating persuasion decreases.
We further predict that this failure to distinguish between
persuasive messages containing information about high- and
low-consensus behaviors is because high-SII people are pri-
marily vigilant in detecting the presence or absence of any
consensus information but have a weaker focus on the level of
consensus provided. That is, even though high-SII consumers
are processing policy-related messages centrally, they treat con-
sensus information more as a peripheral cue, focusing only on
whether it is there and not on the detail of whether it is high or
low. Note that this prediction is congruent with dual process
theories of persuasion, which acknowledge that peripheral cues
can be used in central route processing (Chaiken, Liberman, and
Eagly 1989; Eagly and Chaiken 1993). Thus, we are proposing

that attentional differences in the processing of consensus in-
formation drive the differences we predict for high- and low-SII
consumers. This effect should therefore be moderated as
follows:
H2: When evaluating persuasive messages, attentional cues
high-
lighting whether information about others’ behavior is high
versus low consensus moderate the effect of SII on persuasion
such that (a) when no attentional cue is given, as SII increases,
the relative advantage of high-consensus information over low-
consensus information in generating persuasion decreases, and
(b) when an attentional cue is given, the relative advantage of
high-consensus information over low-consensus information in
generating persuasion is preserved.
Overview of Studies
Next, we report the results of four experiments and two
broadscale surveys designed to test our predictions about SII
and the (un)importance of high consensus in certain segments;
we then highlight the practical utility of our findings.
Consistent
with recent recommendations (Murayama, Pekrun, and Fiedler
2014; Schmidt 2009), we test our hypotheses in multiple con-
texts that may be relevant to policy makers and marketers,
using both lexical and quantitative manipulations of consensus
level and testing for replication in projected and real behavior.
In Study 1a, we probe the interplay between consumer SII and
consensus information using an ethically based purchase de-
cision. In Study 1b, we investigate the impact of consensus
information on healthy eating habits. Study 2 assesses the
impact of consensus information and SII on environmentally
friendly behaviors. Finally, Study 3 evaluates charitable con-
tribution decisions and examines the impact of attentional cues
emphasizing high-consensus and low-consensus information,
demonstrating process through moderation as advocated by

Spencer, Zanna, and Fong (2005). To isolate the effect of SII
on susceptibility to persuasion by messages containing high-
versus low-consensus information, in all of our studies we
manipulate only argument strength, as reflected in consensus
information. Within each study, we keep both the persuasion
goal of the message (i.e., the desired or advocated behavior) and
Journal of Public Policy & Marketing 17
the source of the persuasive message constant. Finally, we re-
port the results of two correlational studies that isolate demo-
graphic and psychographic characteristics associated with SII
and identify low consensus issues for which our findings may
be most helpful. These findings can help policy makers segment
the market using SII and choose consensus-based messages ap-
propriately for segments with high versus low levels of SII.
Study 1a
Study 1a examines consumers’ reactions to lexically described
high- and low-consensus information in a car-buying scenario.
We adapt the conjoint procedure and ethical decision-making
stimulus employed by Irwin and Naylor (2009) to quantify re-
actions to high- versus low-consensus information for re-
spondents at different levels of SII. In this study, we focus on
behavior that is prosocial and policy relevant: taking the labor
practices of the manufacturer into account when making a car
purchase decision.
Method
Seventy-six participants (43% female; Mage = 36.5 years,
age range 19–69 years) recruited using Amazon Mechanical
Turk (MTurk) participated in Study 1a in exchange for a small
monetary incentive. Study 1a employed an SII × consensus in-
formation (high vs. low) between-subjects design, whereby SII

was a measured continuous variable and consensus information
was manipulated. Before beginning the study, participants
completed an instructional manipulation check (IMC) to
identify people who did not follow directions (Oppenheimer,
Meyvis, and Davidenko 2009).1 All participants passed the
IMC and were included in the analysis.
Participants imagined that they were in the market for a new
automobile and were asked to share their opinions on a variety
of cars that differed on three main attributes: price,
performance,
and an ethical labor attribute. Participants were informed that
the cars they would be evaluating did not differ in any ways
other than these three attributes:
• Price: The final negotiated cost of the car.
• Performance: Performance ratings for the car, from a leading
consumer magazine. The performance ratings range from 1 to
10, with 10 being the highest rating.
• Labor practices of the car manufacturer: The manufacturers
differ in the treatment of their workers. The best measure of this
treatment is the number of lawsuits brought by employees
against the management.
The explanation of the labor practices of the car manufac-
turer clearly stated that the number of lawsuits against man-
agement had no bearing on the quality of the car, only the
treatment of employees. Participants were then provided with
a recommendation from a casual acquaintance on how to
proceed with their car search. In the high-consensus informa-
tion condition, participants were provided with the following
recommendation:
Most people consider the labor practices of car manufacturers

when
purchasing a car since that provides a good indication of how
ethical
the organization is. Like everyone else, you should consider
labor
practices as an important factor in your decision.
In the low-consensus information condition, participants saw
the following recommendation:
Most people don’t consider the labor practices of car
manufacturers
when purchasing a car. However, unlike everyone else, you
should
consider labor practices as an important factor in your decision
since
that provides a good indication of how ethical the organization
is.
After participants saw this recommendation, they viewed and
rated all possible car combinations that could be formed using
the aforementioned attributes. Because each attribute had three
levels, participants viewed and evaluated 27 different cars
(labeled from car A through car AA). The three levels of the
price attribute were $15,977, $18,385, and $20,793. The three
levels of the performance attribute (on a ten-point scale) were
6.0, 7.75, and 9.5. Consistent with Irwin and Naylor (2009), the
three levels of the ethical labor attribute were “fewer than
average,” “average,” and “more than average.” These categories
corresponded with the following descriptions: “one or two
lawsuits every few years,” “five to ten lawsuits per year,” and
“many complaints, including assault charges.” Finally, after
completing a short filler task, participants completed Bearden,
Netemeyer, and Teel’s (1989) 12-item measure of SII, which
was indexed for analysis (a = .92; M = 3.02, SD = 1.17).

Results
To be certain that our manipulation of consensus information
did not influence our measure of SII, we first assessed SII
within
both the high- and low-consensus information conditions. There
were no differences in SII between the high-consensus infor-
mation condition (M = 2.94) and the low-consensus information
condition (M = 3.07; F(1, 74) = .24, p > .60). Given this
finding,
we next discuss our focal analysis.
We applied a sequential processto analyze the data, obtaining
conjoint weights for each participant and then testing whether
the weights were dependent on consensus information and SII.
Following Irwin and Naylor (2009), we converted negative
slopes to zeros for the second part of our analysis.2 Given that
participants were advised to take labor practices into account
when evaluating the vehicles, we investigate labor practice
weights as a proxy for persuasion. Greater weights on the labor
attribute indicated greater persuasion as a result of the rec-
ommendation provided. We conducted a regression analysis
with contrast-codedconsensus information, consumer SII (mean-
centered for analysis), and the SII × consensus information
interaction as predictors of weights for the labor attribute.
There
was no main effect of whether the recommendation was high
consensus 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),
such
that an increase in SII resulted in greater persuasion as a result
of
1In our IMC, participants viewed a page that consisted of a
title, directions, and
one multiple-choice question asking which factors they
considered when making

