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Morality and Conformity: The Ach Paradigm
Applied to Moral Decisions

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Social Influence

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Morality and conformity: The Asch paradigm

applied to moral decisions

Payel Kundu & Denise Dellarosa Cummins

To cite this article: Payel Kundu & Denise Dellarosa Cummins (2013) Morality and
conformity: The Asch paradigm applied to moral decisions, Social Influence, 8:4, 268-279, DOI:
10.1080/15534510.2012.727767

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Morality and conformity: The Asch paradigm
applied to moral decisions

Payel Kundu and Denise Dellarosa Cummins
University of Illinois at Urbana-Champaign, Champaign, IL, USA

Morality has long been considered an inherent quality, an internal moral
compass that is unswayed by the actions of those around us. The Solomon
Asch paradigm was employed to gauge whether moral decision making is
subject to conformity under social pressure as other types of decision making
have been shown to be. Participants made decisions about moral dilemmas
either alone or in a group of confederates posing as peers. On a majority of
trials confederates rendered decisions that were contrary to judgments
typically elicited by the dilemmas. The results showed a pronounced effect
of conformity: Compared to the control condition, permissible actions were
deemed less permissible when confederates found them objectionable, and
impermissible actions were judged more permissible if confederates judged
them so.

Keywords: Moral judgment; Conformity; Asch; Decision making.

Traditional theories of moral psychology endorsed the Kantian view that
moral judgments are the outcome of conscious deliberation based on moral
rules, an internal ‘‘moral compass’’ (Kant, 1785, 1787; Kohlberg, 1969).
However, recent studies have shown that moral judgment can be strongly
swayed by seemingly irrelevant contextual factors. People judge actions as
more morally wrong if they are primed to feel disgust before making a moral
judgment (Schnall, Benton, & Harvey, 2008; Schnall, Haidt, Clore, &
Jordan, 2008), while priming positive emotions makes moral transgressions
sometimes appear more permissible (Valdesolo & DeSteno, 2006). Marked
order effects have also been reported in which the judged moral
permissibility of a dilemma varies as a function of the nature of the

Address correspondence to: E-mail: dcummins@illinois.edu

The authors thank Andrew Higgins, Joseph Spino, and John Clevenger for their assistance in

conducting the experiment. This work was supported by research funds provided by the

University of Illinois.

SOCIAL INFLUENCE, 2013
Vol. 8, No. 4, 268–279, http://dx.doi.org/10.1080/15534510.2012.727767

© 2013 Taylor & Francis

Morality and conformity: The Asch paradigm
applied to moral decisions
Payel Kundu and Denise Dellarosa Cummins
University of Illinois at Urbana-Champaign, Champaign, IL, USA
Morality has long been considered an inherent quality, an internal moral
compass that is unswayed by the actions of those around us. The Solomon
Asch paradigm was employed to gauge whether moral decision making is
subject to conformity under social pressure as other types of decision making
have been shown to be. Participants made decisions about moral dilemmas
either alone or in a group of confederates posing as peers. On a majority of
trials confederates rendered decisions that were contrary to judgments
typically elicited by the dilemmas. The results showed a pronounced effect
of conformity: Compared to the control condition, permissible actions were
deemed less permissible when confederates found them objectionable, and
impermissible actions were judged more permissible if confederates judged
them so.
Keywords: Moral judgment; Conformity; Asch; Decision making.
Traditional theories of moral psychology endorsed the Kantian view that
moral judgments are the outcome of conscious deliberation based on moral
rules, an internal ‘‘moral compass’’ (Kant, 1785, 1787; Kohlberg, 1969).
However, recent studies have shown that moral judgment can be strongly
swayed by seemingly irrelevant contextual factors. People judge actions as
more morally wrong if they are primed to feel disgust before making a moral
judgment (Schnall, Benton, & Harvey, 2008; Schnall, Haidt, Clore, &
Jordan, 2008), while priming positive emotions makes moral transgressions
sometimes appear more permissible (Valdesolo & DeSteno, 2006). Marked
order effects have also been reported in which the judged moral
permissibility of a dilemma varies as a function of the nature of the
Address correspondence to: E-mail: dcummins@illinois.edu
The authors thank Andrew Higgins, Joseph Spino, and John Clevenger for their assistance in
conducting the experiment. This work was supported by research funds provided by the
University of Illinois.

dilemmas that preceded it (Nichols & Mallon, 2006), an effect that was
replicated among expert moral reasoners (Schwitzgebel & Cushman, 2012).

One contextual factor that has not been adequately investigated is that of
social consensus on moral decision making. There has been a plethora of
research on decision-making conformity and the situations in which it can
be induced. Perhaps the most famous are the classic studies conducted by
Solomon Asch (1956) using simple visual discrimination. Asch required
participants to choose which of three lines of different lengths matched the
length of a target line. Participants made decisions in a group context which
included six to eight people, and all but one person was a confederate of the
experimenter. Over the course of 18 trials the confederates gave correct
answers on only 6 trials. Asch found that, while participants made errors on
fewer than 1% of trials when deciding alone, they made errors on 37% of
trials in the group condition.

Although numerous studies have been conducted since the publication of
Asch’s classic paper, the majority have as their primary aim identifying the
motivations underlying conforming behavior (see Cialdini & Goldstein,
2004, for a review). Three core motivations have been identified: a desire
for accuracy, a desire for affiliation, and the maintenance of a positive
self-concept. Recent work by Erb and colleagues (Erb, Bohner, Rank,
& Einwiller, 2002; Erb, Bohner, Schmalzle, & Rank, 1998) found that
the contribution of these factors varies as a function of the individuals’
prior beliefs toward the topic under consideration. When people’s
prior beliefs are strongly opposed to the position held by the majority,
conformity is driven by a desire to fit in. But when people hold moderately
or no strong prior beliefs concerning the topic, conformity is driven by a
belief that the majority view is more likely to constitute an objective
consensus.

It is assumed that people violate a norm of rationality when they allow
social consensus to override facts. Campbell (1990) argued that yielding to
conformity allows error and confusion to spread throughout a group, while
independent decision making and resistance to conformity is socially
productive because it allows errors to be corrected. Resistance to conformity
is therefore considered both moral and rational. It is moral because it
reflects adherence to principle, and it is rational because it introduces fact-
based judgment into the group decision-making process.

This raises the following question: Can conformity influence something
we consider to be an integral part of our identities; namely, morality? Unlike
visual decision making where correct answers are clear and unambiguous,
moral dilemmas are dilemmas precisely because the correct course of action
is unclear. Yet the laws and social institutions of virtually every culture are
grounded in moral principles, such as avoiding harm to others and fairness
in social transactions (Haidt, 2007). People are expected to rely on culturally

MORALITY AND CONFORMITY 269

dictated moral principles as well as their own personal moral intuitions

when choosing when and whether to aid others in distress, how to judge the

culpability of parties involved in wrongdoing or disputes, and which

behaviors should be subject to social and legal censure. Our behavior is

frequently judged on the basis of whether we acted in accordance with our

moral principles, or whether we simply chose to ‘‘go along to get along’’, as

would be the case if we allowed social conformity to override moral

principles. Taking this course of action typically makes one the target of

criticism and social censure. An over-reliance on social conformity in

guiding one’s actions is also the hallmark of conventional (stage 3) moral

reasoning in Kohlberg’s six-stage theory of moral development; the highest

level of moral development (stage 6) is rooted in reliance on moral principles

to guide behavior (Kohlberg, 1969).
Despite the ubiquity and gravity of moral judgment in our everyday lives,

scant research exists on the impact of conformity on moral judgment.

Crutchfield (1955) tested the impact of majority opinion on judgments in a

variety of different domains, including agreement with morally relevant

statements such as ‘‘Free speech being a privilege rather than a right, it is

proper for a society to suspend free speech whenever it itself is threatened.’’

He found that only 19% of participants agreed with such statements when

alone, but 58% agreed when confronted with a unanimous group who

endorsed the statements. This is surprising given that people have been

found to reject and distance themselves socially from morally dissimilar

others (Skitka, Bauman, & Sargis, 2005), and should therefore have little

desire to conform to the group. Indeed, Hornsey and colleagues (Hornsey,

Majkut, Terry, & McKimmie, 2003; Hornsey, Smith, & Begg, 2007) found

that participants with strong moral convictions about a social issue

expressed stronger intentions to verbally oppose the issue when they

believed they held a minority view than when they believed they held the

majority view. Importantly, these intentions did not translate to actual

behavior. Aramovich, Lytle, and Skitka (2012) assessed participants’ prior

beliefs concerning the acceptability of torture, along with their prior moral

commitments, socio-political attitudes, and other factors. The participants

then took part in an allegedly group discussion concerning the use of torture

via computer-simulated chat room; the participants believed they were

discussing the topic with fellow students. During the simulated group

discussion, 80% of participants reported less opposition to torture than they

had reported at pretest, but strength of moral conviction about torture was

negatively associated with the degree of pro-torture attitude change.

Although these results addressed only a single moral topic (i.e., permissi-

bility of torture), they suggest that moral judgment may in fact be

susceptible to conformity pressure.

270 KUNDU AND CUMMINS

dictated moral principles as well as their own personal moral intuitions
when choosing when and whether to aid others in distress, how to judge the
culpability of parties involved in wrongdoing or disputes, and which
behaviors should be subject to social and legal censure. Our behavior is
frequently judged on the basis of whether we acted in accordance with our
moral principles, or whether we simply chose to ‘‘go along to get along’’, as
would be the case if we allowed social conformity to override moral
principles. Taking this course of action typically makes one the target of
criticism and social censure. An over-reliance on social conformity in
guiding one’s actions is also the hallmark of conventional (stage 3) moral
reasoning in Kohlberg’s six-stage theory of moral development; the highest
level of moral development (stage 6) is rooted in reliance on moral principles
to guide behavior (Kohlberg, 1969).
Despite the ubiquity and gravity of moral judgment in our everyday lives,
scant research exists on the impact of conformity on moral judgment.
Crutchfield (1955) tested the impact of majority opinion on judgments in a
variety of different domains, including agreement with morally relevant
statements such as ‘‘Free speech being a privilege rather than a right, it is
proper for a society to suspend free speech whenever it itself is threatened.’’
He found that only 19% of participants agreed with such statements when
alone, but 58% agreed when confronted with a unanimous group who
endorsed the statements. This is surprising given that people have been
found to reject and distance themselves socially from morally dissimilar
others (Skitka, Bauman, & Sargis, 2005), and should therefore have little
desire to conform to the group. Indeed, Hornsey and colleagues (Hornsey,
Majkut, Terry, & McKimmie, 2003; Hornsey, Smith, & Begg, 2007) found
that participants with strong moral convictions about a social issue
expressed stronger intentions to verbally oppose the issue when they
believed they held a minority view than when they believed they held the
majority view. Importantly, these intentions did not translate to actual
behavior. Aramovich, Lytle, and Skitka (2012) assessed participants’ prior
beliefs concerning the acceptability of torture, along with their prior moral
commitments, socio-political attitudes, and other factors. The participants
then took part in an allegedly group discussion concerning the use of torture
via computer-simulated chat room; the participants believed they were
discussing the topic with fellow students. During the simulated group
discussion, 80% of participants reported less opposition to torture than they
had reported at pretest, but strength of moral conviction about torture was
negatively associated with the degree of pro-torture attitude change.
Although these results addressed only a single moral topic (i.e., permissi-
bility of torture), they suggest that moral judgment may in fact be
susceptible to conformity pressure.

Importantly, a growing number of studies have shown that judged moral
permissibility varies systematically with the degree of conflict between
morally relevant dilemma features (Greene et al., 2009). Dilemmas
describing actions that maximize aggregate benefits (‘‘greater good’’) while
violating no a priori moral rules yield high endorsement rates, and actions
that fail to maximize such benefits while simultaneously violating one or
more moral rule yield very low endorsement rates. When the two conflict,
causing the decision maker to choose between violating moral principles or
sacrificing the greater good, low decisional consensus obtains. In these
circumstances people are less certain what the morally permissible course of
action should be.

In the present study we used a modification of Asch’s methods to
investigate the impact of social consensus on moral decision making.
Participants were asked to render moral judgments for a series of dilemmas
either alone or in a group that included three confederates. Unlike Asch’s
participants, however, our participants rendered judgments by choosing a
number from a Likert-type scale that described a range of permissibility
ratings, including ‘‘uncertain’’. This allowed greater variability among
confederate judgments while still creating confederate consensus. If moral
judgment is influenced by social context, then participants’ ratings should be
swayed in the direction of the confederates’ atypical judgments compared to
ratings given in the absence of social pressure.

METHOD

Participants

A total of 33 participants were recruited from the University of Illinois
Psychology paid-participant website. There were 17 participants (12 female)
in the control condition, and 16 participants (9 female) in the experimental
condition.

Materials

A total of 12 dilemmas were selected from materials used by Greene, Morelli,
Lowenberg, Nystrom, and Cohen (2008). They differed along three
dimensions: (a) percent ‘‘permissible’’ judgments, (b) use of personal force,
and (c) whether the harm inflicted was intentional or a side effect of the action
taken. The latter two constitute deontological criteria that have been shown
to influence moral judgment (Greene et al., 2009). According to Greene et al.
(2009), an agent applies personal force when the force that directly impacts
the other is generated by the agent’s muscles and is not mediated by
intervening mechanisms that are distinct from the agent’s muscular force,
such as firing a gun. The vignette names, deontological values, percent ‘‘yes’’

MORALITY AND CONFORMITY 271

(permissible) judgments from Green et al., (2008), and confederate judgments
are displayed in Table 1.

Each vignette was printed on single sheet of paper with a 1–7 rating scale
underneath. The labels for the rating scale were (from 1 to 7, respectively)
Highly Impermissible, Impermissible, Somewhat Impermissible, Unsure,
Somewhat Permissible, Permissible, and Highly Permissible.

Four vignettes served as fillers; confederates always gave ratings that were
consistent with the judgment typically elicited by these vignettes (i.e., 6 or 7
for Submarine and Modified Bomb, which people typically judge
permissible; 1 or 2 for Smother for Dollars and Hard Times, which people
typically judge impermissible). Six of the experimental vignettes fell into two
categories. The first contained vignettes that are a majority of people
typically judge to be permissible (Standard Trolley, Standard Fumes, and
Vaccine Test), and for which the confederates gave atypical judgments
(i.e., ratings of 1 or 2). The second contained vignettes that a majority of
people typically judge to be impermissible (Sacrifice, Safari, and Vitamins),
and for which the confederates gave atypical judgments (i.e., ratings of
6 or 7). Finally, two vignettes were included that typically elicit high
disagreement concerning permissibility. Confederates rated one of these

TABLE 1
Vignette title, deontological features, percent acceptance rates, and judgments given

by confederates for the experiment materials

Vignette

Personal

force Harm

%

Yes
a

%

Yes
b

Confederate

judgment

Fillers

Submarine No Intentional 91 80 Permissible

Modified Bomb Yes Intentional 90 85 Permissible

Smother for Dollars Yes Intentional 7 8 Impermissible

Hard Times No Side Effect 9 3 Impermissible

Experimental

Standard Trolley No Side Effect 85 80 Impermissible

Standard Fumes No Side Effect 75 67 Impermissible

Vaccine Test No Side Effect 79 68 Impermissible

Sacrifice
c

Yes Intentional 51 28 Permissible

Safari Yes Intentional 22 28 Permissible

Vitamins Yes Intentional 35 38 Permissible

Sophie’s Choice No Side Effect 62 41 Impermissible

Crying Baby Yes Intentional 60 40 Permissible

a
Values reported by Greene et al. (2008).

b
Values reported by Cummins and Cummins (2012, Exp 1) based on decisions made by UIUC

students.
c
We opted to use Cummins and Cummins (2012) data to classify this vignette because

participants in this study were also drawn from UIUC students.

272 KUNDU AND CUMMINS

(permissible) judgments from Green et al., (2008), and confederate judgments
are displayed in Table 1.
Each vignette was printed on single sheet of paper with a 1–7 rating scale
underneath. The labels for the rating scale were (from 1 to 7, respectively)
Highly Impermissible, Impermissible, Somewhat Impermissible, Unsure,
Somewhat Permissible, Permissible, and Highly Permissible.
Four vignettes served as fillers; confederates always gave ratings that were
consistent with the judgment typically elicited by these vignettes (i.e., 6 or 7
for Submarine and Modified Bomb, which people typically judge
permissible; 1 or 2 for Smother for Dollars and Hard Times, which people
typically judge impermissible). Six of the experimental vignettes fell into two
categories. The first contained vignettes that are a majority of people
typically judge to be permissible (Standard Trolley, Standard Fumes, and
Vaccine Test), and for which the confederates gave atypical judgments
(i.e., ratings of 1 or 2). The second contained vignettes that a majority of
people typically judge to be impermissible (Sacrifice, Safari, and Vitamins),
and for which the confederates gave atypical judgments (i.e., ratings of
6 or 7). Finally, two vignettes were included that typically elicit high
disagreement concerning permissibility. Confederates rated one of these
TABLE 1
Vignette title, deontological features, percent acceptance rates, and judgments given
by confederates for the experiment materials
Vignette
Personal
force Harm
%
Yes
a
%
Yes
b
Confederate
judgment
Fillers
Submarine No Intentional 91 80 Permissible
Modified Bomb Yes Intentional 90 85 Permissible
Smother for Dollars Yes Intentional 7 8 Impermissible
Hard Times No Side Effect 9 3 Impermissible
Experimental
Standard Trolley No Side Effect 85 80 Impermissible
Standard Fumes No Side Effect 75 67 Impermissible
Vaccine Test No Side Effect 79 68 Impermissible
Sacrifice
c
Yes Intentional 51 28 Permissible
Safari Yes Intentional 22 28 Permissible
Vitamins Yes Intentional 35 38 Permissible
Sophie’s Choice No Side Effect 62 41 Impermissible
Crying Baby Yes Intentional 60 40 Permissible
a
Values reported by Greene et al. (2008).
b
Values reported by Cummins and Cummins (2012, Exp 1) based on decisions made by UIUC
students.
c
We opted to use Cummins and Cummins (2012) data to classify this vignette because
participants in this study were also drawn from UIUC students.