a purchase. The directions clearly stated that people should not
answer the question
shown on the page. Instead, to demonstrate that they were
paying attention, par-
ticipants 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 not
follow 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 all
capitalized letters and red font again advising to “Please Read
the Directions.” 2Results remain consistent without converting
negative slopes to zeros.
the recommendation. Although this was not the focus of our
central hypothesis, this result makes sense given that previous
research has supported the notion that high-SII consumers
would be particularly interested in complying with a recom-
mendation from an acquaintance because an acquaintance is
more likely to be viewed as a “significant other.” More impor-
tantly, a significant interaction between consensus information
and consumer SII emerged (t(1) = 2.10, p < .05, b = .16).
To understand the interaction between consensus in-
formation and consumer SII, we applied a floodlight analysis
(Hayes and Matthes 2009). A floodlight analysis shows the
rangeof values for which a simple effect is and is not significant
(Spiller et al. 2013). Therefore, in the context of our study, the
floodlight analysis identified the range of consumer SII values
for which there is a significant difference in labor attribute

weight
between the high- and low-consensus information conditions
and the range of consumer SII values for which there is not a
significant difference in labor attribute weight between the
high- and low-consensus information conditions.
This procedure revealed that participants scoring below an
average value of 2.96 on the seven-point SII scale weighted the
labor attribute more heavily in the high-consensus information
condition compared with the low-consensus information
condition (ps < .05). However, the high-consensus cue did not
generate different weighting of the labor attribute for people
high in SII (participants scoring above 2.96 on the SII mea-
sure; ps > .05). Figure 1, Panel A, provides a graphical rep-
resentation of these results, and Table 1 captures the crossover
values for all studies, beginning with this set of results. This
finding provides support for H1 and illustrates high-SII con-
sumers’ tendency to be persuaded by a persuasive message
backed by either high- or low-consensus information about
others’ behavior.
Discussion
Study 1a demonstrates that consumers’ response to high-
consensus versus low-consensus information is dependent on
SII. We found that lower-SII consumers were more persuaded
by a recommendation backed by high-consensus informa-
tion relative to one backed by low-consensus information.
In contrast, higher-SII participants showed similar levels
of persuasion when a recommendation was accompanied by
either high- or low-consensus information. The results of
Study 1a therefore suggest that the high-SII segment of con-
sumers may be a particularly attractive segment for policy
makers and marketers to target when advocating a new pro-
social behavior (e.g., installing low-flow shower heads to
conserve water) or attempting to build support for a new

prosocial initiative (e.g., charitable or recycling programs).
Targeting these people initially can be an effective way to
ultimately build the majority support needed to persuade
low-SII consumers.
While Study 1a provides initial support for H1, these findings
are subject to an alternate explanation: psychological reactance
on the part of low-SII consumers. Psychological reactance oc-
curs when a person feels threatened by a recommendation and
is therefore motivated to do the opposite of the recommenda-
tion in a bid to regain freedom that has been lost or threatened
(Brehm 1966). It is possible that low-SII people are particularly
likely to experience reactance to a low-consensus recommen-
dation, viewing this communication as a strong threat to their
ability to make a free choice, especially given that the low-
consensus information suggests a low-quality argument to those
high in SII. Therefore, the purpose of Study 1b is to provide
additional evidence for our demonstrated effect and dig deeper
into the process driving the result, including ruling out this
alternative explanation empirically. We also use a different op-
erationalization of high and low consensus. The operationali-
zation of low consensus used in Study 1a requires consumers to
make the inference that while “most people don’t” consider
labor practices, some people do (and, thus, there is low con-
sensus). Given that we expect high-SII consumers to primarily
attend to whether any consensus is present and not to devote
additional processing resources beyond simply noting whether
it is present, we use a simpler operationalization of low con-
sensus (that does not require an inference) in Study 1b and all
subsequent studies.
Figure 1. Consumer SII and Consensus Information for
Studies 1a and 1b
L
ab

or
A
tt
ri
bu
te
W
ei
gh
t
0
0.5
1.0
1.5
2.0
2.5
3.0
+2 SD
SII
High consensus Low consensus
–2 SD 2.96

A: Study 1a
V
eg
et
ab
le
s
on
G
ro
ce
ry
L
is
t
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
–2 SD +2 SD

SII
2.60
B: Study 1b
Study 1b
In Study 1b, we aim to replicate the effect demonstrated in
Study 1a and provide additional support for H1 using a different
product category and consensus cue manipulation. In our first
study, we used lexical cues (i.e., “most people”). In this study,
we use quantitative consensus cues similar to the method used
by Goldstein, Cialdini, and Griskevicius (2008). An additional
goal of this study is to provide initial evidence that attentional
differences are driving this observed effect.
Method
In Study 1b, 241 participants recruited through MTurk com-
pleted the study for a small monetary incentive. Study 1b used
an SII × consensus information (high vs. low) between-subjects
design, in which SII was a measured continuous variable and
consensus information was manipulated. Congruent with Study
1a, participants completed an IMC to detect whether they
followed directions (Oppenheimer, Meyvis, and Davidenko
2009). Eight participants failed the IMC three times and were
dropped from the analysis. Thus, Study 1b had a final sample of
233 participants (50% female; Mage = 36.7 years, age range
18–83 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 they
completed the focal task helped ensure that our experimental
manipulations did not influence this measure. After com-
pleting approximately ten minutes of unrelated filler tasks,
participants were asked to imagine that they had just arrived
at their local grocery store to purchase some food items for
the week. On entering the grocery store, participants imagined
seeing a PSA produced by the Organization for Healthy Eating,
a fictitious public health organization. In the high-consensus
information condition, participants read the following recom-
mendation from the organization:
Almost 76% of American grocery shoppers eat five servings of
vegetables a day. We recommend you join your fellow shoppers
and
consume at least five servings of vegetables a day.
Conversely, in the low consensus information condition,
participants read the following recommendation from the
organization:
Almost 26% of American grocery shoppers eat five servings of
vegetables a day. We recommend you join your fellow shoppers
and
consume at least five servings of vegetables a day.
After reading the PSA, participants were asked to put to-
gether a grocery shopping list for their trip. Each participant
compiled a list of 15 items from a total of 60 available grocery
items. 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 vegetable
items included on the list as our focal dependent variable.
In our conceptualization, we have argued that high-SII

consumers attend to the fact that consensus information is
present but place relatively less weight on the degree of con-
sensus provided. If those high in SII indeed place less emphasis
on consensus information, we would anticipate that they would
have a more difficult time recalling the consensus information
present in the PSA when compared with those lower in SII.
Therefore, after completing the grocery shopping task and sev-
eral filler items unrelated to the current hypotheses, all partici-
pants were asked to recall the consensus information shown in
the PSA by typing in the percentage they saw in the PSA. We
measured memory for this information to serve as a proxy for
participants’ attention given that greater attention should result
in
a stronger memory trace (Baddeley et al. 1984). Responses were
coded as either correct or incorrect.
Finally, given that the results of Study 1a could be explained
by reactance (Brehm 1966) on the part of low-SII people, par-
ticipants completed an eight-item measure of reactance to the
recommendation from the Organization for Healthy Eating
adapted from Hong and Faedda (1996), which was indexed for
analysis (a = .90, M = 2.48, SD = 1.10; sample items: “I resisted
the attempt of the OHE to influence me”; “The recommendation
from the OHE restricted my freedom of choice”; “I considered
the recommendation from the OHE to be an intrusion”).
Results
We first conducted a regression analysis with contrast-coded
consensus information, consumer SII (mean-centered for
Table 1. Consumer SII Crossover Values for Floodlight
Analyses
Significant SII Crossover
Values from the Floodlight Analyses Average SII Value for
Study