(Sophie’s Choice) as impermissible and the other (Crying Baby) as
permissible. Examples of the vignettes are shown in Table 2. Texts for all
vignettes can be found by clicking the supplementary materials link
provided in Greene et al. (2008).

Procedure

In the control condition the experimenter and participant were seated at a
conference table in a private room. In the experimental condition three
confederates came into the room around the same time as the real participant
and posed as real participants. The confederates were three male graduate
students. The confederates took care to sit around the table so that the three
of them were in consecutive seats and the real participant was at one end.

TABLE 2
Examples of vignettes used in the experiment

Filler: Submarine: You are the captain of a military submarine

traveling under a large iceberg. An explosion has damaged

your oxygen supply and injured one of your crew. The

injured crew member cannot survive his wounds. There is

not enough oxygen left for the entire crew to make it to the

surface. The only way to save the other crew members is to

shoot dead the injured crew member so that there will be just

enough oxygen for the rest of the crew to survive. Is it

morally permissible to kill the injured crew member under

the circumstances?

Weak Consensus Crying Baby: Enemy soldiers are approaching your village.

You and your townspeople are hiding. Your baby begins to

cry loudly, which will surely alert the soldiers to your

location. If you cover your baby’s face to muffle the sound

until the soldiers leave, you will smother him. Is it morally

permissible to smother your baby under the circumstances?

Strong Consensus – ‘‘Yes’’ Standard Trolley: A runaway trolley is approaching a fork in

the tracks. On the left track are five people. On the right

track is one person. If you do nothing the trolley will go left,

causing the deaths of five people. The only way to avoid this

is to push a switch that will cause the trolley to go right,

causing the death of the single person. Is it morally

permissible to push the switch under the circumstances?

Strong Consensus – ‘‘No’’ Sacrifice: You, your spouse, and your four children are

crossing a mountain range on your return journey to your

homeland. You have inadvertently set up camp on a local

clan’s sacred burial ground. The leader of the clan says if

you kill your oldest son with the clan leader’s sword, he will

let the rest of you live. Is it morally permissible to kill your

oldest son under the circumstances?

MORALITY AND CONFORMITY 273

Participants were instructed that they would be asked to make a series of

decisions about moral dilemmas for which there were no right or wrong
answers. They were told we were interested in their responses to help us

choose materials for future research. Folders were distributed which

contained the vignettes. The folders given to the confederates had a small
mark beside the rating they were supposed to give for each vignette.

Confederates were not blind to the experimental hypotheses, and so were

trained and instructed to respond according to script, without giving
explanation or commentary on their choices. The answers confederates gave

were distributed across the extreme end of the appropriate range (i.e.,

‘‘permissible’’ could be 6 or 7, and ‘‘impermissible’’ could be 1 or 2). The first
vignette was always Submarine, and the confederates gave a typical answer.

The remaining sheets were shuffled between sessions. Each vignette was read
aloud once and participants were given about 4 seconds to consider the

situation. They were then asked to announce their answers aloud in turn as

the experimenter recorded their choices. The real participant was always
prompted to answer last after all of the confederates had given their answers.

It was explained that answers were to be given aloud in order to save time and

so that the printed materials could be re-used. After the experiment
concluded the purpose of the experiment was explained, including the use

of deception. Participants were not queried about their beliefs concerning the

true purpose of the experiment prior to debriefing, although the majority
spontaneously expressed surprise when informed of the deception, particu-

larly that the graduate students were confederates and not true participants.

RESULTS

If participants’ moral judgments were swayed by social consensus, then we
would expect that ratings of vignettes typically judged permissible should

receive lower permissibility ratings in the group condition than in the

control condition, while ratings of vignettes typically judged impermissible
should receive higher permissibility ratings in the group condition than in

the control condition. To test this prediction, ratings for vignettes that

typically yield strong consensus were analyzed separately from those that
typically yield weak consensus.

For the strong consensus vignettes, ratings were averaged across the three

‘‘impermissible’’ vignettes (Sacrifice, Safari, and Vitamins), and across the
three ‘‘permissible’’ vignettes (Standard Trolley, Standard Fumes, and

Vaccine Test). These mean ratings were analyzed via mixed ANOVA using

condition (Control or Group) and sex (Female or Male) as between-
participant variables, and moral category (Impermissible or Permissible)

as repeated measures. The analysis returned a single significant effect,

274 KUNDU AND CUMMINS

Participants were instructed that they would be asked to make a series of
decisions about moral dilemmas for which there were no right or wrong
answers. They were told we were interested in their responses to help us
choose materials for future research. Folders were distributed which
contained the vignettes. The folders given to the confederates had a small
mark beside the rating they were supposed to give for each vignette.
Confederates were not blind to the experimental hypotheses, and so were
trained and instructed to respond according to script, without giving
explanation or commentary on their choices. The answers confederates gave
were distributed across the extreme end of the appropriate range (i.e.,
‘‘permissible’’ could be 6 or 7, and ‘‘impermissible’’ could be 1 or 2). The first
vignette was always Submarine, and the confederates gave a typical answer.
The remaining sheets were shuffled between sessions. Each vignette was read
aloud once and participants were given about 4 seconds to consider the
situation. They were then asked to announce their answers aloud in turn as
the experimenter recorded their choices. The real participant was always
prompted to answer last after all of the confederates had given their answers.
It was explained that answers were to be given aloud in order to save time and
so that the printed materials could be re-used. After the experiment
concluded the purpose of the experiment was explained, including the use
of deception. Participants were not queried about their beliefs concerning the
true purpose of the experiment prior to debriefing, although the majority
spontaneously expressed surprise when informed of the deception, particu-
larly that the graduate students were confederates and not true participants.
RESULTS
If participants’ moral judgments were swayed by social consensus, then we
would expect that ratings of vignettes typically judged permissible should
receive lower permissibility ratings in the group condition than in the
control condition, while ratings of vignettes typically judged impermissible
should receive higher permissibility ratings in the group condition than in
the control condition. To test this prediction, ratings for vignettes that
typically yield strong consensus were analyzed separately from those that
typically yield weak consensus.
For the strong consensus vignettes, ratings were averaged across the three
‘‘impermissible’’ vignettes (Sacrifice, Safari, and Vitamins), and across the
three ‘‘permissible’’ vignettes (Standard Trolley, Standard Fumes, and
Vaccine Test). These mean ratings were analyzed via mixed ANOVA using
condition (Control or Group) and sex (Female or Male) as between-
participant variables, and moral category (Impermissible or Permissible)
as repeated measures. The analysis returned a single significant effect,

the interaction of moral category and condition, F(1, 29) ¼ 23.57,
MSe ¼ 1.29, p 5 .0001, w2¼ .45. Four planned comparisons were conducted.

Looking within groups, the control group did indeed find the vignettes in
the permissible category more permissible (M ¼ 4.45) than vignettes in the
impermissible category (M ¼ 3.23), t(16) ¼ 5.31, p 5 .0001, Cohen’s d ¼ .80,
thereby replicating the findings of past research using these vignettes. The
social context group, however, departed significantly from this oft-replicated
consensual pattern: When confederates judged highly impermissible moral
transgressions to be permissible, participants also rated them as permissible
(M ¼ 4.37), and when confederates judged highly permissible vignettes to be
impermissible, so did participants (M ¼ 2.67), t(15) ¼ 3.38, p 5 .004,
Cohen’s d ¼ .66. Comparing vignette ratings across groups also yielded a
strong conformity effect: As predicted, vignettes that are typically judged
permissible were found to be significantly less so under dissenting social
pressure (M ¼ 2.67) than when participants made decisions on their own
(M ¼ 4.45), t(31) ¼ 4.18, p 5 .0001, Cohen’s d ¼ .62. Conversely, vignettes
that are normally judged highly impermissible were rated as more
permissible when confederates said so (M ¼ 4.38) than when participants
made decisions by themselves (M ¼ 3.23), t(31) ¼ 2.74, p 5 .01,
Cohen’s d ¼ .62. These results clearly show that our participants’ judgments
were strongly swayed by social context, even for vignettes that typically elicit
the opposite decision from an overwhelming majority of decision makers.

When reasoning under uncertainty, we would expect that decision makers
would be more likely to conform to strong group consensus, and that is
what we found when we analyzed the two vignettes that typically elicit low
decision consensus. Ratings were analyzed via mixed ANOVA using
condition (Control or Group) and sex (Female or Male) as between-
participant factors and dilemma (Sophie’s Choice and Crying Baby) as
repeated measures. The main effect of Dilemma was significant,
F(1, 29) ¼ 6.19, MSe ¼ 2.20, p 5 .02, w2¼ .18. This effect was modified by
an interaction with Condition, F(1, 29) ¼ 21.67, MSe ¼ 2.2, p 5 .0001,
w2¼ .43. Four planned comparisons were conducted.
Looking first within groups, the control group did indeed give statistically

equivalent ratings to Sophie’s Choice (M ¼ 3.53) and to Crying Baby
(M ¼ 2.76), t(16) ¼ 1.54, p ¼ .14. In the social context group the confederates
rated Sophie’s Choice as highly impermissible and Crying Baby as highly
permissible, and participants followed their lead. When deciding among
dissenting confederates, participants found Sophie’s Choice to be far less
permissible (M ¼ 2.00) than Crying Baby (M ¼ 4.75), t(15) ¼ 5.46, p 5 .0001,
Cohen’s d ¼ .82. Comparing group performance on each vignette, partici-
pants were found to rate Crying Baby as significantly more permissible when
confederates rated it so (M ¼ 4.75) than when they made decisions alone
(M ¼ 2.76), t(31) ¼ 3.31, p 5 .002, Cohen’s d ¼ .51. Conversely, participants

MORALITY AND CONFORMITY 275

found Sophie’s Choice far less permissible (M ¼ 2.00) when confederates
rated it as impermissible than when they made decisions on their own
(M ¼ 3.53), t(31) ¼ 2.66, p 5 .025, Cohen’s d ¼ .43. Clearly, our participants’
judgments regarding these ‘‘ambiguous’’ moral dilemmas were strongly
swayed by social consensus.

DISCUSSION

Our results clearly show a strong conformity effect, indicating that moral
decision making is strongly influenced by social context, thereby replicating
Asch’s seminal finding in a new domain. Given that our participants’ moral
judgments were so strongly influenced by social consensus, the next
important questions are whether this behavior (a) is rational and (b) is
itself morally acceptable.

Conformity is considered irrational only if one believes that social
consensus should be awarded less weight in decision making than one’s own
information or beliefs. But according to rational-actor models, people are
not necessarily behaving irrationally when they conform if they believe that
conformity maximizes the expected value of the decision. Consider the Asch
situation from a game-theoretic perspective (Krueger & Massey, 2009; Luce
& Raiffa, 1957). Participants are assumed to prefer to speak the truth, but
the strength of this preference is modulated by what others do. This yields
four possible outcomes that can be ordered in terms of payoffs to the
participant. If the participant is purely self-regarding, then the payoff matrix
yields the following: Everyone tells the truth 4 Participant tells the truth but
others lie (Positive Resistance) 4 Everyone lies 4 Participant lies while
others tell the truth (Negative Resistance). Under these circumstances, the
dominant choice (the best choice regardless of what other parties do) is to
tell the truth. If others tell the truth, the payoff is greater for the participant
if he or she tells the truth as well. If others lie instead, the payoff is still
greater for telling the truth.

But if we assume that people are a mixture of selfish and other-regarding
(benevolent) preferences, the payoff matrix can be modeled as the sum of
one’s own payoffs and others’ payoffs weighted by 1/N, where N is the
number of other people in the group (van Lange, 1999). This yields the
following: Everyone tells the truth 4 Participant tells the truth but others lie
(Positive Resistance) ¼ Participant lies while others tell the truth (Negative
Resistance) ¼ Everyone lies. Now there is no dominant choice. If others tell
the truth, the payoff is greater for telling the truth as well. But if others lie,
then the payoffs for being truthful and for going along with the lie are
the same.

Why would people choose to go along with the lie rather than tell
the truth? One explanation is that pronounced social consensus in a

276 KUNDU AND CUMMINS

found Sophie’s Choice far less permissible (M ¼ 2.00) when confederates
rated it as impermissible than when they made decisions on their own
(M ¼ 3.53), t(31) ¼ 2.66, p 5 .025, Cohen’s d ¼ .43. Clearly, our participants’
judgments regarding these ‘‘ambiguous’’ moral dilemmas were strongly
swayed by social consensus.
DISCUSSION
Our results clearly show a strong conformity effect, indicating that moral
decision making is strongly influenced by social context, thereby replicating
Asch’s seminal finding in a new domain. Given that our participants’ moral
judgments were so strongly influenced by social consensus, the next
important questions are whether this behavior (a) is rational and (b) is
itself morally acceptable.
Conformity is considered irrational only if one believes that social
consensus should be awarded less weight in decision making than one’s own
information or beliefs. But according to rational-actor models, people are
not necessarily behaving irrationally when they conform if they believe that
conformity maximizes the expected value of the decision. Consider the Asch
situation from a game-theoretic perspective (Krueger & Massey, 2009; Luce
& Raiffa, 1957). Participants are assumed to prefer to speak the truth, but
the strength of this preference is modulated by what others do. This yields
four possible outcomes that can be ordered in terms of payoffs to the
participant. If the participant is purely self-regarding, then the payoff matrix
yields the following: Everyone tells the truth 4 Participant tells the truth but
others lie (Positive Resistance) 4 Everyone lies 4 Participant lies while
others tell the truth (Negative Resistance). Under these circumstances, the
dominant choice (the best choice regardless of what other parties do) is to
tell the truth. If others tell the truth, the payoff is greater for the participant
if he or she tells the truth as well. If others lie instead, the payoff is still
greater for telling the truth.
But if we assume that people are a mixture of selfish and other-regarding
(benevolent) preferences, the payoff matrix can be modeled as the sum of
one’s own payoffs and others’ payoffs weighted by 1/N, where N is the
number of other people in the group (van Lange, 1999). This yields the
following: Everyone tells the truth 4 Participant tells the truth but others lie
(Positive Resistance) ¼ Participant lies while others tell the truth (Negative
Resistance) ¼ Everyone lies. Now there is no dominant choice. If others tell
the truth, the payoff is greater for telling the truth as well. But if others lie,
then the payoffs for being truthful and for going along with the lie are
the same.
Why would people choose to go along with the lie rather than tell
the truth? One explanation is that pronounced social consensus in a

decision-making context signals the creation of a social norm; that is, an
explicit or implicit rule concerning what one is permitted, obligated, or
forbidden to do in the current context (Cummins, 1998, 2000, 2005).
Deviations from expectation in nonsocial contexts (such as ‘‘oddball’’
detection in visual and semantic tasks) typically elicit activation in neural
reinforcement learning circuitry. The same network has been shown to be
active when there is conflict with a social norm (Klucharev Hytonen,
Rijpkema, Smidts, & Fernandez, 2009). When conforming to a norm, brain
regions associated with anxiety or disgust (such as the insula) are active,
indicating that conforming comes at an emotional cost (Berns, Capra,
Moore, & Noussair, 2010). These error-related neural signals alert the
reasoner when a decision that deviates from a particular social norm or a
broader social norm that one should both trust others and reciprocate trust
that has been placed in oneself.

Another reason why people may conform is that consensus that departs
from our own beliefs introduces uncertainty, particularly the suspicion that
the consensus ‘‘reflect[s] information that they have and we do not’’
(Banerjee, 1992, p. 798). Conformity can then be viewed as a rational
decision under conditions of uncertainty. This is particularly relevant when
conformity is modeled as informational cascades (Bikhchandani,
Hirschleifer, & Welch, 1992). In cascade models the first person is assumed
to have private information while each subsequent person is assumed to
have private information plus information about others’ decisions. If the
first two people agree, then the third concludes that they share the same
private information. If that information concurs with their own, the cascade
continues on to the next person, and so on. If two consecutive people
disagree, however, then this signals that they have different private
information. Each person can be thought of as equally weighting their
own and other people’s judgments. Group consensus that departs from
one’s own judgment therefore holds sway.

These analyses indicate that conformity can indeed be the outcome of a
rational process. But they also just as clearly indicate that rationality and
morality are separate, incommensurate criteria. One cannot be reduced to or
explained in terms of the other.

CONFLICT OF INTEREST STATEMENT

This research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest.

Manuscript received 14 June 2012

Manuscript accepted 3 September 2012

First published online 4 October 2012

MORALITY AND CONFORMITY 277

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influence of popularity on adolescent ratings of music. Neuroimage, 49,
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MORALITY AND CONFORMITY 279

ORIGINAL ARTICLES

They Came, They Liked, They Commented:
Social Influence on Facebook News Channels

Stephan Winter, PhD, Caroline Brückner, BSc, and Nicole C. Krämer, PhD

Abstract

Due to the increasing importance of social networking sites as sources of information, news media organiza-
tions have set up Facebook channels in which they publish news stories or links to articles. This research
investigated how journalistic texts are perceived in this new context and how reactions of other users change the
influence of the main articles. In an online experiment (N = 197), a Facebook posting of a reputable news site
and the corresponding article were shown. The type of user comments and the number of likes were system-
atically varied. Negative comments diminished the persuasive influence of the article, while there were no
strengthening effects of positive comments. When readers perceived the topic as personally relevant, comments
including relevant arguments were more influential than comments with subjective opinions, which can be
explained by higher levels of elaboration. However, against expectations of bandwagon perceptions, a high
number of likes did not lead to conformity effects, which suggests that exemplifying comments are more
influential than statistical user representations. Results are discussed with regard to effects of news media
content and the mechanisms of social influence in Web 2.0.