Study 1a £2.96 3.02
Study 1b £2.60 3.21
Study 2: No control £3.44 3.28
Study 3: Low attentional emphasis £2.18 3.38
Study 3: High attentional emphasis ‡5.06 3.38
Notes: The crossover values indicate the value of SII at which
the floodlight test reached statistical significance for each
study. The floodlight test highlights a range
of 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 labor
attribute weight between the high- and low-consensus
information groups. At SII values greater than 2.96, there was
no significant difference in labor
attribute weight between the experimental groups.
3Although some of these products may be better classified as
fruits from
a scientific perspective, they are typically classified as
vegetables by U.S.
consumers (Rupp 2015).
analysis), and the SII × consensus information interaction as
predictors of the number of vegetables included on participants’
shopping lists. There was no main effect of whether the rec-
ommendation 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 not
surprising given that organizations are less likely to be per-
ceived as significant others. However, consistent with the
results
of Study 1a, we found a significant interaction between consen-
sus information and consumer SII (t(1) = 2.40, p < .05, b = .25).
To better understand this interaction, we again used a
floodlight procedure (Hayes and Matthes 2009). This pro-
cedure revealed that participants scoring below an average
value of 2.60 on the seven-point SII included more vegetables
on their shopping lists in the high-consensus information
condition compared with the low-consensus information con-
dition (ps < .05). However, participants scoring above 2.60 on
the SII measure showed no difference in the number of vege-
tables included between the high- and low-consensus conditions
(ps > .05). Figure 1, Panel B, provides a graphical
representation
of the results. This finding offers additional support for H1 and
further demonstrates that people high in SII can be influenced
in the face of both high- and low-consensus information.
We next conducted a logistic regression analysis with
contrast-coded consensus information, consumer SII (mean-
centered for analysis), and the SII × consensus information
interaction as predictors of correctly recalling the consensus
information. There was no main effect of consensus informa-
tion 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 corre-
sponds to a 22% decrease in the odds of correctly recalling
the consensus information, in support of our theorization that
higher-SII consumers are less focused on the exact nature of the

consensus information. We also recorded the time participants
spent reading the PSA (M = 13 seconds) and found that time
spent processing the PSA (p > .10) did not differ by SII. This is
consistent with our theory that the differences in attention are
not
driven by the overall amount of attention paid to a persuasive
message but how this attention is allocated, with those high in
SII devoting less attention to the nature of whether consensus
information is high or low.
Finally,weconductedaregressionanalysiswithcontrast-coded
consensus information, consumer SII (mean-centered for analy-
sis), and the SII × consensus information interaction as
predictors
of reactance. All effects were nonsignificant (all ps > .50). Fur-
thermore, a process mediation analysis (Hayes 2013) confirmed
that reactance did not fully or partially mediate the focal effect.
Discussion
The results of Study 1b replicate Study 1a’s effects using a
different consensus information manipulation and consumer
context. We again demonstrate that consumers’ response to
low- and high-consensus information is dependent on SII. Fur-
thermore, Study 1b demonstrates that this effect holds when
the consensus information is presented in quantitative rather
than
lexical form. Finally, the results of Study 1b provide initial
insight into the process driving these results. Previously, we
argued that people high in SII actively process message in-
formation in the same way as people lower in SII, but the
former
are more vigilant to the presence of consensus information
rather than the exact level of consensus. Consistent with our
argument,weobservethat peoplehigher inSII processfor similar
lengths of time as do people lower in SII but are in fact less

likely
to accurately recall consensus information, irrespective of the
nature of the consensus information (i.e., high or low). In ad-
dition, we ruled out reactance as a driver of the observed effect.
We return to the role of attention in Study 3. However,in our
next
study we dig deeper into the role that consensus information
plays in high-SII consumers’ perceptions of argument strength.
Study 2
The purpose of Study 2 is to tease apart the influence of the
message source and consensus cues in the persuasion process.
Previously, we have argued that any consensus information
influences high-SII consumers’ perceptions of argument
strength. However, we have not demonstrated the importance of
consensus information beyond a simple source recommenda-
tion. Therefore, in Study 2 we include a control condition in
which no consensus information is provided.
Method
Two hundred forty-nine participants recruited using MTurk
participated in Study 2 in exchange for a small monetary in-
centive. Study 2 employed an SII ×consensus information (high
vs. low vs. control) between-subjects design, in which SII was
a measured continuous variable and consensus information
was manipulated. Consistent with Studies 1a and 1b,
participants
completed an IMC to identify whether they were following
directions (Oppenheimer, Meyvis, and Davidenko 2009). Two
participants failed the IMC three times and were removed from
theanalysis,leavingauseablesampleof 247people(46% female;
Mage = 35.0 years old, age range 19–68 years).
We designed Study 2 following one of the most compelling
recent demonstrations of the effectiveness of consensus in-
formation in a policy relevant setting, Goldstein, Cialdini, and

Griskevicius’s (2008) hotel field study. That research demon-
strated that using high-consensus information in a persuasive
message increased hotel guests’ participation in an environ-
mental conservation program (by giving guests information
about the number of previous guests who reused their towels)
when compared with traditional proenvironmental appeals.
To begin this study, all participants completed Bearden,
M = 3.28, SD = 1.08). After completing approximately ten
minutes of unrelated filler tasks, participants were asked to
imagine that they were staying seven nights in a hotel. Fur-
thermore, participants were asked to imagine that on entering
the hotel room they notice some literature provided by the hotel
chain related to environmentally responsible behaviors. In the
high-consensus information condition, participants saw the fol-
lowing recommendation from the hotel chain:
Almost 75% of our guests participate in our resource
conservation
program by reusing their towels more than once. Please join
your
fellow guests in this program to help save the environment by
reusing your towels during your stay.
In the low-consensus information condition, participants read
the following recommendation from the hotel chain:
Almost 25% of our guests participate in our resource
conservation
program by reusing their towels more than once. Please join
your

fellow guests in this program to help save the environment by
reusing your towels during your stay.
Finally, participants in the control condition viewed the fol-
lowing recommendation from the hotel chain:
Our hotel has a resource conservation program that involves
reusing
towels more than once. Please help save the environment by
reusing
your towels during your stay.
After the presentation of the hotel’s literature on environ-
mentally responsible behaviors, participants indicated their
likelihood of reusing their towels for two nights on a scale from
0 (“very unlikely”) to 100 (“very likely”).
Results
We measured how long participants spent reading the hotel’s
statement about environmentally responsible behaviors. On
average, participants spent 24 seconds reading, and, as in Study
1b, processing time did not differ by level of SII (p > .70).
No Control Group
We first tested for replication of our prior results, using con-
trast codes to compare the low-consensus and high-consensus
conditions. We conducted a regression analysis with contrast-
coded consensus information, consumer SII (mean-centered for
analysis), and the interaction between SII and consensus in-
formation as predictors of towel reuse likelihood. We found
a main effect of consensus information (t(1) = _2.35, p < .05,
b = _5.17) such that the likelihood of towel reuse was sig-
nificantly lower in the low-consensus condition when compared
with the overall study mean. Consistent with Study 1b, there
was no main effect of SII (t(1) = _.87, p > .30), which again is
not unexpected given that organizations are less likely to fill the

role of significant others. Most importantly, and consistent with
the results of Studies 1a and 1b, a significant interaction be-
tween consensus information and consumer SII emerged (t(1) =
2.26, p < .05, b = 4.78).
To understand the interaction between consensus infor-
mation and consumer SII, we again used the floodlight pro-
cedure advocated by Hayes and Matthes (2009). This procedure
revealed that participants scoring below an average value of
3.44 on the seven-point SII scale were more likely to reuse their
towels in the high-consensus information condition compared
with the low-consensus information condition (ps < .05).
However, high-SII people (participants who scored above 3.44
on the SII measure; ps > .05) showed no difference in their
likelihood of reusing towels between the high- and low-
consensus conditions. Figure 2, Panel A, provides a graphical
representation of the results. This finding provides additional
support for H1 and again affirms the persuasive influence of
both
high- and low-consensus information for people high in SII.
With Control Group
Next, we analyzed the results with the control group. We ap-
plied a regression analysis with two contrast-coded consensus
information variables (“high consensus”: control group vs.
high-
consensus information; “low consensus”: control group vs.
low-consensus information), consumer SII (mean-centered for
analysis), and the interaction between SII and the two con-
sensus information variables (high consensus and low con-
sensus) 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 code
emerged (t(1) = 2.74, p < .01, b = 6.22) such that, regardless of
SII, participants showed higher likelihoods of towel reuse when
viewing the high-consensus message compared with the con-
trol message. This result is consistent with prior research on
social proof and high-consensus effects (Cialdini 2009;
Cialdini,
Kallgren, and Reno 1990; Goldstein, Cialdini, and Griskevicius
2008) and further demonstrates the power of high-consensus
messages compared with standard control messages.
We find that the interaction between the high-consensus
contrast code and consumer SII was not significant (t(1) = _.12,
p > .90). However, we did find a significant interaction between
the low-consensus contrast code and consumer SII (t(1) = 2.19
Study 2
A: No Control Condition
B: With Control Condition
0
10
20
30
40
50
60
70
80
90
100

–2 SD +2 SD
L
ik
el
ih
oo
d
of
R
eu
se
T
w
o
N
ig
ht
s
SII
3.44
0
10

20
30
40
50
60
70
80
90
100
–2 SD +2 SD
L
ik
el
ih
oo
d
of
R
eu
se
T
w
o
N
ig
ht

s
High consensus
SII
Low consensus Control
p < .05, b = 4.52). This result demonstrates that as consumer
SII increases, participants showed a greater likelihood of
reusing
their towels when viewing the low-consensus message when
compared with the control message. Figure 2, Panel B, provides
a graphical representation of the results.
Discussion
The results of Study 2 provide several important insights. First,
we replicate the general effect found in Studies 1a and 1b. We
also find that consistent with previous research, both low- and
high-SII people found a high-consensus message more per-
suasive than a control message. However, high-SII people also
found the low-consensus message significantly more persuasive
than the control message. This result reaffirms our conceptu-
alization and suggests that high-SII people are sensitive to
consensus elements in a message (beyond a simple recom-
mendation) but do not differentiate between high and low con-
sensus levels.
Study 3
We have argued that high-SII people pay relatively less at-
tention to whether consensus information is high or low and

focus instead on the fact that any consensus information is
present in a message. If high-SII consumers’ failure to differ-
entiate between high- and low-consensus information is driven
by a weaker attention to the nature of consensus information,
externally drawing focus to the level of consensus information
should change the way that high-SII people react to high- versus
low-consensus cues. That is, increasing the salience of the spe-
cificlevel of consensusinformationwillsignal tohigher-SIIpeople
that this information is relevant in their decision making.
For this study, we therefore take an experimental causal-chain
approach to demonstrate our proposed process. By manipulating
bothour independentvariableandtheproposedprocessmeasure,
focal attention, this approach enables us to make inferences
about the mechanism driving our results (Spencer, Zanna, and
Fong2005).Furthermore,inthisstudy,weexaminepeople’sactual
donation behaviors to an environmental charitable organization.
Method
A total of 200 participants recruited using MTurk participated
in
Study 3 in exchange for a small monetary incentive. Study 3
employed an SII × consensus information (high vs. low) ×
attentional emphasis (low vs. high) between-subjects design, in
which SII was a measured continuous variable and consensus
information and attentional emphasis were manipulated. Con-
sistent with our previous studies, participants first completed
an IMC to identify whether they were following directions
(Oppenheimer, Meyvis, and Davidenko 2009). Three partici-
pants failed the IMC three times and were removed from the
sample, leaving 197participants(47% female; Mage = 33.9 years
old, age range 19–69 years).
To begin this study, all participants completed Bearden,
M = 3.38, SD = 1.25). After completing the measure of SII,

participants began the study, which consisted of several dif-
ferent tasks. First, participants were informed that they
would evaluate a series of charitable organizations. Each par-
ticipant read about five charitable organizations (the United
Way, Feeding America, the Task Force for Global Health, the
American Red Cross, and the American Cancer Society) and
evaluated each organization. This task was meant to orient
participants and prepare them for our focal investigation. After
rating these charitable organizations, participants completed
approximately 15 minutes of filler tasks. Finally, participants
were informed that they would be asked to evaluate one last
charitable organization. As a thank you for evaluating this final
charitable organization, participants were provided with a $.25
bonus. Each participant then evaluated the American Wood-
land Foundation (AWF), a fictional charity, which was mod-
eled after the American Forest Foundation. Participants saw the
charity’s web page, which included information on the mission,
vision, and values of the AWF, as well as a recommendation
from the manager of AWF advising each person to make
a donation.
In the high-consensus information condition, participants
were informed: “In previous studies, 75% of people made
a donation to this charitable organization. We recommend
you consider making a donation.” By contrast, in the low-
consensus information condition, participants saw the fol-
lowing information: “In previous studies, 5% of people made
a donation to this charitable organization. We recommend you
consider making a donation.”
In addition to manipulating the consensus information, we
also manipulated attention to the high- or low-consensus in-
formation by changing the spatial and visual presentation of the
message. Recent research has acknowledged the considerable
difference that ad positioning makes in garnering attention in

a digital environment (Sharethrough 2015; Stambor 2013). For
example, using eye-tracking technology, Sharethrough (2015)
found that native online advertisements (i.e., ads using the same
format and positioning as the focal content of a website) re-
ceived 52% more attention than banner advertisements in the
periphery. Similarly, advertisements placed in-stream on a web
page showed click-through rates 45 times greater than display
ads on the right margin of the page (Stambor 2013). Given these
findings, in the high–attentional emphasis condition, the high-
consensus or low-consensus information was placed in the web
page stream (vs. on the right margin of the page) and was a
larger font than the rest of the web page. Furthermore, the num-
erical information was in red, boldface font to create a visual
contrast (see Appendix A). In contrast, in the low–attentional
emphasis condition, the high- or low-consensus information
was placed in the right margin of the AWF web page and used
a font that was similar in size to the rest of the web page, and
the red font was not used for the numerical information (see
Appendix A).
After reviewing the web page, participants were asked if they
would like to donate any of their $.25 bonus to AWF. The
instructions were clear that the decision was up to the partic-
ipant, who could choose if and how much (s)he wanted to
donate. We used the amount donated to the AWF charity as our
dependent variable in this experiment. Participants who donated
a portion of their bonus to the charity received the remaining
balance (if any) as their bonus.
Results
Consistent with prior studies, we measured the time participants
spent reading the AWF web page. Participants spent 40 seconds