Introduction

Since circulations of printed newspapers are de-clining, news media organizations have been trying to
reach their audiences online. In this context, social media
platforms such as Facebook have emerged as an increasingly
relevant channel. Recent studies suggest that members use
these sites not only for social contacts but also as a source of
information on politics or public affairs.

1–3
Messing and

Westwood argued that this trend may lead to a situation in
which ‘‘the window through which the public views the world
is no longer the front page of the New York Times, but the
Facebook news feed.’’

4(p1058)
Many newspapers and TV and

radio stations have developed strategies to (at least partly)
adapt to the changing patterns of media usage and set up
channels within the social networking site (SNS) Facebook.
On these pages, the social media editors regularly publish
short news or links to online articles that can be ‘‘liked,’’
discussed, or shared by the users. The Facebook page of the
New York Times, for instance, has about nine million ‘‘fans.’’

On SNS news channels, journalistic texts are accompanied
by likes and peer comments, which represents a convergence
of mass and interpersonal communication.

5

While most gen-

eral online news sites also include comment sections, the de-
sign of Facebook and similar SNS put an even larger emphasis
on user reactions, and feature them in a more salient way. From

the receivers’ perspective, peer reactions may offer additional
information or facilitate finding relevant postings in a situation
of information overload.

6
From the journalists’ perspective,

direct feedback may be helpful for understanding the interests
of their audience, but it is also possible that the authors’ claims
are contradicted. Predominantly in the setting of e-commerce
sites, research showed that peer comments or ratings are in-
deed able to exert substantial effects on readers’ evaluations.

7

Against this background, this study aims to investigate the
effects of user reactions in the increasingly popular setting of
Facebook news channels and the underlying psychological
mechanisms of information processing. As the most relevant
form of peer reactions in SNS news channels, this study
focuses on the comparison of user-generated comments and
the number of likes.

8

The influence of online comments

First studies of online comments showed that readers use
these statements to assess credibility

9
and that viewers of

YouTube clips evaluate the content in line with peer com-
ments.

10,11
Focusing on online news sites, Lee and Jang

5
found

that contradicting comments below an article are able to
change readers’ opinions, as well as their perception of the
opinion climate. Based on exemplification theory,

12
Lee and

Jang argue that comments are seen as relevant statements of

Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg, Germany.

CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL NETWORKING
Volume 18, Number 8, 2015
ª Mary Ann Liebert, Inc.
DOI: 10.1089/cyber.2015.0005

431

peers. Although these voices out of the audience are probably
not representative, they exert their influence as vivid exem-
plars of public opinion.

5
One difference compared with com-

menters on news sites is that people who comment on
Facebook are not anonymous but typically visible with their
name and a small picture. On the one hand, this may limit
identification with the commenters when differences in age or
cultural background become salient.

13
On the other hand, the

fact that people connect their comments to their public profile
may increase the credibility of these statements. Therefore, it
can be assumed that the patterns of peer influence that have
been shown for commenters on news sites also apply for SNS
news channels. While previous studies only investigated the
influence of contradicting comments, it can furthermore be
assumed that positive comments are likely to enforce the view
that is advocated in the main article.

H1: SNS user comments affect readers’ attitudes toward the
topic in the direction of the comments’ valence.

H2: SNS user comments affect readers’ perception of public
opinion toward the topic in the direction of the comments’
valence.

Typically, there are huge differences in the style and
quality of the statements that are posted by readers.

14,15
For

instance, comments may contain relevant arguments on the
topic, but also merely consist of subjective opinions without
further reasoning. To explore the question of whether and
how these differences influence the comments’ impact, this
study utilizes the elaboration likelihood model (ELM),

16

which posits that the depth of elaboration depends on read-
ers’ motivation and abilities. When readers are highly mo-
tivated, they scrutinize the quality of the given arguments;
otherwise, they primarily pay attention to peripheral aspects
(such as the source). Given that Facebook users who follow
news channels are likely to be interested in current topics, it
can be expected that differences in argument quality

17
are

detected by the readers.
14

Therefore, it is assumed:

H3: Argumentative comments are more persuasive than
subjective comments.

According to the ELM, this effect should be most pro-
nounced among readers with high levels of elaboration,
which can be assumed when the topic is personally relevant
or when readers are generally motivated to engage in com-
plex thinking (need for cognition, NC).

18

H4: The effect of comment type is strengthened by (a) the
perceived relevance of the topic and (b) readers’ NC.

The influence of Facebook likes

Besides comments, the ‘‘like’’ function is a very promi-
nent feature of Facebook and a means of expressing agree-
ment with a posting, which results in an aggregated number.
Prior research has shown that users tend to follow the be-
havior of the crowd, for example by selecting films with
positive ratings or a high popularity.

19
The number of likes

differs from ratings insofar as it is limited to agreement and
cannot convey a negative evaluation (only a low number may
be interpreted as a signal of an unpopular posting). Com-
pared to textual comments, likes are less specific, since there
is no further information than mere popularity. However,

they usually include reactions of larger parts of the audience
(and thereby a possibly more valid impression of others’
opinions

4
). According to research on bandwagon percep-

tions,
20,21

it can thus be assumed that readers evaluate arti-
cles that appear to be appreciated by others more favorably.

H5: A high numbers of likes leads (a) to more positive
evaluations of article quality, (b) to stronger persuasive
effects of the article, and (c) to stronger effects on readers’
perceptions of public opinion compared with a low number
of likes.

Method

An online experiment was conducted with a 5 · 2 (type of
comments · number of likes) between-subjects design. Par-
ticipants saw a screenshot with a short summary of an online
news story presented on the Facebook page of a reputable
news magazine (see Fig. 1). Afterwards, they read the long
version of the article. The study aimed to select an exemplary
topic that is moderately relevant for most readers but in
which they are less likely to have strong and polarized prior
attitudes. Therefore, the debate on the legalization of mari-
juana (which attracted moderate media attention at the time
of the study) was chosen.

Sample

A total of 227 participants filled out the online question-
naire. Five participants younger than the age of 18 years were
excluded. Furthermore, only participants who spent a mini-
mum reading time on the posting and the article (more than
20 seconds for each stimulus) were considered for further
analysis. This resulted in a final sample of 197 (100 females;
Mage = 25.23 years; SD = 4.93 years). Due to the recruitment
at a large European university, most of the participants (124)
were students, and 46 were employed.

Design

In the Facebook posting and the corresponding article,
statements of an economist in favor of legalization were
summarized. The posting included a short teaser (34 words)
and a link to the Web page—the article itself (371 words,
based on existing material) explains statements of a professor
who argues that prohibition does not prevent people from
consuming harmful drugs and that a legalization would lead
to more control.

With regard to the type of comments, the postings of the
ostensible peers were either positive or negative toward the
slant of the article and either argumentative or subjective.
Subjective comments included the mere expression of a
specific opinion (example [negative]: ‘‘I can only hope that
marijuana will never be legal. I am against any type of drug,
it’s just not right’’), while argumentative comments men-
tioned a relevant point (example [positive]: ‘‘Prohibition
creates a black market without any rules. The legalization
would be a chance to stop the criminal structures and the
corresponding risks’’). In every condition, five comments
were displayed as ostensible statements of other users
(shown with average names and small profile pictures). Be-
sides four conditions with comments (subjective/pro, sub-
jective/con, argumentative/pro, argumentative/con), a fifth
version did not include any further comments.

432 WINTER ET AL.

To check the validity of the manipulation, an additional 44
participants (30 female, Mage = 24.84 years; SD = 4.86 years)
rated all comments with regard to competence, trustworthi-
ness, and argument quality, averaged to a quality score
(Cronbach’s a between 0.83 and 0.96). Results showed a
strong effect of comment type (argumentative vs. subjective)
on participants’ perception of quality, F(1, 43) = 238.69;
p < 0.001; gp

2 = 0.85.
The number of ‘‘likes,’’ which was shown below the Fa-

cebook posting, was either high (around 500) or low (around
40). The specific numbers were chosen based on observa-
tions of the specific Facebook page for articles with high and
low popularity.

Dependent measures

Readers’ attitude toward the topic was assessed with five
items (e.g., ‘‘The legalization of marihuana should be sup-
ported’’), which were rated on a 7-point scale (a = 0.90;
M = 4.17; SD = 1.65). Based on prior studies,5 perceived public
opinion was measured by asking participants whether they be-
lieved that the general public would agree with the above
mentioned statements (a = 0.79; M = 3.31; SD = 1.01). Further-
more, the evaluation of the article was measured with a semantic
differential

14
(‘‘well-written–not well-written,’’ ‘‘useful–not

useful,’’ ‘‘like–dislike’’; a = 0.80; M = 4.56; SD = 1.20) and four
items on the credibility and quality of the text (a = 0.83;
M = 4.27; SD = 1.31).

Moderating variables

Participant’s NC was measured with 16 items
18,22

(a = 0.81;
M = 5.00; SD = 0.70). A further three items assessed the per-

sonal relevance of the topic (e.g., ‘‘I frequently think about the
topic of Marihuana and legalization’’; a = 0.71; M = 3.55;
SD = 1.42).

Results

For hypothesis tests, analyses of variance were conducted
with type of comment and number of likes as independent
factors. For the attitude toward the topic, results showed a
significant main effect of comment type, F(4, 187) = 2.60,
p = 0.038, gp

2 = 0.05, but no main effect of likes. Pairwise
comparisons (LSD) showed a significant difference between
the control group and argumentative con comments ( p = 0.006;
SE = 0.374). Readers who read these negative comments had a
more negative attitude toward legalization (M = 3.59; SD =
1.56) than those who only read the main posting (M = 4.62;
SD = 1.67). Participants who read negative subjective com-
ments also tended to express a more negative attitude
(M = 3.91; SD = 1.82) than the control group, but the difference
was smaller—the post hoc contrast approached significance
( p = 0.054; SE = 0.376). Positive comments did not lead to a
more positive attitude in comparison to the control group.
Readers who saw positive argumentative comments (M = 4.38;
SD = 1.40) and subjective comments (M = 4.37; SD = 1.65)
only differed from participants who saw negative argumenta-
tive comments (post hoc contrasts: p = 0.029; SE = 0.361/
p = 0.033; SE = 0.372). Therefore, H1 is partially supported for
negative comments but not for positive comments.

Further analyses of variance for the dependent measures of
perceived public opinion and article quality did not show
significant effects of comment type and likes. Therefore, H2
about the effects of comments on the perception of public

FIG. 1. Example of the
stimulus material: Facebook
posting of a reputable news
media source with user reac-
tions (pictures blurred for
publication).

SOCIAL INFLUENCE ON FACEBOOK NEWS CHANNELS 433

opinion has to be rejected. Since readers who saw the posting
with a high versus low number of likes did not differ regarding
their evaluation of article quality, their attitude and perceptions
of public opinion, H5 is not supported by the data either.

The finding that negative argumentative comments showed
stronger effects on readers’ attitudes than negative subjective
comments partially supports H3. However, there were no
differences between subjective and argumentative comments
when their valence was positive.

H4 predicted a stronger persuasive influence of argu-
mentative comments for readers with a higher level of
elaboration. This was tested in moderated regression analy-
ses with readers’ attitude as criterion. Due to the ineffec-
tiveness of positive comments, this analysis only included
participants who saw negative comments and focused on a
comparison of argumentative and subjective comments in
this subsample (n = 79). To test H4a, the type of comment,
personal relevance, and the interaction of comment type and
personal relevance were entered as predictors. According to
the results (Table 1), the interaction emerged as a significant
predictor. A simple slope analysis

23
revealed that readers

who perceived the topic as relevant were affected more
strongly by argumentative comments than by subjective com-
ments (b = 1.07; SE = 0.49; t = 2.19; p = 0.032), while comment
type did not matter for participants with low levels of relevance
(see Fig. 2), supporting H4a.

With regard to H4b on need for cognition, a regression
analysis following the same pattern was conducted. How-
ever, none of the predictors accounted for a significant
amount of variance, so that H4b is not supported. Additional
analyses for the subsample of participants who saw positive
comments did not yield significant regression models.

Discussion

The goal of the present study was to examine the effects of
peer reactions in Facebook news channels. Results predom-
inantly showed persuasive effects of negative user state-
ments, which is in line with research on (anonymous)
comments on news sites

5
and YouTube,

10,11
and shows that

voices out of the Facebook community are able to diminish
the persuasive effects of articles published by renowned
news sources. However, statements that supported the arti-
cle’s claims did not lead to strengthening persuasive effects.
This may be due to a ceiling effect, since the article itself
already led to relatively high levels of agreement or a neg-

ativity bias
24

in that information of negative valence arouses
more attention. These interpretations could be tested with a
further variation of the slant of the article.

With regard to the differences in the quality of reader
comments,

14
results showed stronger and more consistent ef-

fects of (contradicting) argumentative comments, but readers
who saw negative comments with merely subjective state-
ments also tended to express a more negative attitude. Mod-
eration analyses showed that readers who perceived the topic
as personally relevant were affected more strongly by argu-
mentative comments. In line with the ELM,

16
these readers

appear to detect the low informational value of subjective
comments more easily. This underlines that dual process
models such as the ELM can serve as useful frameworks to
describe the interplay of reader characteristics and user gen-
erated messages in Web 2.0, although there was no such
moderating effect for dispositional need for cognition. Rea-
sons for the lack of effects may be that the level of NC in the
sample was generally high and that thinking about common
news topics is not considered as sufficiently complex by
readers with higher NC. Considering argumentative (but not
subjective) comments would match ideals of deliberation

25
—

the fact that at least highly involved participants were only
persuaded by relevant arguments might therefore temper fears
that incompetent comments lead readers’ opinions into ques-
tionable directions.

While there were substantial effects on readers’ own at-
titudes, comments did not influence perceptions of public
opinion. That is, participants did not perceive the commen-
ters as representative, which may appear reasonable, since
only a minority actively writes comments.

15
These findings

suggest that the mechanisms of peer influence in this setting
are mostly due to direct persuasive effects rather than indi-
rect effects over perceived public opinion.

5

Against expectations on bandwagon effects,
21

the number
of likes did not influence the way in which readers evaluated a
news story or its content. This may also be connected to a
negativity bias. Since likes are limited to agreement, they
might fail to arouse readers’ attention and might not provide an
interpretable overview on the percentage of proponents and

Table 1. Moderated Regression Analysis
Predicting Attitudes Toward Legalization

by Type of Comment (Argumentative vs. Subjective),

Personal Relevance, and Interaction

of Comment Type and Personal Relevance

Attitude toward legalization

Predictor R
2 b p

(1) Type of comment 0.010 0.098 0.393
(2) Personal relevance 0.157 0.386 < 0.001 (3) Type of comment

· personal relevance
0.223 0.257 0.014

Final model: F(3, 75) = 7.17; p < 0.001; R2 = 0.223.

FIG. 2. Simple slopes: interaction of comment type and
personal relevance on readers’ attitude toward the topic.

434 WINTER ET AL.

opponents in the public. The superiority of comments over
likes may also be explained by exemplification theory.

5,12

Single statements by peers can be regarded as vivid exemplars
(which also contain more potentially persuasive content to
think about), whereas numbers of likes are less concrete sta-
tistics. However, it is possible that this may change when ex-
traordinarily high numbers of likes signal extreme popularity
or conflict with an initially negative impression of a source.

From a practical point of view, journalists might conclude
that they can only ‘‘lose’’ if they are confronted with reader
statements, since positive comments and likes did not en-
hance persuasive effects. This, however, neglects that argu-
mentative comments may contribute to processes of
deliberation and also give additional feedback to journalists.

When interpreting the results, the sample that mainly con-
sisted of students and the restriction to one specific article have
to be mentioned as limitations. Furthermore, the static nature of
the screenshot and the relatively uniform comments did not
fully reproduce the interactive nature of SNS. Since SNS in-
creasingly try to highlight comments that are written by friends,
relations to the commenters are likely to be a further important
factor in the mechanisms of peer influence. While the present
study showed comments by strangers, future research should
try to analyze the impact of these interpersonal aspects.

Despite these limitations, it is argued that this study
contributes to research on social influence in Web 2.0
settings. It demonstrates that the juxtaposition of mass and
interpersonal communication

5
in Facebook news channels

may attenuate traditional effects of mass media content. At
the same time, this study clarifies the conditions under
which voices out of the audience influence other readers:
To make a difference, they have to contradict the news
slant and include reasonable arguments, while positive
comments or likes do not strengthen the article’s claims.
With regard to theoretical perspectives, the study shows
that classic theories such as the ELM

16
and exemplification

theory
12

are helpful in analyzing the underlying mecha-
nisms of peer influence. Following this path may be a
worthwhile endeavor to understand patterns of news con-
sumption in a changing media landscape.

Acknowledgments

An earlier version of this work has been presented at the
2014 conference of the International Communication Asso-
ciation (Seattle, WA).

Author Disclosure Statement

No competing financial interests exist.