on 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-coded
consensus information, contrast-coded attentional emphasis,
consumer SII (mean-centered for analysis), and all possible
interactions as predictors of donations. All main effects and
two-
way interaction effects were nonsignificant (ps > .15). However,
as we predicted, results revealed a significant three-way inter-
action among consensus information, attentional emphasis, and
SII (t(1) = _2.95, p < .01, b = _.16).
To further investigate this three-way interaction, we sepa-
rately examined the impact of consensus information and con-
sumer SII within the low– and high–attentional emphasis
conditions. In both conditions, we conducted separate
regression
analyseswithconsensusinformation,consumer SII, andtheSII ×
consensus information interaction as predictors of donations.
In the low–attentional emphasis condition, the effect of
consensus information (t(1) = _.58, p > .50) and consumer SII
were not significant (t(1) = _.35, p > .70). However, we found
a significant interaction between consensus information and
SII (t(1) = 2.23, p < .05, b = .02). A floodlight analysis (Hayes
and Matthes 2009) revealed that participants scoring below an
averagevalue of 2.18on the seven-point SII scale donatedmore
money to AWF in the high-consensus information condition
compared with the low-consensus information condition (all
values less than 2.18; ps < .05). In contrast, the high- and low-
consensus cues did not generate different donation amounts for
high-SII participants (people who scored above 2.18 on the SII
measure; ps > .05). Figure 3, Panel A, provides a graphical

representation of the interaction. This result is conceptually
consistent with the findings of prior studies and provides sup-
port for H2a.
In the high–attentional emphasis condition, the effect of
consensus information (t(1) = _.69, p > .40) and consumer
SII were not significant (t(1) = _.36, p > .70). However,
we found a significant interaction between consensus in-
formation and SII (t(1) = _1.92, p = .05, b = _.01). Most
importantly, a floodlight analysis (Hayes and Matthes 2009)
revealed that participants scoring above an average value of
5.06 on the seven-point SII scale donated more to AWF in
the high-consensus information condition compared with
the low-consensus information condition (all values greater
than 5.06; ps < .05). Figure 3, Panel B, provides a graphical
representation of the results. This finding provides support
for H2b, demonstrating that when attentional emphasis is
added to consensus information, people higher in SII can
effectively differentiate between high- and low-consensus
information.
Discussion
Study 3 demonstrates that attentional emphasis highlighting
consensus information moderates the interactive effect of
SII and consensus information on persuasion. By showing
this moderation, the results of Study 3 provide evidence of
our proposed process (Spencer, Zanna, and Fong 2005), in
support of our theory that attentional differences drive high-
SII consumers’ failure to differentiate between high- and
low-consensus information. The results of Study 3 therefore
suggest that if policy makers want to discourage a given
behavior by pointing out how few people engage in the
behavior (e.g., smoking), it is critical that they use these type
of attentional cues to ensure that high SII consumers re-
spond in the desired manner. Importantly, public policy
makers can easily implement an attentional intervention in

the type of PSA used as the stimulus for this study if the goal
is to ensure that high-SII consumers recognize the difference
between the behavior of a majority and that of a minority.
Although this particular study was done in an online con-
text, similar techniques for emphasizing consensus in-
formation (e.g., with different font sizes and color) can be used
in offline contexts.
While the results of Study 3 demonstrate the importance of
attentional cues to help high-SII consumers effectively differ-
entiate between high- and low-consensus information, we note
that our manipulation seems to eliminate low-SII consumers’
differentiation among consensus information levels. It is pos-
sible that highlighting consensus information may have acti-
vated a strong persuasion knowledge schema (Friestad and
Study 3
A: Low Attentional Emphasis
B: High Attentional Emphasis
0
.05
.10
.15
.20
.25
–2 SD +2 SD

A
m
ou
nt
D
on
at
ed
($
)
SII
2.18
0
.05
.10
.15
.20
–2 SD +2 SD
A
m

ou
nt
D
on
at
ed
($
)
SII
5.06
Wright 1994) in low-SII consumers, leading them to be
skeptical
of the message.
Study 4a
Although work that focuses on an individual difference as
a moderator often provides important theoretical insights, one
critique is that it is challenging for this type of research to
shape
practice. How can policy makers or charities reach people of
various trait characteristics? To support a legitimate discussion
of the practical implications of our work for policy makers,
NGOs, and managers, we sought reliable demographic and

psychographic correlates of SII—observable variables or be-
haviors that could allow for the identification and targeting of
people at different levels of SII. To do so, we conducted
a survey of 582 consumers, using MTurk, who completed the
survey in return for a nominal payment (Mage = 31.3 years, age
range 18–70 years; 44% female). To begin, all participants
completed Bearden, Netemeyer, and Teel’s (1989) 12-item
measure of SII, which was indexed for analysis (a = .92, M =
3.12, SD = 1.15). Next, participants completed items capturing
a wide variety of focal constructs. We chose these behavioral,
psychographic, and demographic constructs for both practical
and theoretical reasons, described next. The measures used
appear in Appendix B.
Money Management
If better or worse money managers consistently vary in
SII, managers of various types of products (i.e., debt col-
lection services vs. wealth-management services) or
policy makers (who may be attempting to motivate people
with poor money management skills) might choose to use
consensus information and/or attentional cues differently.
Prior research has established that consumers’ financial
knowledge and constraints affect both their financial
planning (Morrin, Broniarczyk, and Inman 2012; Xiao et al.
2011) and the type of products they purchase (Cheema
and Soman 2006; Soman and Cheema 2002). Similarly,
financial management has been linked to different de-
mographic and personality characteristics (Norvilitis et al.
2006); thus, we included a measure of money management
to determine whether it is significantly related to consumer
SII.
Political Orientation and Nationalism
A rich store of research has established the geographic dis-
persion of people of different political orientations. For
example,

states are commonly known as more “red” (conservative) or
“blue” (liberal). Furthermore, data on political orientation can
also be captured longitudinally and at district or local levels.
Thus, if people of different SII levels can be identified on the
basis of political orientation, we have a large amount of in-
formation that can be used to geotarget messages and shape
persuasive communications accordingly. In addition, under-
standing whether SII is related to political orientation may
enable us to make contributions directly to the literature on
political communications, an arena in which consensus in-
formation has been used with varying degrees of success
(Gerber and Rogers 2009). We therefore measured individual
political orientation and nationalism.
Responsible Consumerism and Resource
Conservation
Policy makers and marketers are increasingly recognizing
environmentalism and social responsibility as an important
domain both for the introduction of new products and for
encouraging positive behavior change (Banerjee, Iyer, and
Kashyap 2003; Kronrod, Grinstein, and Wathieu 2012; Luchs
et al. 2010; Prothero et al. 2011). In addition, some work has
acknowledged environmental conservation attitudes as a use-
ful profiling and segmentation basis (Laroche, Bergeron,
and Barbaro-Forleo 2001; Straughan and Roberts 1999). Fur-
thermore, given the importance of consensus information in
encouraging conservation behaviors (as in Goldstein, Cialdini,
and Griskevicius 2008), our findings may have implications for
how prior research findings are implemented. Accordingly, we
included measures of responsible consumerism and resource
conservation.
Individual Lifestyles
Prior work has established the importance of individual life-
styles and habits in both marketing (Burroughs and Rindfleisch
2002) and public policy domains (Verplanken and Wood 2006).