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Address correspondence to:
Dr. Stephan Winter

University of Duisburg-Essen
Social Psychology: Media and Communication

Forsthausweg 2
47057 Duisburg

Germany

E-mail: stephan.winter@uni-due.de

436 WINTER ET AL.

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Contents lists available at ScienceDirect

Computers in Human Behavior

journal homepage: www.elsevier.com/locate/comphumbeh

Full length article

Deindividuation effects on normative and informational social influence
within computer-mediated-communication

Serena Coppolino Perfumia,b,∗, Franco Bagnolic, Corrado Caudekd, Andrea Guazzinie

a Department of Sociology, Stockholm University, S-106 91, Stockholm, Sweden
b Department of Educational Sciences and Psychology, University of Florence, 50135, Florence, Italy
c Department of Physics and Astronomy and Center for the Study of Complex Dynamics (CSDC), University of Florence, 50019 Sesto Fiorentino, also INFN sec, Florence,
Italy
d Department of Neuroscience, Psychology, Drug Research and Children’s Health (NEUROFARBA) – sect. Psychology, University of Florence, 50135, Florence, Italy
e Department of Educational Sciences and Psychology and Center for the Study of Complex Dynamics (CSDC), University of Florence, 50135, Florence, Italy

A R

T

I C L E I N F O

Keywords:
Social influence
Conformity
Computer-mediated-communication
Anonymity
Deindividuation

A B S T R A C T

Research on social influence shows that different patterns take place when this phenomenon happens within
computer-mediated-communication (CMC), if compared to face-to-face interaction. Informational social influ-
ence can still easily take place also by means of CMC, however normative influence seems to be more affected by
the environmental characteristics. Different authors have theorized that deindividuation nullifies the effects of
normative influence, but the Social Identity Model of Deindividuation Effects theorizes that users will conform
even when deindividuated, but only if social identity is made salient.

The two typologies of social influence have never been studied in comparison, therefore in our work, we
decided to create an online experiment to observe how the same variables affect them, and in particular how
deindividuation works in both cases. The 181 experimental subjects that took part, performed 3 tasks: one
aiming to elicit normative influence, and two semantic tasks created to test informational influence. Entropy has
been used as a mathematical assessment of information availability.

Our results show that normative influence becomes almost ineffective within CMC (1.4% of conformity) when
subjects are deindividuated.

Informational influence is generally more effective than normative influence within CMC (15–29% of con-
formity), but similarly to normative influence, it is inhibited by deindividuation.

1. Introduction

With the diffusion of social networking platforms, the social and
information seeking-related human behaviors have been affected by the
“new” environment. Information seeking increasingly takes place on
social media platforms, relying on what a users’ contacts and followed
pages share (Zubiaga, Liakata, Procter, Hoi, & Tolmie, 2016).

Because of this filtering and selection, the users’ knowledge-building
process could be severely biased and polarized.

For example, a study shows that 72% of participants (college stu-
dents) trusted links sent by friends, even if they contained phishing
attempts (Jagatic, Johnson, Jakobsson, & Menczer,

2007).

The recent debate on fake news, highlighted the potential link be-
tween the increase in their spread, and the structure of social networks
as well as their embedded algorithms, which turned these environments
into “echo chambers”, in which users are selectively exposed to

information, and tend to filter the information in order to reinforce
their positions (confirmation bias), rather than to find alternatives (Del
Vicario et al., 2016).

These factors highlight the importance of studying the effects of
social influence within computer-mediated-communication, in order to
understand which environmental factors can enhance its effects.

Social norms exist also in online environments, but the users’ per-
ception of them can be different according to the platform, to anon-
ymity and the social ties among contacts. Therefore, compliance to
social norms can emerge in different ways, than those observable in
face-to-face interaction.

Also, information-seeking behavior can be affected by online en-
vironments: on one side we observe its interrelation with social norms,
especially when it takes place on social media platforms, and users
gather information on the basis of what they read on their personal
newsfeed. However, we also observe how users can rely on opinions

https://doi.org/10.1016/j.chb.2018.11.017
Received 29 March 2018; Received in revised form 9 October 2018; Accepted 7 November 2018

∗ Corresponding author. Department of Sociology, Stockholm University, S-106 91, Stockholm, Sweden.
E-mail address: serena.perfumi@sociology.su.se (S. Coppolino Perfumi).

Computers in Human Behavior 92 (2019) 230–

237

Available online 13 November 2018
0747-5632/ © 2018 Elsevier Ltd. All rights reserved.

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expressed by unknown actors, as it happens on platforms like
TripAdvisor.

The present study, using online experiments, aims to separate
norms-oriented social influence from information-oriented social in-
fluence, in order to observe which elements and environmental factors
have an effect on both typologies and which are peculiar for each.

1.1. Theoretical framework

A major understanding on the functioning of social influence came
about thanks to the pioneering works of Sherif (1937) and then Asch
(1951, 1955, 1956). The authors studied how the physical presence of
other people can lead experimental subjects to conform their judgment
to the one of the others. They used two different types of tasks: while in
Asch conformity experiments, guessing the correct answer could be
straightforward (Asch, 1955, 1956; Asch & Guetzkow, 1951), Sherif
used the autokinetic effect, so a more ambiguous task, to test the effects
of social influence (Sherif, 1937). From these experiments, two typol-
ogies of social influence have been identified, called “normative” when
people conform in order to satisfy a need to belong and comply to social
norms, as observed in Asch’s experiments, and “informational” when
the subjects lack on information in order to perform a task, as observed
in the autokinetic experiment (Deutsch & Gerard, 1955). According to
this theorization proposed by Deutsch and Gerard (1955), we can say
that we are able to observe normative social influence in Asch’s con-
formity experiments, because the task is relatively easy and the sub-
jects, when interviewed after taking part to the experiment stated that
they were able to spot the correct answer, but conform in order not to
break the social norms and be group outsiders. Instead, given that the
task presented in the autokinetic experiment is more ambiguous, as it is
based on a visual illusion, in this case we can say that subjects conform
because they are unsure on how to proceed.

While, as observed in these classical studies, to elicit conformity in
face-to-face situations, the physical presence of other people and being
exposed to their judgment can be enough, things go differently when
people interact online, especially for normative social influence.

Indeed, it is still unclear which elements can have the power to lead
people to conform during computer-mediated-communication.

Deindividuation, namely the diminished perception of one’s per-
sonal traits (Zimbardo, 1969), has been identified as a potential key
element in the discourse on normative influence.

The original deindividuation model was proposed by Zimbardo in
1969, and the author identified a series of variables that according to
him can lead to a deindividuation state. The variables considered by
Zimbardo are for example anonymity, arousal, sensory overload, novel
or unstructured situations, involvement in the act, and the use of al-
tering substances (Zimbardo, 1969). Several other authors suggest that
if people interact while being in a deindividuation state, normative
social influence can disappear (Deutsch & Gerard, 1955; Latané, 1981;
Lott & Lott, 1965; Short, Williams, & Christie, 1976). This happens
because there is not the possibility to identify the interlocutors, due to a
lack of actual or perceived proximity, and consequently, deindividua-
tion should lighten the pressure to act according to social norms
(Latané, 1981).

Furthermore, a study which tested antinormative behavior by
counterposing deindividuation to the presence of an explicit aggressive
social norm, showed that subjects were actually more aggressive when
deindividuated, rather than when exposed to the explicit norm, so in
this case, deindividuation resulted to be more powerful in leading to
antinormative behavior (Mann, Newton, & Innes, 1982).

A significant advancement in explaining the functioning of norma-
tive social influence in online environments is represented by the
contribution provided by the Social Identity Model of Deindividuation
Effects (SIDE Model), that takes the concept of deindividuation and
expands it, explaining its link and implications on social influence in
online environments (Spears, Postmes, Lea, & Wolbert, 2002).

The authors theorize that deindividuation is indeed likely to occur
in online environments, but it can become a powerful tool to trigger
conformity: given that while deindividuated, subjects have a dimin-
ished perception of their personal traits, if the group the subjects are
interacting with is made salient, then the subjects will be more likely to
conform (Spears, Postmes, & Lea, 2018).

This happens because combining a lack of relevance of one’s per-
sonality with an enhancement of the importance of the interlocutors,
will lead the subjects to identify at the group level, and consequently to
comply to the social norms. The experimental results seem to confirm
the predictions presented by the SIDE Model (Lee, 2004; Postmes,
Spears, Sakhel, & De Groot, 2001), but it is not clear what happens
when users are deindividuated but the group saliency is not enhanced.

On the matter of informational influence during computer-medi-
ated-communication instead, studies have focused on different aspects.

As aforementioned, a visible example of informational influence in
online environments is represented by users making choices on the
basis of reviews or ratings provided by other unknown users while
using platforms such as Tripadvisor, Uber or Airbnb (Liu & Zhang,
2010), but other examples show that it can take place easily also in
other ways.

A study conducted by Rosander and Eriksson (2012), shows that
users facing a general knowledge quiz in which they were exposed to
histograms showing the distribution of the answers provided by other
unknown users, conformed in high percentages (52%).

While many studies on online consumers behavior focused on fac-
tors such as the perceived importance of feedback (Liu & Zhang, 2010)
on informational influence, or on the conjunct effect of informational
and normative influence on behavior when subjects interact without
personal contact (LaTour & Manrai, 1989), no study tried to isolate it,
and point out the environmental factors that could be able to enhance
or diminish the compliance of users in this case. Furthermore, no study
tested the effects of deindividuation on informational influence.

In order to test and fulfill the predictions developed based on the
literature, we developed an experimental framework aiming to study
separately the two typologies of social influence during computer-
mediated-communication.

On one side, we reduced group saliency to test how deindividuation
works on both typologies of social influence and controlled the possible
interactions between some psychological dimensions and the operative
variables.

On the other side, we calculated the items entropy to test if task
ambiguity increases informational-based compliance. The environ-
mental factors that we decided to manipulate and study in relation to
both typologies of social influence are anonymity and physical isola-
tion, as their combination can trigger deindividuation.

1.2. Overview and predictions

To test online normative influence, we replicated Asch’s conformity
experiment (Asch, 1955, 1956; Asch & Guetzkow, 1951) on a web-
based platform, while to test online informational influence we created
two linguistic tasks of increasing ambiguity, designed adopting the
same structure of the “classical” Asch’s items. Task ambiguity was
measured by calculating the items’ entropy, and in this way, we were
able to assess the subjects’ lack of information. The diversity of the
tasks, allowed us to measure the interaction between anonymity, phy-
sical isolation, and degree of ambiguity, in relation to the behavior of
the experimental subjects. Considering the literature, we could for-
mulate the following predictions:

• H1) Diminished effectiveness of normative influence due to the
combination of a deindividuation state given by anonymity and
physical isolation, and minimum levels of group saliency, as theo-
rized by several authors (Deutsch & Gerard, 1955; Latané, 1981;
Lott & Lott, 1965; Short et al., 1976) and hypothesized by the SIDE

S. Coppolino Perfumi et al. Computers in Human Behavior 92 (2019) 230–237

231

Model (Postmes et al., 2001).

• H2) There is no specific evidence to build on, on the potential re-
lationship between deindividuation and informational influence (if
separated by normative influence), but we expect it to have the
same inhibitory effect it has on normative influence (Lee, 2007). The
effect of the anonymity and physical isolation variables alone will
also be controlled.

• H3) We expect a positive correlation between conformity and task
ambiguity, given that with more ambiguous items the subjects will
possess less information on how to handle the task, and might rely
on other people’s judgment (Cialdini & Trost, 1998; Rosander &
Eriksson, 2012).

We also controlled the interaction of personality and psychological
traits on conformity. In order to make sure that the analyzed effects
were relatable to the manipulated features and not to particular psy-
chological traits, we measured the psychological dimensions that ac-
cording to literature, result related to some extent to conformity. Only a
few studies analyzed the relation between conformity and personality
traits, suggesting some interesting connections between social con-
formity and Emotional Stability, Agreeableness and Closeness
(DeYoung, Peterson, & Higgins, 2002). So we expect that:

• H4) Factors as Neuroticism, Surgency (a trait linked to Extraversion)
and Closeness will have an inhibitory effect on conformity

• H5) Agreeableness will increase the tendency to yield to majority
pressure.

However, it is necessary to consider the contextual peculiarities,
illustrated by both the deindividuation explanation provided by lit-
erature (Latané, 1981; Postmes et al., 2001; Tsikerdekis, 2013), and the
theoretical framework supporting the idea that real and virtual iden-
tities are not consistent (Kim & Sherman, 2007), that highlight the lack
of saliency of personality traits in anonymity conditions, which may
predict a:

• H6) weak general effect of personality traits, especially if measured
with scales calibrated to assess “real life” traits.

Finally, since the experiment was conducted both in group and
single (i.e., physical isolation) conditions, according to the existing
literature that illustrates how the mere presence of other people can
affect an individual’s performance (Markus, 1978), we expect:

• H7) Physical isolation and group conditions to produce significantly
different behavioral outcomes.

2. Method

In order to analyze the variables and dimensions of interests, the
experiment was structured as follows. To analyze the anonymity effect
on conformity, we manipulated anonymity levels making the subjects
perform the experiment in either full or partial anonymity (i.e., anon-
ymity vs nonymity). In the full anonymity condition, the participants
were distinguished from the other group members by a number re-
presenting their response order, while in the nonymity condition they
had to provide their name and surname and could see the others’. To
test the physical isolation variable, we made the subjects perform the
experiment alone (physical isolation) or with other experimental sub-
jects in the same room (group condition). In the group condition, the
subjects were not interacting with each other but with other agents: the
group of confederates in the platform was composed by programmed
bots that in some trials provided the correct answer, and in some other
the wrong one. In order to induce normative influence, we adapted
Asch’s original line-judgment task for an online support and adminis-
tered it as first task (Asch, 1956). We also maintained the original

pattern in making the confederates provide wrong and correct answers.
Adopting the structure of the classic Asch’s experiment, we designed
two brand new tasks, respectively labeled “cultural” and “appercep-
tive”, in order to manipulate ambiguity both between tasks and among
the single items. The cultural task consisted in a target word (primer)
associated with three possible answer options more or less semantically
related (targets). The apperceptive task, instead, consisted in three
different combinations of real and invented words (i.e., condition A:
real primer word vs invented words as answer option; condition B:
invented primer word vs real words as answer option; condition C:
invented prime word vs invented words as answer option). In order to
measure the informational influence effects, we first estimated the
items’ entropy, defined as an inverse function of the probability to
observe a certain association between the prime and the target. The
entropy of each item, measured by means of a preliminary survey ad-
ministered to an ad hoc sample, represents a quantitative estimation of
the “lack degree” of information contained by each item. A study on the
voting tendencies related to conformity, hypothesized this factor to be
inversely related to entropy, since the more predictable the behavior is
(i.e., low entropy), the higher is the tendency to conform (Coleman,
2004). Nevertheless, such result describes the behavior of a subject
under a direct majority pressure. In our study we exposed the experi-
mental subjects to a constant majority pressure always towards a more
entropic answer. In this way, the cultural and apperceptive tasks, in-
vestigate the relation between entropy of the choice, and the informa-
tional influence dynamics.

2.1. Sampling and participants

The research was conducted in accordance with the guidelines for
the ethical treatment of human participants of the Italian Psychological
Association (AIP). The participants were recruited with a snowball
sampling strategy. Most of them were undergraduate students from an
Italian university. All participants gave their consent to participate and
had the possibility to withdraw from the experiment at any time. The
participants were 181 (76.8% identifying as female) and all of them
were over 18 years of age (age: M = 22.11, S D = 4.44). All the par-
ticipants filled out the survey and none of them withdrew during the
experiment. In order to obtain a robust approximation of the optimal
sample size, disregarding the debate about the standard sample size
estimation for GLMM (Bolker et al., 2009), we conducted a power
analysis by reducing the hypotheses to the case of two samples’ mean
comparison under a 2-sided equality hypothesis (eqs. (1)–(3)) (Chow,
Shao, Wang, & Lokhnygina, 2017). The results are reported in Table 1.

⎜ ⎟=

⎛
⎝

+ ⎞
⎠

⎛
⎝

+

−
⎞
⎠

− −
n

K
σ

Z Z

μ μ

1

1
b

β

a b

1 1σ2

(1)

with

− = − + − −− −( ) ( )β ϕ Z Z ϕ Z Z1 α α1 2 1 2 (2)
and

Table 1
Sample size estimation using the variable Conformity as dependent measure, to
compare 2 means from 2 samples with 2 sided equality hypothesis, requiring a
Power (1 − β) of 80%, and a Type I Error confidence level (α) of 5%.

Dimension Mean test
(SD)

Control mean
(SD)

K Na/Nb Sample size

Required Available

Anonymity 18%
(11%)

15% (7%) 1.06 86 88

Physical Isolation 18%
(10%)

14% (7%) 0.5 106 120

S. Coppolino Perfumi et al. Computers in Human Behavior 92 (2019) 230–237

232

=
−

+
Z

μ μ

σ
A B

n n
1 1
a b (3)

where, =K n
n

a
b
, σ is the standard deviation, Φ is the standard Normal

distribution function, −ϕ 1 is the standard Normal quantile function, α is
Type I error, and β is Type II error, meaning 1 − β is power. This
analysis revealed that approximately 180 participants would be needed
to achieve 80% power (1 − β) at a 0.05 α level (α = 0.05).

The exclusion criteria regarded any type of psychiatric diagnosis
and a lack of fluency in the Italian language, since the cultural and
apperceptive tasks were of semantic nature. Out of 181 subjects, 61
participants performed the experiment in the group condition (groups
of six, seven or eight people), while 120 performed the experiment in
the physical isolation condition (Table 2).

The participants were also balanced according to the anonymity
condition and 93 performed the experiment in partial anonymity (i.e.,
“nonymity”), while 88 in full anonymity (Table 3).

Since the recruitment method consisted in a snowball sampling, we
have not been able to balance the subjects according to their genders
and as consequence, the majority of them identified as females (76.8%,
versus 23.2% identifying as males). This factor has been controlled
during the data analysis.

2.2. Materials and apparatus

At first, we administered a series of scales in order to determine
psychological traits and states. The scales have been chosen according
to the dimension they aim to measure and its relation to social influ-
ence. Studies have investigated the link between conformity and Big-
Five traits, showing relations between some traits and conformity
(DeYoung et al., 2002). Anxiety has been identified as a potential
predictor for conformity, while self-esteem and self-efficacy predict the
opposite tendency, namely nonconformity (Deutsch & Gerard, 1955).
Finally, according to the literature, a high sense of community results to
be positively related to conformity (McMillan & Chavis, 1986). For
these reasons, we chose scales that measure the aforementioned di-
mensions:

• Five Factor Adjective Short Test (5-FasT) (Giannini, Pannocchia,
Grotto, & Gori, 2012), a short version of the Big Five aiming to asses
personality traits. It comprises 26 dichotomous items (true-false).
All the subscales present a good reliability (Neuroticism = 0.78;
Surgency = 0.73; Agreeableness = 0.71; Closeness = 0.71; Con-
scientiousness = 0.70)

• The State-Trait Anxiety Inventory for Adults (Spielberger & Gorsuch,
1983), a self-reporting 20-item measure on state and trait anxiety.
The items are on a 4-point Likert scale whose range goes from 1 (not
at all) to 4 (very much so). The scale appears to have an excellent
test-retest reliability (r = 0.88) (Grös, Antony, Simms, & McCabe,

2007).

• The Multidimensional Sense Of Community Scale, a 26-item scale on
which each item is on a 4-point Likert scale (4-strongly agree to 1-
strongly disagree). The scale results to have good reliability and
good construct validity (Cronbach Alpha’s from 0.61 to 0.80)
(Prezza, Pacilli, Barbaranelli, & Zampatti, 2009)

• The Rosenberg’s Self-Esteem Scale, a 10-item scale on which each
item is on a 4-point Likert scale (4-strongly agree to 1-strongly
disagree). The scale has an excellent internal consistency (coeffi-
cient of reproducibility of .92), and stability (0.85 and 0.88 on a 2
weeks test-retest) (Rosenberg, 1965).

• The General Self-Efficacy Scale (Sibilia, Schwarzer, & Jerusalem,
1995), a 10-item scale with items on a 4-point Likert scale (1-not at
all true, 4-exactly true). The scale has a good reliability with
Cronbach Alphas’ ranging from 0.76 to 0.90 (Schwarzer &
Jerusalem, 2010).

For what concerns the experiment, besides resizing Asch’s visual
task (Asch, 1956) for online supports, we created the cultural and ap-
perceptive tasks, of semantic nature: examples of cultural and apper-
ceptive tasks items are in Fig. 1.

Within the two tasks, we calculated the item’s entropy, in order to
mathematically assess the ambiguity of the stimuli. We presented the
cultural items to a sample of 71 subjects and the apperceptive to 79
subjects, collected their answers and calculated frequencies and per-
centage. On the basis of the latter, we proceeded to calculate the en-
tropy for items i, using an equation (4) with pkj = (Σ

n
i=1 r

k
i )/n, and “n”

indicating the respondents to item k.

∑= −
=

E p logpk
j

j
k

j
k
1

3

(4)

Finally, according to the median, we divided the items in high and
low entropy (Fig. 1). For what concerns the cultural and apperceptive
items, the correct answer was the most chosen during the pre-test, so,
when the majority gave a unanimous incorrect answer, they picked the
least chosen option. However, differently from Asch’s task, in some
cases we randomized the majority’s choices in order to make the in-
teraction more believable. The experiment was composed by 20 Asch-
task items, 45 cultural items and 45 apperceptive items, for a total of
110. The experiment was performed on an online software graphically
based on the Crutchfield apparatus (Crutchfield, 1955), designed by us
on Google Scripts (Fig. 2).

The interface was designed to allow interaction between the ex-
perimental subject and six other confederates, for a total of seven ac-
tors: the experimental subject was always placed in sixth position (Asch
& Guetzkow, 1951), and the interface simulated the responses of six
other non-existing subjects. It also provided the possibility to record the
subjects’ response times and control anonymity, displaying only num-
bers associated with each group member in the full anonymity condi-
tion, and asking to provide name and surname, and showing fictional
names and surnames in the nonymity condition. The experimental
subjects could see the answers of the other fake group members beside
their name or identification, and the stimulus appeared only when their
turn came. After the experiment, we administered a questionnaire in-
vestigating the subjects’ experience, using questions based on Asch’s
post-experimental interview (Asch, 1956).

2.3. Procedure

The experiment was presented as a study on visual and semantic
perception, in order to avoid biases. The group-condition experiment
took place in a computer room, where groups of 6, 7 or 8 subjects,
performed the experiment on distantly placed computers. The physical
isolation-condition experiment, instead, took place in a laboratory,
where the participants were alone with a maximum of three

Table 2
Physical Isolation versus group conditions.

Condition Frequency Percentage

Physical Isolation [PI(1)] 120 66.3
Group Condition [PI(0)] 61 33.7
Total 181 100

Table 3
Anonymity versus Nonymity conditions.

Condition Frequency Percentage

Anonymity [FA (1)] 88 48.6
Nonymity [FA (0)] 93 51.4
Total 181 100

S. Coppolino Perfumi et al. Computers in Human Behavior 92 (2019) 230–237

233

experimenters. Every participant was given an ID code that needed to
be reported in all the three experimental phases. The first phase con-
sisted in the filling of the scales that took approximately 15 min. When
completed, the participants could start the experiment, which took
approximately 50 min to be completed. The first task was Asch’s, the
second the cultural and the third the apperceptive, and each phase was
introduced by means of an informational page with instructions. The
last phase consisted in the filling of the post-experimental ques-
tionnaire, and this phase lasted 10 min circa. When finished, the sub-
jects were informed on the real purposes of the study and were told not
to divulge details on the experiment, in order to avoid potential biases
from the other experimental subjects.

3. Results

Fig. 3 shows the different percentage of conformity in each task. In
Asch’s task, the one used to test normative influence 1,4% of the sub-
jects conformed to the majority when it gave a clearly incorrect answer.
Conformity percentages grow significantly in the cultural task, with
15,2% of subjects conforming and the highest rate is registered in the
apperceptive task, with 29,8% of conformity.

Both the cultural and the apperceptive tasks were used to test in-
formational influence and more insights on the effects of this type of
influence can be obtained by observing the results concerning entropy.
Conformity increased significantly with higher entropy, thus with more
ambiguous items (Table 4).

Since the tasks have always been presented in the same order (Asch
first, then cultural and finally apperceptive), we conducted some ana-
lysis in order to verify if any eventual learning mechanisms could have
occurred and invalidated the trustworthiness of conformity data. The

only interaction appeared between conformity and entropy but once
controlled the entropy effect, no significant learning mechanism ap-
peared, besides a slight negative effect of time on the cultural task. To
analyze the relationship between conformity, physical condition,
anonymity and personality traits, we used Generalized Linear Mixed
Models, the size effect of which results to be 77%. From the model,
emerged that conformity takes place differently whether subjects are
physically isolated, anonymous or in both conditions happening at the
same time (deindividuated). Full anonymity and physical isolation
analyzed singularly have a positive relationship with conformity, but if
these two variables interact (creating deindividuation), the relationship
becomes negative (Table 4). This analysis also provided results re-
garding the effects of personality traits, in particular, Neuroticism,
Surgency (i.e., Extraversion), Agreeableness, Closeness, Self-Efficacy
and State and Trait Anxiety.

The factors that result to be positively related to conformity are
Closeness, Self-Efficacy and State Anxiety. The traits that are negatively
related to conformity, are Neuroticism, Surgency, Agreeableness.

4. General discussion and conclusions

The results of this study could help to explain the dynamics that can
occur in online environments, where the different available platforms
allow the users to interact under different levels of anonymity, and with
known and unknown people. We found an almost non-existent effect of
normative influence when social identity is not strengthened, with only
1.4% of the subjects conforming to Asch’s task.

In our experiment, group saliency was minimal due to anonymity,
the impossibility to communicate with the other members, and the
absence of any type of information exchange (except fictional name and

Fig. 1. Example of cultural and apperceptive items. In
figure are shown three different examples of the stimuli
adopted in the experiment. In the first row there are two
examples of cultural items: in the first rectangle the primer
is associated with three options, among which one is more
semantically related than the others (low entropy), the
second example present three untied options (high entropy).
In the second row we can find two types of apperceptive
stimuli with invented words both for the primer and the
answer options.

Fig. 2. Screenshot representing the interface on which the subjects performed the experiment in the nonymity condition.

S. Coppolino Perfumi et al. Computers in Human Behavior 92 (2019) 230–237

234

surnames in the nonymity condition) concerning the group members.
Furthermore, the subject did not engage in any type of cooperative task
before the experiment, a method often used to enhance group saliency
(Postmes et al., 2001).

Thus, we confirm the existing literature on deindividuation
(Postmes et al., 2001), showing that deindividuation alone is an in-
hibitory factor for normative influence in online environments.

On the other side, when the focus is on obtaining information and
the subjects’ knowledge on a topic lacks because the task is particularly
difficult or ambiguous, even unknown users can be considered a reli-
able source, even when deprived of cues about their actual level of
knowledge. In fact, from our analysis, emerged that the strongest pre-
dictor of conformity is task ambiguity: entropy resulted to have a sig-
nificant positive effect on conformity. In the case of the present study,
entropy was modulated both within and in-between tasks, and we

registered a 15.2% of conformity in the cultural task, and a 29.8% in
the apperceptive, the most ambiguous task.

These results confirm other studies (Rosander & Eriksson, 2012)
that show the effectiveness of informational influence also in online
environments. However, new evidence emerged from the present study,
showing that two contextual characteristics can actually affect in a
complex way the effects of informational influence: full anonymity,
physical isolation, as well as their interaction (i.e., deindividuation).
Anonymity and physical isolation taken separately have a positive ef-
fect on conformity, confuting the “mere presence-effect” hypothesis, at
least in this case (Markus, 1978), but if combined, thus creating a
deindividuation state, they actually reduce conformity. In this way, we
can say that deindividuation has an inhibitory effect not only on nor-
mative influence, as theorized by the SIDE Model (Postmes et al., 2001),
but also on informational influence within CMC. These results provide
us interesting insights on the environmental and psychological elements
that can affect information-seeking behavior in online environments.
The large amount of information available on the Internet, combined
with online social dynamics often lead users not to verify the credibility
of sources, and the present study provides new insights that show that if
users are deindividuated, their tendency to trust unknown sources of
information is minor. This result has two potential implications, a so-
cially-related one and an exposure-related one. The first one is related
to the fact that such result suggests that in order to trust random in-
formation, the underlying social dynamics, namely, the perceived im-
portance and/or trust towards who is supporting such information is
crucial.

As the deindividuation perspective presented by the SIDE Model
suggests, if there is no social identification with the group members, the
effects of social influence will reduce and according to these results, this
could happen also when the push towards conformity is not strictly
related to a compliance with social norms, but rather to a need for
information.

Future research could deepen this result, for example by focusing on
the relationship between the spread of misinformation in social net-
works and informational influence, deepening how social dynamics
underlie this process, to what extent they influence information

Fig. 3. Percentages of conformity in Asch, Cultural and Apperceptive tasks and Entropy’s quadratic plot.

Table 4
Generalized Linear Mixed Model. Model’s Size Effects: 66%. ∗∗∗ = p < 0.001, ∗∗ = p < 0.01, ∗ = p < 0.05. The variables included in the model are en- tropy, anonymity, physical isolation, Neuroticism, Surgency, Agreeableness, Closeness, Self- Efficacy and state anxiety.

GLMM Best Model

Model precision Akaike∗ F Df-1 (2)

81.5% 9396.12 67.67∗∗∗ 12 (9116)

Parameter Fixed effect (F) Coefficient St. Error Student t

Entropy 672, 98∗∗∗ 8, 714 0,34 25, 94∗∗∗

Full anonymity 23, 11∗∗∗ 2, 416 0,46 5, 31∗∗∗

Physical isolation 10, 71∗∗∗ 0, 474 0,09 5, 78∗∗∗

Neuroticism 7, 38∗∗ −0, 027 0,01 −2, 72∗∗

Surgency 7, 07∗∗ −0, 032 0,01 −2, 66∗∗

Agreeableness 23, 18∗∗∗ −0, 042 0,01 −4, 81∗∗∗

Closeness 6, 79∗∗ 0, 022 0,01 2, 61∗∗

Self-efficacy 24, 09∗∗∗ 0, 046 0,01 4, 91∗∗∗

STAI-State 9, 97∗∗∗ 0, 017 0,01 3, 16∗∗∗

FA (1)∗PI(1) 24, 94∗∗∗ −0, 574 0,12 −4, 99∗∗∗

S. Coppolino Perfumi et al. Computers in Human Behavior 92 (2019) 230–237

235

acceptance, and whether other contextual factors can affect this pro-
cess, since this phenomenon is having a strong political and social
impact.

The second implication is related to the subjects’ feeling of exposure:
if they perceive that there is no way to identify them, as they are both
anonymous and physically isolated, they are more prone to disregard
the opinions they are exposed to.

Future research could investigate, for example, whether this hap-
pens because subjects try to provide their own judgment, because they
engage in explicit non-conformist behavior, or because they do not put
too much effort in completing the task.

Finally, for what concerns the effects of personality traits, the ones
which resulted to have an inhibitory effect on conformity are
Neuroticism, Surgency (i.e., Extraversion) and Agreeableness, in line
with the existing literature (DeYoung et al., 2002), while subjects with
higher scores in Closeness, Self-Efficacy and State Anxiety conformed
more.

These results however predict a small portion of the general ten-
dency to conform, so further studies are necessary to understand the
entity of the impact of personality traits on conformity and its pre-
dictability.

In line with the theoretical framework, the previous result could
support the literature stressing how personality changes when users are
online (Kim & Sherman, 2007).

Within such a background, any type of personality assessment re-
ferring to real-life personality traits could explain only a small portion
of online behavior variance, and not fit with the purpose. Future re-
search could develop new models of web-personality assessment tools
in order to measure the impact of “online personality” on social influ-
ence and conformity.

Furthermore, the study presented here has some limitations that
could be controlled in further research on the topic.

As mentioned while describing the sample, we have not been able to
balance the subjects according to genders and we have an over-
representation of people identifying as females. The more dated lit-
erature that explored the gender differences in conformist behaviors
registered higher conformity in the females (Baumeister & Sommer,
1997), while more recent studies found no differences (Rosander &
Eriksson, 2012). This could be due by the increasing push towards
gender equality which resulted in a less strict adherence to the tradi-
tional division between gender roles that especially western societies
(those in which the aforementioned studies were conducted) have ex-
perienced throughout the years.

Another limitation regards the diversity of the pool of participants.
For linguistic reasons related to the semantic nature of two of the

three tasks, the participants had to be fluent in Italian, and this resulted
in having mostly Italians taking part to the experiment, who, in the
nonymity condition, interacted with bots to which were given Italian-
sounding names and surnames.

We believe that these results can be generalized to other contexts
and similar countries, but we must consider that cultural differences
shaping the behavior in different ways may appear if the study is re-
plicated elsewhere.

First and foremost, according to the literature, the perception to-
wards conformity is different in individualistic and collectivistic cul-
tures, where in the former it is a negatively connoted behavior, while in
the latter it is generally seen more positively (Bond & Smith, 1996),
therefore, with a broader pool of participants, different patterns might
emerge.

In addition, according to the context, the level of contact with
people having different backgrounds, and the potential prejudices or
negative attitudes towards some social groups that the experimental
subjects might present, there could be different levels of identification
with the group members, if more information that indicates diversity is
given to the participants. This factor could be interesting to control and
analyze in further studies.

In the same way, at a broader level, the multiculturalism, general
openness, political and social situation of the context could also affect
the subjects’ behavior in relation to the building of in-group and out-
group perception towards the group members.

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• Deindividuation effects on normative and informational social influence within computer-mediated-communication

Introduction
Theoretical framework
Overview and predictions
Method
Sampling and participants
Materials and apparatus
Procedure
Results
General discussion and conclusions
References

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Social Influence

ISSN: 1553-4510 (Print) 1553-4529 (Online) Journal homepage: https://www.tandfonline.com/loi/psif20

Moral conformity in online interactions: rational
justifications increase influence of peer opinions
on moral judgments

Meagan Kelly, Lawrence Ngo, Vladimir Chituc, Scott Huettel & Walter
Sinnott-Armstrong

To cite this article: Meagan Kelly, Lawrence Ngo, Vladimir Chituc, Scott Huettel & Walter
Sinnott-Armstrong (2017) Moral conformity in online interactions: rational justifications
increase influence of peer opinions on moral judgments, Social Influence, 12:2-3, 57-68, DOI:
10.1080/15534510.2017.1323007

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Social influence, 2017
Vol. 12, noS. 2–3, 57–68
https://doi.org/10.1080/15534510.2017.1323007

Moral conformity in online interactions: rational justifications
increase influence of peer opinions on moral judgments

Meagan Kellya,b†, Lawrence Ngoa,c,d,g†, Vladimir Chituch, Scott Huettele,f,g and
Walter Sinnott-Armstronga,b,e

aKenan institute for ethics, Duke university, Durham, nc, uSa; bDepartment of Philosophy, Duke university,
Durham, nc, uSa; cMedical Scientist Training Program, Duke university School of Medicine, Durham, nc, uSa;
dDepartment of neurobiology, Duke university School of Medicine, Durham, nc, uSa; ecenter for cognitive
neurosciences, Duke university, Durham, nc, uSa; fDepartment of Psychology and neuroscience, Duke
university, Durham, nc, uSa; gBrain imaging analysis center, Duke university, Durham, nc, uSa; hSocial
Science Research institute, Duke university, Durham, nc, uSa

ABSTRACT
Over the last decade, social media has increasingly been used as a
platform for political and moral discourse. We investigate whether
conformity, specifically concerning moral attitudes, occurs in these
virtual environments apart from face-to-face interactions. Participants
took an online survey and saw either statistical information about
the frequency of certain responses, as one might see on social media
(Study 1), or arguments that defend the responses in either a rational
or emotional way (Study 2). Our results show that social information
shaped moral judgments, even in an impersonal digital setting.
Furthermore, rational arguments were more effective at eliciting
conformity than emotional arguments. We discuss the implications of
these results for theories of moral judgment that prioritize emotional
responses.