Therefore, we also included measures of individual lifestyle,
religious habits, life satisfaction, and health perceptions, inclu-
ding information such as frequency of exercise, frequency of
diningout,hours spent pursuing ahobby,smoking habits, general
feelings of stress, feelings of religiosity, and general
satisfaction
with life, to determine whether any of these values, activities,
and
lifestyles are significantly correlated with consumer SII.
Media Habits
Both marketers and policy makers identify and target indi-
vidual consumers on the basis of these consumers’ media habits
and consumption. It is still imperative to use both traditional
channels of communication (i.e., television and print adver-
tising) and newer channels (i.e., the Internet and social net-
works) to deliver content and messages to a relevant and highly
selective market segment. Therefore, we also included measures
of media usage such as television and Internet habits.
Demographics
Finally, we also included measures of demographic variables.
These included age, gender, marital status, and educational
background.
Results
The average score on SII was 3.12 in the overall (n = 582)
sample. To provide background on the pervasiveness of high
SII in the general population, we assessed the average SII in the
top quintile (n = 123) of SII. The average SII value for these
123
people was 4.81 (SD = .46; range 4.25–6.33). Furthermore,
37% of our sample fell above an SII value of 3.44, the highest
crossover value from our experiments. These findings sug-
gest that higher-SII consumers are not an inconsequential group

and make up a substantial number of people in the general
marketplace.
Next, we discuss the results of our associative analysis be-
tween SII and the demographic, psychographic, and behavioral
measures previously highlighted. For continuous measures, we
conducted a regression analysis with responses to the afore-
mentioned measures as predictors of consumer SII. To assess
multicollinearity, we evaluated variance inflation factors for all
continuous predictors in the model. All variance inflation
factors were well below a value of 10, indicating no issues with
multicollinearity (Kutner et al. 2005).
For categorical measures, we ran analyses of variance with
a Bonferroni correction for multiple comparisons. Results
revealed that higher SII is associated with better ability to
manage money (b = .12, p < .01), a more liberal as opposed to
conservative political orientation (b = _.07, p < .05), consumer
behaviors that are environmentally responsible (b = .16, p <
.001), and strong feelings of nationalism (b = .21, p < .001).
Regarding lifestyle and media habits, higher SII was positively
associated with prevalence of dining out in a restaurant (b =
.07,
p < .05), time spent on the Internet for fun (b = .01, p < .05),
and
time watching cable television (b = .02, p < .05). Notably,
consumers who do not smoke also showed higher levels of SII
compared with consumers who do smoke (p < .001).
Perhaps of most use to marketers and policy makers, we
also found that age was negatively associated with SII (b =
_.03,

p < .001) and that men showed higher levels of SII when
compared with women (p < .01). In addition, single consumers
had higher SII than married consumers (p < .001).
To enhance these findings, we applied an index procedure
similar to the one employed by the Nielsen company to
identify measures likely to be successful in targeting high-SII
consumers (ACNielsen 2006). For this analysis, we compared
the overall mean (n = 582) with the mean of those in the top
quintile of SII (n =123) for each measure.Measures inwhich the
top SII quintile was greater than or less than 15% of the overall
mean are likely to be successful in targeting higher-SII con-
sumers. Using this procedure, the results revealed that nation-
alism, frequency of eating out, time spent on the Internet for
fun,
time spent watching cable television, and age are all measures
that can be used to effectively target higher-SII consumers.
Study 4b
In Study 4a, we outline important psychographic, demo-
graphic, and behavioral characteristics that can be helpful in
identifying and targeting high-SII people. The purpose of
Study 4b is to highlight policy-relevant behaviors that are
currently low consensus and therefore to offer useful avenues
for policy makers to employ low-consensus information aimed
at high-SII consumers. Thus, Study 4b pinpoints additional
practical applications of our work for policy makers and
managers.
We used MTurk to survey 251 consumers, who provided
responses in return for a nominal payment (Mage = 36.8 years,
age range 18–83 years; 54% female). All participants read
a series of 35 behaviors often encouraged by policy makers,
NGOs, and governmental agencies (see Appendix C). Partic-
ipants were asked to read each behavioral statement and
indicate

their level of agreement or disagreement on a scale from 1
(“strongly disagree”) to 7 (“strongly agree”).
Results
To identify policy relevant behaviors to which our work might
be most fruitfully applied, we assessed the means for each of
the
35 statements and compared each value with the scale midpoint.
Means that are significantly lower than or no different from the
scale midpoint are likely to represent low-consensus behav-
iors, such that messages cannot honestly say that a majority of
consumers currently engage in an activity. Conversely, those
means that are significantly greater than the scale midpoint are
likely to be high-consensus behaviors, such that information
could be presented arguing that a majority of consumers engage
in a behavior. As Appendix C outlines, this analysis suggests
a wide range of applications for our findings: behaviors such as
taking reusable bags to the grocery store (M = 4.12); installing
low-flow shower heads at home (M = 3.34); considering
manufacturers’ labor practices during product purchase (M =
3.93); eating five to six servings of fruits and vegetables a day
(M = 3.92); getting a yearly flu shot (M = 3.23); buying fair-
trade products (M = 4.11); buying organic foods (M = 4.04);
donating money to social (M = 3.78), environmental (M = 3.31),
and religious charities (M = 2.90); buying carbon offsets (M =
2.33); and purchasing non-GMO foods (M = 4.03) are all likely
to be low-consensus behaviors. In this range of domains, our
results suggest that policy makers do not need to avoid sharing
consensus information if they want to persuade high-SII con-
sumers. Rather, the simple information that some consumers
engage in the focal action should be persuasive to the types of
consumers profiled in Study 4a.
We also measured SII in this sample to provide further
characterization of its prevalence in the population. The av-
erage score on SII was 3.41 in the overall sample (n = 251). The

average SII value for those in the top quintile of people (n = 48)
was 5.52 (SD = .55; range 4.83–7.00), Furthermore, 48% of
our sample fell above an SII value of 3.44, the highest cross-
over value from our experiments. These findings are consis-
tent with those of Study 4a in indicating that consumers high in
SII make up a substantial number of people in the general
marketplace.
Discussion: Studies 4a and 4b
The results of Studies 4a and 4b offer insight into identifying
higher-SII consumers and policy-relevant behaviors that might
currently be low consensus. Study 4a suggests that higher-SII
consumers may be relatively easy to reach because they are
more likely to view Internet and television ads. Furthermore,
higher-SII consumers’ propensity to eat out often may make
them an important target for nutritional interventions such as
those managed by the U.S. Department of Agriculture (e.g.,
http://www.choosemyplate.gov/supertracker-tools/supertracker.
html), an especially important intervention given that eating
five
or six servings of vegetables a day is currently a low-consensus
behavior. Finally, the finding that people higher in SII tend to
be younger suggests that social media sites might be attractive
platforms through which to reach them.
Study 4b identifies many critical behaviors important to
policy makers that may currently lack majority support; there-
fore, these are attractive categories to promote to higher-SII
people. For example, our results revealed several environmental
(using reusable grocery bags, installing low-flow shower heads,
http://www.choosemyplate.gov/supertracker-
tools/supertracker.html
http://www.choosemyplate.gov/supertracker-

tools/supertracker.html
buying carbon offsets, purchasing non-GMO foods), health
(eating five to six fruits and vegetables per day, getting a yearly
flu shot, buying organic foods), and ethical (considering
manufactures’ labor practices, buying fair-trade products, do-
nating to charities) behaviors that might benefit from our
findings. Our theory suggests that if higher-SII consumers can
be persuaded to be responsive in these low-consensus situations,
they may well form consensus for others—an implication we
explore next.
General Discussion
Prior research has recognized the benefits of building and em-
phasizing high consensus as a means to influence consumers
(Cialdini 2009; Goldstein, Cialdini, and Griskevicius 2008).
Highlighting the persuasive powers of consensus information,
business bloggers and writers have continually recommended
that organizations invest in generating majority support to in-
crease compliance (e.g., Dowdeswell 2013; Zych 2013).
However, the present work demonstrates that for certain
consumers, an investment in consensus creation may be
unnecessary to persuade them to change their behaviors. We
show that high-SII people evaluate argument strength without
considering whether consensus levels are low or high, leading
them to conform with recommendations in both high- and
low-consensus situations.
This work provides several novel theoretical insights. First,
we find that for consumers high in SII, any consensus in-
formation provides enough evidence to persuade. Therefore,
this work extends prior theory that established high-SII con-
sumers’ tendency to emulate others (Bearden, Netemeyer, and
Teel 1989), fit in, and avoid attention (Batra, Homer, and Kahle
2001; Wooten and Reed 2004), showing that the drive of high-

SII people to do so can result in situations in which they fail
to differentiate between high- and low-consensus cues. As such,
we find that high-SII consumers may comply with positions
deemed unlikely by prior research (Cialdini 2009; Cialdini,
Kallgren, and Reno 1990, 1991). Second, our research suggests
variations in SII as the reason why people can occasionally be
persuaded by low-consensus information, beyond situations in
which sources of influence are clearly identified (Asch 1951;
Maass and Clark 1984; Moscovici 1985; Wood et al. 1994). Our
findings locate this tendency in an individual attentional failure:
high-SII consumers fail to pay attention to the nature of the
con-
sensus information and instead focus solely on the presence of
consensus cues.
Furthermore, our application of two types of consensus cue
presentations (i.e., lexical in Study 1a and quantitative in
Studies 1b, 2, and 3) demonstrates that the outlined effect holds
across multiple conditions. This is an important finding be-
cause it suggests that marketers and public policy makers can
display consensus information in either a lexical or quantitative
manner and still achieve the desired effect. Finally, we show
that high-SII consumers are equally persuaded by information
about other consumers that is high- or low-consensus re-
gardless of whether the information is presented as a direct
recommendation from another consumer (Study 1a), a per-
suasive message from a for-profit firm (Study 2), or a per-
suasive communication from a nonprofit organization (Studies
1b and 3). Similarly, we demonstrate the effect using a variety
of dependent variables including attribute weights (Study 1a),
planned shoppingbehaviors (Study 1b), behavioral likelihoods
(Study 2), and real donation behavior (Study 3).
How Can This Research Inform Policy, Practice,
and Consumers?

Our experimental studies demonstrate that even without
majority
support, public policy makers and marketers can highlight the
actions of a minority of relevant others and still persuade high-
(but not low-) SII consumers. Furthermore, using both demo-
graphic and lifestyle variables, we find that SII is likely to be
a targetable characteristic. As such, our research is consistent
with
previous work exploring the intersection of marketing and
public
policy that suggests that SII and other personality traits can be
successfully used to segment the market to more effectively de-
velop appropriate interventions to reduce undesirable behaviors
suchas teensubstanceabuse (Rose, Bearden, and Manning 1996)
or to better understand consumer response to, and interest in,
prosocialbehaviorssuchassocialentrepreneurship(Wood2012).
In the following sections, we discuss the specific implications
of our findings. In addition, to help policy makers manage the
use of consensus information in communications, we developed
a decision table based on two important factors: (1) the com-
position of the target market (i.e., predominantly low- or high-
SII consumers) and (2) the nature of the consensus information
available to policy makers (i.e., low- or high-consensus in-
formation). Table 2 highlights situations in which the use of
consensus information may be helpful and situations in which
the usefulness of consensus information may be limited.
Encouraging Nonmajority Behaviors
Our results can provide considerable guidance for policy makers
aiming to encourage consumers to engage in nonmajority (i.e.,
low-consensus) behaviors, such as environmentally friendly
behaviors or other prosocial actions like those considered in our
experiments and highlighted in Study 4b. Our findings indicate
that marketers can first target high-SII consumers who we have
demonstrated to be particularly sensitive to any information on

the actions of other consumers, regardless of whether the
actions
of others are consistent with a majority or minority. This initial
targeting of high-SII consumers can help establish support for
the
behavior, which can later be used to influence low-SII people.
As we highlight in Study 4b, there are several important,
policy-relevant behaviors that might be bolstered using our
findings. Consistent with our results, many important public
health and environmental behaviors currently demonstrate low
consensus. For example, in 2015 the Centers for Disease
Control and Prevention (CDC) noted that only 43% of adults
over the age of 18 received a seasonal flu vaccination
(CDC 2015). Furthermore, a recent Gallup poll found that
45% of Americans actively try to include organic foods in their
diets (Riffkin 2014). Similarly, in the first quarter of 2015,
electric and hybrid vehicles accounted for 2.7% of all new car
sales in the United States (Edmunds 2015). In addition, it is
estimated that consumers purchase carbon offsets on only 2% of
international flights (Gallucci 2014). Finally, the CDC notes
that
less than 9% of Americans consume the recommended two to
three cups of vegetables per day (Moore and Thompson 2015).
All of these behaviors, while critically important for the
preservation of our environmental resources or improvement
of public health, are currently low consensus. Therefore, our
results suggest that policy makers and governments can target
high-SII consumers and highlight the actions of the minority of
consumers already engaging in these behaviors. After per-

suading high-SII consumers, policy makers can then begin to
employ norms-based appeals in persuasive messages, leading
to conformity among low-SII consumers as the power of social
proof becomes overwhelming.
Building Support for a New Program or Initiative
As in the previous examples, our results also demonstrate that
policy makers introducing a new program or initiative may find
it easier to first persuade higher- as opposed to lower-SII
consumers. If people high in SII form a support base, they can
help build a strong-enough norm to persuade consumers lower
in SII. The ability to target and persuade high-SII consumers
even without majority support is critical because chance plays
a large part in popularizing new initiatives. Therefore, much in
the same way that innovators and early adopters are critical
targets for innovation diffusion through the marketplace
(Rogers 1995), high-SII consumers may be essential early
targets for any new program. This is likely to be especially true
for new programs that are particularly prosocial or for con-
sumers’ well-being. These programs may have a difficult time
finding support otherwise, so targeting high-SII consumers may
be a particularly effective way to build long-term support for
prosocial, proenvironmental, or health-focused initiatives.
Consumer Welfare
Along with informing communications strategies for both
public policy makers and marketers, our results can also be
valuable in enhancing consumer welfare. Prior research has
highlighted high-SII consumers’ desire to blend into the crowd
and refrain from drawing attention (Wooten and Reed 2004);
however, our results suggest that high-SII people’s failure to
note whether information on relevant others is high or low
consensus may lead to behaviors that create differentiation
from others. Therefore, high-SII consumers must be certain to
process the magnitude of consensus cues rather than only