Introduction

People conform to a blatantly erroneous majority opinion, even on a simple perceptual
task (Asch, 1956). Although a large body of research in social psychology has elucidated
some of the varying conditions under which conforming behavior occurs – such as social
setting, type of judgment, number and group membership of the confederates – contention
remains about exactly what the conditions are (Bond & Smith, 1996).

Changes in how people interact socially – from synchronous in-person conversations
to asynchronous and abstract digital communication – present new environments for con-
formity. Research predating the development of anonymous online settings suggests that,
without direct, face-to-face contact, there won’t be the same level of pressure to conform
(e.g., Allen, 1966; Deutsch & Gerard, 1955; Levy, 1960). Furthermore, early research dur-
ing the development of online spaces suggest that, without nonverbal cues such as body

KEYWORDS
conformity; morality;
reasoning; emotion; social
media

ARTICLE HISTORY
Received 26 august 2016
accepted 18 april 2017

© 2017 informa uK limited, trading as Taylor & francis Group

CONTACT Walter Sinnott-armstrong ws66@duke.edu, walter.sinnott-armstrong@duke.edu
†equal contribution.

mailto: ws66@duke.edu

mailto: walter.sinnott-armstrong@duke.edu

http://www.tandfonline.com

http://crossmark.crossref.org/dialog/?doi=10.1080/15534510.2017.1323007&domain=pdf

58 M. KELLY ET AL.

language or prosody, digital communication will alter the ways in which we exchange infor-
mation, communicate norms, and exert persuasive influence (Bargh & McKenna, 2004).
Nonetheless, in certain online contexts, other studies have shown that laws of social influ-
ence, such as the foot-in-the-door technique, still hold in purely virtual settings (Eastwick
& Gardner, 2009), and merely providing participants with numerical consensus information
can change prejudicial beliefs about various racial groups (Stangor, Sechrist, & Jost, 2001)
and the obese (Puhl, Schwartz, & Brownell, 2005). This suggests that, while there may have
been initial doubts about the extent of conformity in anonymous online contexts, these new
virtual spaces remain susceptible to social influence.

Prior research has also raised questions about whether conformity operates differently
within certain domains, such as moral or evaluative judgments. Traditional philosophical
views (e.g., Aristotle, 1941; Kant, 1996) emphasize that moral judgments should ideally be
free from social influences, depending only one’s own judgment. In line with this ideal, more
recent psychological experimentation suggests that people at least sometimes are less likely
to conform when they have a strong moral basis for an attitude (Hornsey, Majkut, Terry, &
McKimmie, 2003). In contrast, however, other studies have shown that at least some moral
opinions can be influenced by social pressure in small group discussions (Aramovich, Lytle,
& Skitka, 2012; Kundu & Cummins, 2012; Lisciandra, Postma-Nilsenová, & Colombo,
2013), and information about the distribution of responses elicits conformity in deonto-
logical, but not consequentialist, responses to the Trolley problem (Bostyn & Roets, 2016).
Taking these ideas together, we were interested in whether the mere knowledge of others’
opinions online would produce conformity regarding moral issues, particularly in online
contexts.

Study 1: impersonal statistics influence moral judgments

In Study 1, we examined participants’ sensitivity to anonymous moral judgments regard-
ing ethical dilemmas. We presented participants with two stories, along with statistical
information about how other participants had responded. Unlike other research providing
distributions of responses (e.g., Bostyn & Roets, 2016) this information is similar to what
users might see on a social media website like Twitter, Facebook, or Reddit, where users
can see numerical information about how other users reacted to some opinion (e.g., ‘15
users liked this post’ or ‘35 users favorited this tweet’). While we provide no information
about what proportion of participants responded this way to each scenario, this mirrors
the experience of being in an online context where we are unaware of how many users have
seen a post without reacting.

Method

Participants
Participants were recruited through the online labor market Amazon Mechanical Turk
(MTurk) and redirected to Qualtrics to complete an online survey. All participants provided
written informed consent as part of an exemption approved by the Institutional Review
Board of Duke University. Each participant rated one of two scenarios; 302 participants
rated Scenario A, while 290 participants rated Scenario B. Participants were restricted to
those located in the US with a task approval rating of at least 80%. Although no demographic

SOCIAL INFLUENCE 59

information was collected on our participants specifically, a typical sample of MTurk users
is considerably more demographically diverse than an average American college sample
(36% non-White, 55% female; mean age = 32.8 years, SD = 11.5; Buhrmester, Kwang, &
Gosling, 2011). Numerous replication studies have also demonstrated that data collected
on MTurk is reliable and consistent with other methods (Rand, 2012). Participants were
compensated $.10 for their involvement.

Materials
Participants were randomly assigned to one of two scenarios. Scenario A, one of Haidt’s
classic moral scenarios, describes a family that eats their dead pet dog (Haidt, Koller, & Dias,
1993). Scenario B involves the passengers of a sinking lifeboat that sacrifice an overweight,
injured passenger. (See Table 1 for full text of scenarios.) These scenarios were chosen partly
because they fall under different moral foundations (Haidt & Graham, 2007). Because the
foundations have been shown to exhibit dissimilar properties in other studies (e.g., Young
& Saxe, 2011), we were interested in how the degree of conformity might vary in a scenario
involving harm violations versus purity violations.

Procedure
Participants read an ethical dilemma and were asked how morally condemnable the agent’s
actions were. Ratings were made on an 11-point Likert scale from 0 (completely morally
acceptable) to 10 (completely morally condemnable). Participants were randomly assigned to
one of three conditions in this survey. Two of the conditions contained a prime to induce
conformity by providing an established opinion about the scenario. The form of that prime
mirrored that seen on many social media websites (e.g., Facebook): it described the number
of people who provided a given rating when viewing a similar scenario. For Scenario A,
participants read the following: ‘58 people who previously took this survey rated it as morally
condemnable [acceptable]’. Participants read an identical statement for Scenario B, except
they were told that 65 people previously took the survey. To ensure that no deception was
used, these numbers of people had indeed rated these scenarios that way in a previous
experiment.

The final condition served as a baseline and contained no prime; participants merely read
and rated the moral dilemma. This design was repeated in separate samples for scenarios
A and B. While the core of the paradigm remained constant throughout our experiments,
the survey from Study 1 Scenario B also contained a follow-up question measuring level of
confidence and a catch question about details from the scenario.

Table 1. Scenarios detailing moral violations in the purity (Scenario a) and harm (Scenario B) domains.

Scenario  
a a family’s dog was killed by a car in front of their house. They had heard that dog meat was delicious, so

they cut up the dog’s body and cooked it and ate it for dinner
B a cruise boat sank. a group of survivors are now overcrowding a lifeboat, and a storm is coming. The

lifeboat will sink, and all of its passengers will drown unless some weight is removed from the boat.
nobody volunteers. Ten passengers are so small that two of them would have to be thrown overboard
to save the rest. However, one passenger is very large and seriously injured. if the ten small passengers
throw the very large passenger overboard, then he will drown but the others will survive. They throw
the large passenger overboard

60 M. KELLY ET AL.

Results

We performed a one-way ANOVA on moral ratings by condition for each scenario. In
Scenario A, moral ratings differed significantly across three conditions, [F(2, 299) = 3.78,
p = .024, �2

p
 = .025]. Post-hoc Tukey tests of the three conditions indicated that the con-

demnable group (M = 7.09, SD = 2.98) gave significantly higher ratings (more condemnable)
than the acceptable group (M = 5.80, SD = 3.67), p = .019, d = .39 (Figure 1). Comparisons
between the baseline group (M = 6.26, SD = 3.47) and the other two groups were not sig-
nificant. The same results were obtained for Scenario B: moral ratings differed significantly
across three conditions, [F(2,287) = 4.28, p = .015, �2

p
 = .029]. Post-hoc Tukey tests of the

three conditions indicated that the condemnable group (M = 6.08, SD = 2.90) gave signifi-
cantly higher ratings (more condemnable) than the acceptable group (M = 4.82, SD = 3.08),
p = .010, d = .42 (Figure 1). Comparisons between baseline group (M = 5.43, SD = 2.97)
and the other two groups were not significant. For illustrative purposes all figures show the
average difference from baseline for each condition.

Discussion

We found manipulations containing sparse statistical data about other participants’ attitudes
were effective in inducing conformity in moral judgments. Though early research in con-
formity suggested that face-to-face interactions were critical, and both philosophical and
psychological writing on moral judgments suggest it should be free from social influence,
these results show that all that is required to induce conformity in moral judgments is to
provide statistical information about how others responded. Even subtle social information
in anonymous contexts seems to affect moral judgments.

Figure 1.  Statistical information about other participants’ moral judgments significantly influences
individual responses.
note: error bars represent standard errors. *p < .05.

SOCIAL INFLUENCE 61

Having observed conformity to manipulations containing only statistical information,
we were next interested in how different kinds of arguments, specifically emotional and
rational arguments, might be more or less effective at influencing moral judgments.

Study 2: rational arguments elicit more conformity than emotional
arguments

Having observed conformity to primes using mere statistical information, we were inter-
ested in whether the effect could be strengthened by the addition of different types of argu-
ments: those containing emotionally charged language to appeal to participants’ feelings or
arguments using reasoning referring to consequences or moral principles. The distinction
between emotional and rational arguments reflects some of the core predictions put forth
by prominent psychological models of moral judgment. In the Social Intuitionist Model
(SIM), for example, ‘moral intuitions (including moral emotions) come first and directly
cause moral judgments’ (Haidt, 2001, p. 814), while reasoning is purely a post hoc defense
of those emotional intuitions. The SIM predicts that moral conformity would only manifest
by altering others’ emotional intuitions, thus in order to change what people think about a
moral issue, they must first change how they feel.

This prediction is supported by a host of studies that measure changes in moral opinions
after manipulating emotions and reasoning (for a review, see Avramova & Inbar, 2013).
For example, inducing positive emotions through funny videos (Valdesolo & DeSteno,
2006), encouraging emotion regulation (Feinberg, Willer, Antonenko, & John, 2012), and
prompting longer reflection (Paxton & Greene, 2010) all generated less harsh moral judg-
ments. Furthermore, moral outrage from one scenario may spill over into harsher judgments
of subsequent scenarios (Goldberg, Lerner, & Tetlock, 1999), and emotion drives higher
ascription of intentionality in cases involving negative consequences (Ngo et al., 2015).
Recent work utilizing virtual reality also demonstrates a discrepancy between hypothetical
moral judgments and moral decisions taken in virtual environments, and this discrepancy
seems modulated by emotional responses (Francis et al., 2016; Patil, Cogoni, Zangrando,
Chittaro, & Silani, 2014). Other work, for example, suggests that emotions are instrumental
for driving moral behavior (for a review, see Teper, Zhong, & Inzlicht, 2015). Therefore, this
literature suggests that emotional manipulations would be particularly effective in swaying
moral attitudes.

In accordance with these findings, we hypothesized that arguments appealing to partic-
ipants’ emotions would affect their judgments more than arguments citing abstract princi-
ples, rights, or reasons. To test this hypothesis, we gave participants emotional or rational
justifications for why the dilemma was either morally acceptable or morally condemnable
according to previous participants.

Method

Participants
Again, participants were recruited online from Amazon Mechanical Turk and redirected
to a survey on Qualtrics. Scenario A was rated by 506 participants, and 496 participants
rated Scenario B. All participant restrictions and compensation rates were identical to
Study 1. To ensure that participants interpreted the stimuli as intended, we recruited 160

62 M. KELLY ET AL.

additional subjects via Amazon Mechanical Turk, two of which were dropped for failing
an attention check.

Procedure
Once more, participants were presented with a vignette describing a moral violation and
asked how morally wrong they believed the agent’s actions were on a scale from 0 (com-
pletely morally acceptable) to 10 (completely morally condemnable). However, in this experi-
ment, participants were randomly assigned either to a baseline or one of four experimental
conditions. The four experimental conditions arose from a 2 × 2 between-subjects factorial
design with statistical norm (condemnable vs. acceptable) as one IV, similar to Study 1,
and argument type (emotional vs. rational) as the other. The condemnable emotional
argument in Scenario B, for instance, stated: ‘75 people who previously took this survey
rated it as morally condemnable and said something similar to “Those barbaric passen-
gers committed a horrible murder!”’ Analogously, the condemnable rational argument
in Scenario B was:

75 people who previously took this survey rated it as morally condemnable and said some-
thing similar to ‘The passengers do not have the right to judge who gets thrown off. Whether
someone is large or small, injured or uninjured, it is never okay to take a life.’ (See Table 2 for
full text of Study 2 manipulations.)

The baseline condition contained no manipulations. Again, this paradigm was repeated for
scenarios A and B. The content used for the arguments represents a combination of indi-
vidual replies to a previous survey’s free response question prompting participants to either
explain the rationale behind their rating or describe their emotional response to the scenario.
To ensure that these naturalistic responses were interpreted as either rational or emotional
by our subjects, we presented participants in our post hoc test with one random argument

Table 2. Study 2 manipulations representing actual participant responses from a prior study.

Condition Scenario A Scenario B
acceptable rational fifty-eight people who previously took this

survey rated it as morally acceptable,
and said something similar to ‘The family
did not cause the dog any harm; it was
already dead. Many cultures eat dogs, and
they should not let food go to waste.’

Seventy-five people who previously took
this survey rated it as morally acceptable,
and said something similar to ‘The pas-
sengers did what they had to do to save
the most human lives. The injured man
may not have survived anyways.’

acceptable emotional fifty-eight people who previously took this
survey rated it as morally acceptable, and
said something similar to ‘i feel bad for
the poor family! They must have been
starving to have to make this decision!’

Seventy-five people who previously took
this survey rated it as morally acceptable,
and said something similar to ‘i feel bad
for the passengers because they had to
make an extremely stressful choice!’

condemnable emotional fifty-eight people who previously took this
survey rated it as morally condemnable,
and said something similar to ‘i feel
completely disgusted that this sick family
would eat a beloved pet!’

Seventy-five people who previously took
this survey rated it as morally condemna-
ble, and said something similar to ‘Those
barbaric passengers committed a horrible
murder. i am sickened by what they did!’

condemnable rational fifty-eight people who previously took this
survey rated it as morally condemnable,
and said something similar to ‘You are
supposed to respectfully mourn and
honor a dead pet’s body with a proper
burial, not abuse it.’

Seventy-five people who previously took
this survey rated it as morally condemn-
able, and said something similar to ‘The
passengers do not have the right to judge
who gets thrown off. Whether someone
is large or small, injured or uninjured, it is
never okay to take a life.’

SOCIAL INFLUENCE 63

from Scenario A and another from Scenario B in a within-subjects design. Participants
rated these arguments on a scale from 1 (‘Not at all rational [emotional]’) to 7 (‘Extremely
rational [emotional]’).

In order to compare the magnitude of conformity based on whether participants were
conforming to condemnable information or acceptable information, we converted the raw
moral ratings into a conformity index to account for the fact that the acceptable and con-
demnable conditions moved participant’s responses in opposite directions. This allows us
to compare the magnitude of conformity based on whether participants were conforming
to condemnable information or acceptable information.

To construct the conformity index, we calculated the difference in moral ratings from
the baseline and sign-normalized for condition. Thus, positive scores represented agree-
ment with the provided statistical norm, or conformity, while negative scores represented
disagreement with the statistical norm, or non-conformity/anti-conformity. First, we sub-
tracted the average of the baseline condition from each moral rating and took the absolute
value of that number (see Figure 2 for the raw differences from the baseline). Next, based
on condition, we assessed whether the difference from the baseline represented conform-
ity or non-conformity. On the moral rating scale, higher numbers corresponded to more
condemnable ratings. Therefore, if a rating in the condemnable condition was greater than
the baseline, it remained positive to represent conformity. If a rating in the condemnable
condition was less than the baseline, it was made negative to represent non-conformity.
There were no ratings in either scenario or for any condition that was exactly at the baseline.
The opposite was done for the acceptable condition, where ratings below the baseline repre-
sented conformity (and thus stayed positive), while ratings above the baseline represented
non-conformity (and thus made negative).

Figure 2. Rational arguments have a stronger effect on participants’ moral judgments than emotional
arguments.
note: error bars represent standard errors.

64 M. KELLY ET AL.

Results

Our post hoc test of argument type revealed that, on the whole, participants rated the rational
arguments as more rational (M = 4.71, SD = 1.86) than emotional (M = 4.20, SD = 2.07,
t(314) = 2.28, p = .02, d = .26) on a 7-point scale. Similarly, participants rated emotional
arguments as more emotional (M = 5.67, SD = 1.32) than rational (M = 4.13, SD = 1.88,
t(314) = 8.37, p < .0001, d = .94) on a 7-point scale. This suggests that the participants in our main experiment interpreted our stimuli as intended.

To test the role of argument type and statistical norm, we conducted a 2 (argument
type: emotional vs. rational) × 2 (statistical norm: condemnable vs. acceptable) between
subjects ANOVA. Starting with the raw scores of Scenario A (see Figure 2), we found a
main effect of statistical norm [F(1, 401) = 15.89, p < .001, �2

p
 = .038], replicating the results

of Experiment 1. There was no main effect, however, of type of argument [F(1, 401) = 1.18,
p = .28, �2

p
 = .003], though the interaction between argument type and norm was significant

[F(1, 401) = 5.94, p = .02, �2
p
 = .015].

To explore directly the extent to which each condition elicited conformity, we conducted
a 2 × 2 ANOVA using the conformity index. In Scenario A, there was a main effect of
argument type [F(1, 401) = 5.94, p = .015, �2

p
 = .015], such that the conformity index was

significantly greater for rational arguments (M = 1.09, SD = 3.42) than for emotional argu-
ments (M = .27, SD = 3.49). There was also a significant main effect of statistical norm [F(1,
401) = 5.48, p = .02, �2

p
 = .013], such that acceptable judgments elicited more conformity

(M = 1.07, SD = 3.46) than condemnable judgments (M = .28, SD = 3.45) . There was no
significant interaction, however, between statistical norm and argument type [F(1, 401) =
1.18, p = .28, �2

p
 = .003] for the conformity index.

A similar pattern of results obtained for Scenario B. Starting with the raw scores, we found
a main effect of statistical norm [F(1, 394) = 10.53, p = .001, �2

p
 = .026], again replicating

the results of Experiment 1. There was no main effect, however, of type of argument [F(1,
401) = .92, p = .337, �2

p
 = .002], though the interaction between argument type and norm

was significant [F(1, 401) = 7.18, p = .008, �2
p
 = .018].

To explore directly the extent to which each condition elicited conformity, we conducted
a 2 × 2 ANOVA using the conformity index. There was a main effect of argument type [F(1,
394) = 7.18, p = .008, �2

p
 = .018], such that the conformity index was significantly greater for

rational arguments (M = .86, SD = 2.97) than for emotional arguments (M = .08, SD = 2.81).
Here, acceptable judgments (M = .65, SD = 2.92) were no more prone to conformity than
condemnable ones (M = .29, SD = 2.91) [F(1, 394) = 1.60, p = .21, �2

p
 = .004]. Again, there

was no significant interaction between statistical norm and argument type [F(1, 394) = .92,
p = .34, �2

p
 = .002].

Discussion

When presented with either rational or emotional justifications for moral judgments, par-
ticipants conformed more to the rational justifications. These results are inconsistent with
our second hypothesis and with predictions made more broadly by the SIM (Haidt, 2001),
because our participants responded more to appeals citing reasons than to appeals citing
emotions. This is unexpected given the body of literature demonstrating that manipulations
of emotion are powerful tools in shaping judgment (Valdesolo & DeSteno, 2006; Feinberg

SOCIAL INFLUENCE 65

et al., 2012; Paxton & Greene, 2010). Furthermore, the SIM suggests that moral judgments
can only be affected by changing moral intuitions, though the model may be consistent
with these findings, since post hoc reasoning of one person, via the ‘reasoned persuasion’
link in the model, may still impact the judgments of others. The reasoned persuasion link,
however, remains largely unspecified, and it makes no predictions or claims about how
that persuasion works, nor what kinds of persuasion should be most effective. We discuss
potential explanations for our findings in the following section.

General discussion

In this paper we have shown that participants readily conformed to subtle statistical manip-
ulations of their moral judgments. Furthermore, we have provided some evidence that
arguments appealing directly to participants’ emotions did not induce conformity as strongly
as rational appeals.

In the literature on conformity, some studies have drawn a distinction between nor-
mative social motivations to conform, which are characterized by a desire to avoid social
isolation, and informational motivations, which are based on a need to be correct (Deutsch
& Gerard, 1955). Several features of our experiments suggest that the nature of conformity
in this context may be due to informational rather than social factors. First, the context of
our experiments is much less personal than in other studies, which include face-to-face
social interaction. Given the lack of social interaction and the lack of possibility for social
feedback, the likelihood that participants are responding to direct social pressure seems low.
Further, a previous study has shown that participants rely more heavily epistemologically
on their peers when the answer to a question is more ambiguous and open to interpreta-
tion (Stangor et al., 2001). The nature of moral judgment can be quite ambiguous, and the
stimuli in this experiment were designed to evoke competing intuitions. Therefore, our
participants seem to be interpreting the number of supporters as evidence for the correct
judgment about a very difficult moral question.

Additionally, contrary to the SIM and other literature on emotional manipulation, our
emotional primes were not as successful in inducing conformity as their rational counter-
parts. These results do accord well, however, with recent critics of the SIM, such as those
questioning the link between disgust and moral judgment (e.g., Landy & Goodwin, 2015;
Johnson et al., 2016). Our results also fit into a burgeoning literature exploring the role of
reasoning in moral judgment. Moral reasoning, this research suggests, can set the bound-
aries of what we consider moral (Royzman, Landy, & Goodwin, 2014), aid in discounting
intuitions with no justifications, and correct for bias (see Paxton & Greene, 2010, for a
review). Furthermore, controlling for demographic factors, the willingness to engage in
rational thinking predicts wrongness judgments of purity violations like Scenario A of our
study (Pennycook, Cheyne, Barr, Koehler, & Fugelsang, 2014).

Supporters of SIM may argue that perhaps these primes failed to make participants feel
any emotions, or perhaps participants counterreacted to what they saw as excessive expres-
sions of emotion. Even if that were the case, the arguments used were real responses given
by participants and represent ecologically valid instances of emotional persuasion in many
online settings, where the expression of emotion is done through written words rather than
the ‘emotional’ stimuli explored in other studies (e.g., Valdesolo & DeSteno, 2006). Given
the limitations of the expression of emotion through online media, our data suggest that

66 M. KELLY ET AL.

the more effective tactic for persuasion regarding moral judgments, whether on the smaller
scale between individuals or the larger scale of public opinion, may be rational appeals to
abstract principles rather than expressions of emotions. It is worth noting, however, that
our stimuli hardly capture the full breadth of emotional and rational arguments available.
Future work might explore whether this pattern holds more broadly, or only for the stimuli
in the present study.

Today, in contrast with Asch’s time, more of our social interactions and, consequently,
discussions on matters of morality and politics are conducted across digital screens rather
than face-to-face. Though it is reasonable to predict that the influence we have on each oth-
er’s opinions would be greatly diminished in this detached world, it appears that the power
of social influence is retained. The exact consequences of an increasingly interconnected
virtual web of people, ideas, and opinions remain to be seen. Future research may eluci-
date whether the robustness of conformity online will lead to good or bad consequences,
whether it be through the facilitation of advances in knowledge as with ‘The Wisdom of
Crowds’ effect (Golub & Jackson, 2010) or an amplification of erroneous noise through a
‘Groupthink’ phenomenon (Esser, 1998).

Acknowledgments

We thank Phil Costanzo for his helpful feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.

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• Abstract

Introduction
Study 1: impersonal statistics influence moral judgments
Method
Participants
Materials
Procedure
Results
Discussion

• Study 2: rational arguments elicit more conformity than emotional arguments

Method
Participants
Procedure
Results
Discussion
General discussion
Acknowledgments
Disclosure statement
References

ToSwitch or Not To Switch: Understanding Social

Influence in Online Choices

Haiyi Zhu*, Bernardo A. Huberman, Yarun Luon

Social Computing Lab

Hewlett Packard Labs

Palo Alto, California, USA

haiyiz@cs.cmu.edu; {bernardo.huberman, yarun.luon}@hp.com

ABSTRACT

We designed and ran an experiment to measure social

influence in online recommender systems, specifically how

often people’s choices are changed by others’

recommendations when facing different levels of

confirmation and conformity pressures. In our experiment

participants were first asked to provide their

preferences

between pairs of items. They were then asked to make

second choices about the same pairs with knowledge of

others’ preferences. Our results show that others people’s

opinions significantly sway people’s own choices. The

influence is stronger when people are required to make their

second decision sometime later (22.4%) than immediately

(14.1%). Moreover, people seem to be most likely to

reverse their choices when facing a moderate, as opposed to

large, number of opposing opinions. Finally, the time

people spend making the first decision significantly predicts

whether they will reverse their decisions later on, while

demographics such as age and gender do not. These results

have implications for consumer behavior research as well as

online

marketing strategies.

Author Keywords

Social influence, online choices, recommender systems.

ACM Classification Keywords

H.5.3 [Information Interfaces and Presentation]: Group and

Organization Interfaces – Collaborative computing, Web-

based interaction; K.4.4 [Computers and Society]:

Electronic Commerce – Distributed commercial

transactions.

General Terms

Experimentation.

INTRODUCTION

Picture yourself shopping online. You already have an idea

about what product you are looking for. After navigating

through the website you find that particular item, as well as

several similar items, and other people’s opinions and

preferences about them provided by the recommendation

system. Will other people’ preferences reverse your own?

Notice that in this scenario there are two contradictory

psychological processes at play. On one hand, when

learning of other’s opinions people tend to select those

aspects that confirm their own existing ones. A large

literature suggests that once one has taken a position on an

issue, one’s primary purpose becomes defending or

justifying that position [21]. From this point of view, if

others’ recommendations contradict their own opinion,

people will not take this information into account and stick

to their own choices. On the other hand, social influence

and conformity theory [8] suggest that even when not

directly, personally, or publicly chosen as the target of

others’ disapproval, individuals may choose to conform to

others and reverse their own opinion in order to restore their

sense of belonging and self-esteem.

To investigate whether online recommendations can sway

peoples’ own opinions, we designed an online experiment

to test how often people’s choices are reversed by others’

preferences when facing different levels of confirmation

and conformity pressures. We used Rankr [19] as the study

platform, which provides a lightweight and efficient way to

crowdsource the relative ranking of ideas, photos, or

priorities through a series of pairwise comparisons. In our

experiment participants were first asked to provide their

preferences between pairs of items. Then they were asked

to make second choices about the same pairs with the

knowledge of others’ preferences. To measure the pressure

to confirm people’s own opinions, we manipulated the time

before the participants were asked to make their second

decisions. And in order to determine the effects of social

pressure, we manipulated the number of opposing

opinions

that the participants saw when making the second decision.

Finally, we tested whether other factors (i.e. age, gender

and decision time) affect the tendency to revert.

Our results show that other people’s opinions significantly

sway people’s own choices. The influence is stronger when

people are required to make their second decision later

* Haiyi Zhu is currently a PhD candidate in Human

Computer Interaction Institute at Carnegie Mellon

University. This work was performed while she was a

research intern at HP labs.

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise,

or republish, to post on servers or to redistribute to lists, requires prior

specific permission and/or a fee.

CHI 2012, May 5-10, 2012, Austin, TX, USA.

Copyright 2012 ACM xxx-x-xxxx-xxxx-x/xx/xx…$10.00.

(22.4%) rather than immediately (14.1%) after their first

decision. Furthermore, people are most likely to reverse

their choices when facing a moderate number of opposing

opinions. Last but not least, the time people spend making

the first decision significantly predicts whether they will

reverse their decisions later on, while demographics such as

age and gender do not.

The main contribution of the paper is that we designed and

ran an experiment to understand the mechanisms of social

influence in online recommender systems. Specifically, we

measured the impact of others’ preferences on people’s own

choices under different conditions. The results have

implications for consumer behavior research and online

marketing strategies.

RELATED WORK

Confirming Existing Opinions

Confirmation of existing opinions is a long-recognized

phenomenon [21]. As Francis Bacon stated several

centuries ago [2]:

“The human understanding when it has once

adopted an opinion (either as being received

opinion or as being agreeable to itself) draws all

things else to support and agree with it. Although

there be a greater number and weight of instances

to be found on the other side, yet these it either

neglects and despises, or else by some distinction

sets aside and rejects”

This phenomenon can be explained by Festinger’s

dissonance theory: as soon as individuals adopt a position,

they favor consistent over inconsistent information in order

to avoid dissonance [11].

A great deal of empirical studies supports this idea (see [21]

for a review). Many of these studies use a task invented by

Wason [30], in which people are asked to find the rule that

was used to generate specified triplets of numbers. The

experimenter presents a triplet, and the participant

hypothesizes the rule that produced it. The participants then

test the hypothesis by suggesting additional triplets and

being told whether it is consistent with the rule to be

discovered. Results show that people typically test

hypothesized rules by producing only triplets that are

consistent with them, indicating hypothesis-determined

information seeking and interpretation. Confirmation of

existing opinions also contributes to the phenomenon of

belief persistence. Ross and his colleagues showed that

once a belief or opinion has been formed, it can be very

resistant to change, even after learning that the data on

which the beliefs were originally based were fictitious [25].

Social conformity

In contrast to confirmation theories, social influence

experiments have shown that often people change their own

opinion to match others’ responses. The most famous

experiment is Asch’s [1] line-judgment conformity

experiments. In the series of studies, participants were

asked to choose which of a set of three disparate lines

matched a standard, either alone or after 1 to 16

confederates had first given a unanimous incorrect answer.

Meta-analysis showed that on average 25% of the

participants conformed to the incorrect consensus [4].

Moreover, the conformity rate increases with the number of

unanimous majority. More recently, Cosley and his

colleagues [10] conducted a field experiment on a movie

rating site. They found that by showing manipulated

predictions, users tended to rate movies toward the shown

prediction. Researchers have also found that social

conformity leads to multiple macro-level phenomenons,

such as group consensus [1], inequality and unpredictability

in markets [26], unpredicted diffusion of soft technologies

[3] and undermined group wisdom [18].

Latané proposed a theory [16] to quantitatively predict how

the impact of the social influence will increase as a function

of the size of the influencing social source. The theory

states that the relationship between the impact of the social

influence (I) and the size of the influencing social source (N)

follows a negative accelerating power function
[16]. The theory has been empirically
supported by a meta-analysis of conformity experiments

using Asch’s line-judgment task [4].

There are informational and normative motivations

underlying social conformity, the former based on the

desire to form an accurate interpretation of reality and

behave correctly, and the latter based on the goal of

obtaining social approval from others [8]. However, the two

are interrelated and often difficult to disentangle

theoretically as well as empirically. Additionally, both

goals act in service of a third underlying motive to maintain

one’s positive self-concept [8].

Both self-confirmation and social conformity are extensive

and strong and they appear in many guises. In what follows

we consider both processes in order to understand users’

reaction to online recommender systems.

Online recommender systems

Compared to traditional sources of recommendations –

peers such as friends and coworkers, experts such as movie

critics, and industrial media such as Consumer Reports,

online recommender systems combined personalized

recommendations sensitive to people’s interests and

independently reporting other peoples’ opinions and

reviews. One popular example of a successful online

recommender system is the Amazon product recommender

system [17].

Users’ reaction to recommender system

In computer science and the HCI community, most research

in recommender systems has focused on creating accurate

and effective algorithms for a long time (e.g. [5]). Recently,

researchers have realized that recommendations generated

by standard accuracy metrics, while generally useful, are

not always the most useful to users.[20] People started

building new user-centric evaluation metrics [24,32]. Still,

there are few empirical studies investigating the basic

psychological processes underlying the interaction of users

with recommendations; and none of them addresses both

self-confirmation and social conformity. As mentioned

above, Cosley and his colleagues [10] studied conformity in

movie rating sites and showed that people’s rating are

significantly influenced by other users’ ratings. But they did

not consider the effects of self-confirmation or the effects

of different levels of social conformity pressures. Schwind

et al studied how to overcome users’ confirmation bias by

providing preference-inconsistent recommendations [28].

However, they represented recommendations as search

results rather than recommendations from humans, and thus

did not investigate the effects of social conformity.

Furthermore, their task was more related to logical

inference rather than purchase decision making.

In the area of marketing and customer research, studies

about the influence of recommendations are typically

subsumed under personal influence and word-of-mouth

research [27]. Past research has shown that word-of-mouth

plays an important role in consumer buying decisions, and

the use of internet brought new threats and opportunities for

marketing [27,14,29]. There were several studies

specifically investigating social conformity in product

evaluations [7,9,23]. Although they found substantial

effects of others’ evaluations on people’s own judgments,

the effects were not always significantly stronger when the

social conformity pressures are stronger
1
. In Burnkrant and

Cousineau’s [7] and Cohen and Golden’s [9] experiments,

subjects were exposed to evaluations of coffee with high

uniformity or low uniformity. Both results showed that

participants did not exhibit significantly increased

adherence to others’ evaluation in the high uniformity

condition (although in Burnkrant’s experiments, the

participants significantly recognized the difference between

high and low uniformity). On the other hand, in Pincus and

Waters’s experiments (college students rated the quality of

one paper plate while exposed to simulated quality

evaluations of other raters), it was found that conformity

1
Unlike other conformity experiments such as line-

judgment where the pressure of social conformity is

manipulated by increasing the number of “unanimous”

majority, experiments about social influence in product

evaluation [7, 9, 23] usually manipulate the pressure of

social influence by changing the degree of uniformity of

opinions. As discussed in [9], since it is seldom that no

variation exists in the advice or opinions in reality, the latter

method is more likely to stimulate participants’ real

reactions. We also use the latter method in our experiment

by manipulating the ratio of opposing opinions versus

supporting

opinions.

effects are stronger when the evaluations are more

uniform[20].