noting that consensus information is present. These consumers
can benefit from asking themselves whether an advocated
position or behavior is consistent with a majority or minority
before conforming. For example, high-SII consumers pur-
chasing products on a website such as Amazon.com must be
aware of their tendency to overlook whether consensus in-
formation is high or low while shopping. If they can do so, they
will be able to better recognize that a product with a four-star
rating from one consumer is very different from a product with
a four-star rating from 1,000 consumers.
Furthermore, although we have focused on policy makers’
and marketers’ desire to prompt certain actions, they may also
want to persuade consumers to refrain from certain negative
behaviors. It maybe detrimental to expose higher-SII consumers
to information that even a minority of people have, for example,
cheated on their taxes or engaged in binge drinking. As such, if
low-consensus information is used to dissuade consumers
from behaving in a certain way, a substantial amount of
attention should be drawn to the low-consensus nature
of the behavior using external prompts, visual devices, or
explicit framing, consistent with our findings in Study 3. This
recommendation is critical for the execution of social norms
marketing, in which persuasive messages often highlight that
only a minority engages in a specific negative behavior (e.g.,
drinking, smoking).
Limitations and Further Research
While this research extends our understanding of consumers’
response to high consensus and low consensus information,
several avenues remain for future inquiry. First, further research
can assess the interplay among consensus cues, consumer SII,
and the strength of the message source. Our studies demon-
strated that high-SII people fail to differentiate between high-
and low-consensus information when the communication
comes from a moderately strong source. However, we did

not manipulate the strength of the message source, a vari-
able that prior research has shown can significantly influ-
ence compliance with a recommendation (Naylor, Lamberton,
Table 2. Market Composition and Level of Consensus: When
Should We Use Consensus Information?
Composition of Target Market
Level of Consensus Information
Low Consensusa High Consensusb
Predominantly low SII (approximately
45%–55% of population across Studies 4a
and 4b; more likely to be older, female,
and married)
Presentation ineffective: Policy makers
should withhold low-consensus information
from persuasive messages.
Presentation effective: Policy makers should
use high-consensus information in persuasive
messages. Those low in SII will find this
information to be an important part of
argument strength.
Predominantly high SII (approximately
35%–45% of population across Studies 4a
and 4b; more likely to be younger, male,
and single)
use low-consensus information in persuasive
messages. Those high in SII will find this

argument strength.
use high-consensus information in persuasive
messages. Those high in SII will find this
argument strength.
aFor example, installing low-flow shower heads, getting an
annual flu shot, purchasing carbon offsets, and consuming
recommended daily servings of fruits and vegetables.
bFor example, refraining from smoking, paying taxes, buying
energy efficient lightbulbs, not texting while driving, taking
prescribed medications, getting
recommended sleep each night.
http://Amazon.com
and Norton 2011). High-SII consumers might be more likely
to scrutinize messages provided by weaker or less credible
sources, potentially creating situations in which high-SII
people effectively differentiate between high- and low-
consensus information if the identity of the message source
leads to greater message elaboration.
Second, we only explored messages that consumers are likely
to process using central route processing. Additional research is
needed to investigate how consumers of varying levels of SII
respond to consensus information when a message is processed
peripherally. We speculate that low-SII consumers may behave
like high-SII consumers under peripheral processing because
they fail to attend to this important determinant of argument

strength as a result of their lack of motivation. However, we
leave this empirical question to further research.
Third, we demonstrated that high-SII consumers are sen-
sitive to consensus elements in a message beyond a simple
recommendation but fail to differentiate between high and low
consensus levels (i.e., Study 2 results); however our control
message lacked an element of social inclusion (e.g., “join your
fellow guests”). Consequently, it is plausible that high-SII
participants were persuaded by these short social compliance
statements in the manipulated messages. Therefore, further
research is needed to investigate high-SII consumers’ reactions
to social inclusion cues in messages and compare their ef-
fectiveness with consensus information.
Fourth, additional research can evaluate the interplay among
SII, consensus information, and cultural orientation (e.g.,
Hofstede 2001). In all of our analyses, we employ a very U.S.-
centric perspective; however, cross-cultural variability has been
recognized as an important construct affecting persuasion
(Aaker and Maheswaran 1997). Therefore, dimensions of cul-
tural variability such as individualism–collectivism may play
a critical role in consumers’ response to high- and low-
consensus
information. For example, consumers from cultures character-
ized by high collectivism might be less likely to overlook the
nature of consensus information because their cultural orienta-
tion emphasizes connectedness and focuses on relationships
(Triandis 1989). Therefore, consumers from highly collective
cultures might have a stronger incentive to pay attention to the
nature of consensus information as high or low.
Finally, additional research is needed to assess different
ways to highlight high-consensus cues for high-SII consumers.
Although we have shown that high-SII consumers can dis-
criminate between high- and low-consensus information

when their attention is prompted, our manipulation in Study 3
demonstrated only one way to achieve this goal. Consumers
may find it odd or intrusive for policy makers to highlight
consensus information using our approach. In addition, our
attentional manipulation eliminated low-SII people’s ability to
differentiate between low- and high-consensus information.
Therefore, policy makers who want to get the most out of
consensus cues would likely be interested in additional ways to
highlight consensus information.
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Appendix A. Study 3 Stimuli

Appendix A. Continued.
Appendix B. Study 4a Measures
Category Number of Items Sample Items
Money Management (Point of Purchase Advertising
International Shopper Engagement Study 2012)
5 • I have a system set up for managing my money.
• I have trouble keeping my finances organized.
Political Orientation and Nationalism (Pratto,
Sidanius, Stallworth, and Malle 1994)
8 • Please indicate your political views for each
category by checking one of the following scale
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“slightly liberal,” 4 = “middle of the road,” 5 =
“slightly conservative,” 6 = “conservative,” and 7 =
“very conservative.” The three issue categories used
were “foreign policy issues,” “economic issues,”
and “social issues.”
• Generally, the more influence America has on other
nations, the better off they are.

Responsible Consumerism and Resource
Conservation (Olli, Grendstad, and Wollebaek 2001)
9 • I choose products that carry an environmental seal of
approval.
• I avoid products with a lot of unnecessary
packaging.
• I avoid using a car for environmental reasons.
Satisfaction with Life (Diener, Emmons, Larsen, and
Griffin 1985)
5 • In most ways my life is close to my ideal.
• The conditions of my life are excellent.
Lifestyle, Religiosity, and Health 6 • How many days a week do
you exercise?
• How many days a week do you dine out?
• How stressed do you feel during a normal week?
• How religious or spiritual are you?
Media Habits 8 • How many hours per week do you watch TV?
• How many hours per week do you use the Internet?
Demographics 6 • Gender, age, employment status, ethnic
background,
educational background, and household size.
Appendix C. Study 4b Low- and High-Consensus Behaviors
Behavior M SD
Low-Consensus Behaviors
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I consider manufacturers’ labor practices when purchasing

products 3.93 1.91
I donate money to environmental charities 3.31*** 2.00
I donate money to religious charities 2.90*** 2.17
I donate money to social charities 3.78* 2.09
I eat five to six serving of fruits and vegetables a day 3.92 2.02
I frequently take reusable bags to the grocery store 4.12 2.31
I get a flu shot every year 3.23*** 2.49
I have installed low flow shower heads in my home to conserve
water 3.34*** 2.18
I often buy fair trade products 4.11 1.84
I often buy organic foods 4.04 1.99
I purchase carbon offsets to compensate for my greenhouse gas
emissions 2.33*** 1.77
I see my doctor once a year to get a physical 4.19 2.36
High-Consensus Behaviors
I buy energy efficient light bulbs 5.39*** 1.77
I do not smoke 5.74*** 2.17
I drink eight eight-ounce glasses of water a day 4.66*** 2.04
I get 30 minutes of exercise at least five days a week 4.29**
2.19
I get at least seven hours of sleep each night 5.15*** 1.74
I often buy locally grown produce 4.45*** 1.83
I often buy products with recycled content 4.84*** 1.75
I often read food labels when grocery shopping 5.24*** 1.88
I often turn off the lights after I leave the room to conserve
energy 6.02*** 1.36
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