In summary, while previous research showed that others’

opinions can influence people’s own decisions, none of that

research addresses both the self-confirmation and social

conformity mechanism that underlie choice among several

recommendations. Additionally, regarding to the effects of

increasing social conformity pressures, experiments using

Asch’s line-judgment tasks supported that people are more

likely to be influenced when facing stronger social

pressures, while the findings of studies using product

evaluation tasks were mixed.

Our experiments address how often people reverse their

own opinions when confronted with others people

preferences, especially when facing different levels of

confirmation and conformity pressures. The hypothesis is

that people are more likely to reverse their minds when the

reversion causes less self-inconsistency (the confirmation

pressure is weaker) or the opposing social opinions are

stronger (the conformity pressure is stronger).

a. Comparing baby pictures, not showing others’

preferences

b. Comparing loveseats, showing

others’ preferences

Figure 1. Example pairwise comparisons in Rankr

EXPERIMENTAL DESIGN

We conducted a series of online experiments. All

participants were asked to go to the website of Rankr [19]

to make a series of pairwise comparisons with or without

knowing others people’s preferences (Figure 1). The

pictures were collected from Google Images. We wanted to

determine whether people reverse their choices by seeing

others’ preferences.

Basic idea of the experiment

Participants were asked to provide their preferences

between the same pair of items twice. The first time the

participant encountered the pair, they made a

choice

without the knowledge of others’ preferences. The second

time the participant encountered the same pair, they made a

choice with the knowledge of others’ preferences. Social

influence is measured as whether people switched their

choices between the first time and second time they

encountered the same pair.

To manipulate the pressure to confirm people’s own

opinions, we changed the time between the two decisions.

In the short interval condition, people first compared two

pictures on their own and they were then immediately asked

to make another choice with available information about

others’ preferences. When the memories were fresh,

reversion leads to strong inconsistency and dissonance of

other people’s choices with their own previous ones (strong

confirmation pressure). However, in the long interval

condition, participants compared pairs of items in the

beginning of the test followed by several distractor pairs

and then followed again by the same pairs the participant

had previously compared but with augmented information

of others’ preferences. In this case, participants’ memories

of their previous choices decay, so the pressure to confirm

their own opinions is less explicit.

To manipulate the social pressure, we changed the number

of opposing opinions that the participants saw when making

the second decision. We selected four levels: the opposing

opinions were twice, five times, ten times, or twenty times

as many as the number of people who supported their

opinions.

In the following section, the details of experimental

conditions are discussed.

Conditions

The experimental design was 2 (baby pictures and loveseat

pictures) X 3 (short interval, long interval and control) X 4

(ratio of opposing opinions versus supporting opinions: 2:1,

5:1, 10:1 and 20:1). Participants were recruited from

Amazon’s Mechanical Turk and were randomly assigned

into one of six conditions (baby–short, baby-long, baby–

control, loveseat-short, loveseat-long and loveseat-control)

and made four choices with different levels of conformity

pressure.

In the baby condition, people were asked to compare

twenty-three or twenty-four pairs of baby pictures by

answering the question “which baby looks cuter on a baby

product label”. Note that the Caucasian baby pictures in

Figure 1 are examples. We displayed baby pictures from

different races in the experiment. In the loveseat condition,

the question was “your close friend wants your opinion on a

loveseat for their living room, which one do you suggest”;

and people also needed to make twenty-three or twenty-four

choices.

In the short interval condition, people first compared two
pictures on their own and they were then immediately asked
to make another choice with available information about

others’ preferences. Furthermore, we tested whether people

would reverse their first choice under four levels of social

pressure: when the number of opposing opinions was twice,

five times, ten times, and twenty times as many as the

number of people who supported their opinions. The

numbers were randomly generated
2
. Except for theses eight

experimental pairs, we also added fourteen noise pairs and

an honesty test composed of two pairs (twenty-four pairs in

total, see Figure 2 for an example). In this condition, noise

pairs also consisted of consecutive pairs (a pair with social

information immediately after the pair without social

information). However, others’ opinions were either

indifferent or in favor of the participants’ choices. We

created an honesty test to identify participants who cheated

the system and quickly clicked on the same answers. The

test consisted of two consecutive pairs with the same items

but with the positions of the items exchanged.

Participants

needed to make the same choices among these consecutive

two pairs in order to pass the honesty test. The relative

orders of experimental pairs, noise pairs, and honesty test in

the sequence and the items in each pair were randomly

assigned to each participant.

In contrast with the short interval condition where people

were aware that they reversed their choices, in the long

interval condition we manipulated the order of display and

the item positions so that the reversion was less explicit.

People first compared pairs of the items without knowing

others’ preferences, and then on average after 11.5 pairs

later we showed the participants the same pair (with the

positions of items in the pair exchanged) and others’

opinions. Similarly, with the short interval condition we

showed eight experimental pairs to determine whether

people reversed their previous choices with increasing

pressures of social influence. Additionally, we showed

thirteen noise pairs (nine without others’ preferences and

four with others’ preferences) and performed an honesty

test (see Figure 2 for an example).

2
We first generated a random integer from 150 to 200 as

total participants. Then we generate the number of people

holding different opinions according to the ratio. Here are a

few examples: 51 vs 103 (2X), 31 vs156 (5X), 16 vs 16

1

(10X) and 9 vs 181(20X).

By increasing the time between two choices, we blurred

people’s memories of their choices in order to exert a subtle

confirmation pressure. However, as people proceeded with

the experiment they were presented with new information

to process. This new information may lead them to think in

a different direction and change their own opinions

regardless of social influence. In order to control for this

confounding factor, we added a long interval control

condition, where the order of the pairs were the same as

with the long interval condition but without showing the

influence of others.

Procedures

We conducted our experiment on Amazon Mechanical Turk

(mTurk) [15]. The recruiting messages stated that the

objective of the survey was to do a survey to collect

people’s opinions. Once mTurk users accepted the task they

were asked to click the link to Rankr, which randomly

directed them to one of the six conditions. This process was

invisible to them.

First, the participants were asked to provide their

preferences about twenty-three or twenty-four pairs of

babies or loveseats. They were then directed to a simple

survey. They were asked to report their age, gender and

answer two 5-Likert scale questions. The questions were as

follows. “Is showing others’ preferences useful to you?”

“How much does showing others’ preferences influence

your response?” After filling out the survey, a unique

confirmation code was generated and displayed on the

webpage. Participants needed to paste the code back to the

mTurk task. With the confirmation code in hand we

matched mTurk users with the participants of our

experiments, allowing us to pay mTurk users according to

their behaviors. We paid $0.35 for each valid response.

Participants

We collected 600 responses. Of this number, we omitted 37

responses from 12 people who completed the experiment

multiple times; 22 incomplete responses; 1 response which

did not conform to the participation requirements (i.e. being

at least 18 years old); and 107 responses who did not pass

the honesty test. These procedures left 433 valid

participants in the sample, about 72% of the original

number. According to participant self-reporting, 40% were

females; age ranged between18 to 82 with a median age of

27 years. Geocoding
3
the ip addresses of the participants

revealed 57% were from India, 25% from USA, with the

remaining 18% of participants coming from over 34

different countries.

The numbers of participants in each condition were as

follows. Baby-short: 72; baby-long: 91; baby-control: 49;

loveseat-short:75; loveseat-long:99; loveseat-control:47.
4

People spent a reasonable amount of time on each decision

(average 6.6 seconds; median 4.25 seconds).

3
MaxMind GeoLite was used to geocode the ip addresses

which self-reports a 99.5% accuracy rate.

4
Among the 600 responses, originally 20% were assigned

for baby-strong; 20% for baby-weak; 10% for baby-control;

20% for loveseat-strong; 20% for loveseat-weak and10%

for loveseat-control. The valid responses in short interval

conditions were fewer than the ones in long interval

conditions because the short interval condition had a higher

failure rate in the honesty test. The reason might be that

short interval condition had more repetitive pairs, fewer

new items and more straightforward patterns, leading to

boredom and casual decisions, which in turn caused failure

in the honesty tests.

Experimental

pair

Experimental pair displaying

others preferences which are

against people’s previous

choice

Pair

Short interval

Long interval

Long interval control

Pair i, j Pair i, j Pair i, j

Figure 2. Example displaying orders in each condition.

Pair i, j
Pair displaying

others’ preferences

1, 2 2, 1 3, 4 3, 4 5, 6 5, 6 7, 8 7, 8 9,10 9,10

11,12

1
11,12
1

13,14

1
13,14
1

15,16

1
15,16
1

17,18

1
17,18
1

19,20

1 1

19,20

1

21,22

1
21,22
1

23,24

1
23,24
1

1, 2 3, 4 5, 6 6, 5 7, 8 9,10 11,12 13,14 15,16

1
17,18
1
19,20
1
21,22
1
23,24
1

25,26

1 1

27,28

1

8, 7 14,13

1

29,30

1

31,32

1

33,34

1

2, 1 35,36

1

16,15

1
1, 2 3, 4 5, 6 6, 5 7, 8 9,10 11,12 13,14 15,16
1
17,18
1
19,20
1
21,22
1
23,24
1
25,26
1 1
27,28
1
8, 7 14,13
1
29,30
1
31,32
1
33,34
1
2, 1 35,36
1
16,15

1

Honesty test

Honesty test

Honesty test

Among the 433 responses, 243 left comments in the open-

ended comments section at the end of the experiments.

Most of them said that they had a good experience when

participating in the survey. (They were typically not aware

that they were in an experiment).

Measures

 Reversion: whether people reverse their preferences after
knowing others’ opinion.

 Social conformity pressures: the ratio of opposing
opinions to supporting opinions.

 Decision time: the time (in seconds) people spent in
making each decision.

 Demographic information: age and gender.

 Self-reported usefulness of others’ opinions.

 Self-reported level of being influenced.

RESULTS

1. Did people reverse their opinions by others’
preferences when facing different confirmation
pressures?

Figure 3. Reversion rate by conditions.

Figure 3 shows the reversion rate as a function of the

conditions we manipulated in our experiment. First, we

found out that content does not matter, i.e., although baby

pictures are more emotionally engaging than loveseat

pictures, the patterns are the same. The statistics test also

shows that there is no significant difference between the

baby and the loveseat results (t(431)=1.35, p=0.18).

Second, in the short interval condition, the reversion rate

was 14.1%, which is higher than zero (the results of the t-

test is t(146)=6.7, p<0.001).

Third, the percentage of people that reversed their opinion

was as high as 32.5% in the long interval condition,

significantly higher than the long interval control condition

(10.1%) which measures the effects of other factors leading

to reversion regardless of the social influence during the

long interval. T-test shows that this difference is significant:

t(284)=6.5, p<0.001. We can therefore conclude that social

influence contributes approximately to 22.4% of the

reversion of opinions observed.

To summarize the results, in both the long and the short

interval conditions, others’ opinions significantly swayed

people’s own choices (22.4% and 14.1%
5
). The effect size

of social influence was larger when the self-confirmation

pressure was weaker (i.e., the time between the two choices

is larger).

2. Were people more likely to reverse their own
preferences when more people are against them?

Figure 4. Reversion rate by the ratio of opposing opinions.

Table 1. Linear regression predicting the reversion percent.

Note that the squared ratio of opposing opinions has a

significant negative value (-0.142, p=0.045).

Interestingly, we saw an increasing and then decreasing

trend when the opposing opinions became exponentially

stronger (from 2X, 5X, 10X to 20X). The condition with

the most uniform opposing opinions (20X) was not more

effective in reversing people’s own opinions than the

moderate opposing opinions (5X and 10X). The statistical

5
In order to calibrate the magnitude of our results, we point

out that our results are of the same magnitude as the classic

line-judgment experiments. According to a 1996 meta-

analysis of line-judgment experiment consisting of 133

separate experiments and 4,627 participants, the average

conformity rate is 25% [4].

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

Baby Loveseat

Short Interval

Long Interval

Long Interval
Control

0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%

2X 5X 10X 20X

Long
Interval

Short
Interval

Predictors Coef. Std.

Err.

P

value

Condition
(1-long interval;

0-short interval)

.839 .059 <.001

Ratio of opposing opinions .563 .184 .038

Square ratio of opposing

opinions

-.142 .049 .045

Intercept -2.42 .151 <.001

Adjusted R square 0.96

R
e

v
e

rs
io

n

R
e
v
e
rs
io
n

The ratio of opposing opinions to supporting opinions

test is shown in Table 1. Note that the squared ratio of

opposing opinions has a significant negative value (Coef. =

-0.142, p<0.05), suggesting that the returning effect is

statistically significant.

These results might be explained by Brehn’s finding of

psychological reactance [6]. According to Brehn, if an

individual’s freedom is perceived as being reduced or

threatened with reduction, he will become aroused to

maintain or enhance his freedom. The motivational state of

arousal to reestablish or enhance his freedom is termed

psychological reactance. Therefore if the participants

perceived the uniform opposing opinions as a threat to their

freedom to express their own opinions, their psychological

reactance might be aroused to defend and confirm their own

opinions.

These results can also be explained in terms of Wu and

Huberman’s findings about online opinion formation [31].

In their work they used the idea of maximizing the impact

that individuals have on the average rating of items to

explain the phenomenon that later reviews tend to show a

big difference with earlier reviews in Amazon.com and

IMDB.

We can use the same idea to explain our results. Social

influence in product recommendations is not just a one-way

process. People are not just passively influenced by others’

opinions but also want to maximize their impact on other

people’s future decision making (e.g., in our experiments,

according to our recruiting messages, participants would

assume that their choices would be recorded in the database

and shown to others; in real life, people like to influence

their friends and family). We assume that the influence of

an individual on others can be measured by how much his

or her expression will change the average opinion. Suppose

there are supporting opinions and opposing opinions,
and that . A person’s choice c (0 indicates
confirming his or her own choice, 1 indicates conforming to

others) can move the average percentage of opposing

opinions from to .

So the influence on the average opinion is

. A simple derivation shows that to maximize the

influence on average opinion, people need to stick to their

own choices and vote for the minority. Then their influence

gain will be stronger when the difference between existing

majority opinions and minority ones is larger. Therefore,

the motivation to exert influence on other people can play a

role in resisting the social conformity pressure and lead

people to confirm their own decisions especially when

facing uniform opposing opinions.

3. What else predicts the reversion?

We used a logistic regression model to predict the decision-

level reversion with the participants’ age, gender, self-

reported usefulness of the recommendation system, self-

reported level of being influenced by the recommendation

system and standardized first decision time (as shown in

Table 2). Note that standardized first decision time = (time

in this decision – this person’s average decision time) / this

person’s standard deviation. So “first decision time” is an

intrapersonal variable.

The results showed that age and gender do not significantly

predict reversion (p=0.407, p=0.642). Self-reported

influence level has a strong prediction power (Coef. = 0.334,

p <0.001), which is reasonable. The interesting fact is that

decision time, a simple behavioral measure, also predicts

reversion very well (Coef. = 0.323, p<0.001). The longer

people spent on the decisions, the more equivalent the two

choices are for them. According to Festinger’s theory [12]:

the more equivocal the evidence, the more people rely on

social cues. Therefore, the more time people spend on a

choice, the more likely they are to reverse this choice and

conform to others later on.

DISCUSSION

On one hand, the phenomena we found (i.e., the returning

effects of strong influence pressure) are quite different from

the classic line-judgment conformity studies [1,16]. Notice

that these experiments used questions with only one single

correct answer [1]. In contrast, in our experiment we

examined the social influence on people’s subjective

preferences among similar items, which might result in

such different phenomena. On the other hand, our findings

reconcile the mixed results of studies investigating social

conformity in product evaluation tasks (which are often

subjective tasks) [7,9,23]. Due to the returning effects,

strong conformity pressure is not always more effective in

influencing people’s evaluations compared to weak

conformity pressure.

Additionally, in our experiment we did not look at social

influence in scenarios where the uncertainty level is quite

high. For example, in settings such as searching

recommendations for restaurants or hotels where people

have never been to, the level of uncertainty is high and

people need to rely on other cues. However, in our

experimental setting people can confidently make their

Predictors Coef. Std.
Err.

P>|z|

Condition
(1-long interval;
0-short interval)

1.26 .152 <.001

Age -.006 6.89e-3 .407

Gender .067 .143 .642

Self-reported usefulness .164 .070 .020

Self-reported influence level .334 .072 <.001

Std. first decision time .323 .065 <.001

Log likelihood -657.83

Table 2. Logistic regression predicting the reversion.

choice by comparing the pictures of the settings. Therefore,

some caution is needed when trying to generalize our

results to other settings with high uncertainty.

Regarding the tasks used in the experiment, the choice of

“which baby looks cuter on a product label” involves

emotions and subjective feelings. Alternatively similar

choices include the preferences of iTune music or

YouTube’s commercial videos. In contrast, the other type of

question, “which loveseat is better”, is less emotionally

engaging. In this case people are more likely to consider the

usability factors such as color and perceived

comfortableness when making these types of choices.

Results showed that the impact of social influence on these

two different types of choices are similar and consistent,

which suggests the general applicability of the results.

There might be concern about cultural differences between

the global nature of the respondents and the Western-style

nature of the tasks. We point out that this intercultural

preference difference inherent in the participant is assumed

consistent throughout the test and therefore should not

affect a particular person’s preference changes and will not

confound the results.

During our research, we invented an experimental paradigm

to easily measure the effects of social influence on people’s

decision making (i.e., the reversion) and manipulate

conditions under which people make choices. This

paradigm can be extended to scenarios beyond those of

binary choices, to the effect of recommendations from

friends as opposed to strangers and whether social influence

varies with different visualizations for the recommendations.

LIMITATIONS & FUTURE WORK

In our experiments, we examined whether people reverse

their choices when facing different ratios of opposing

opinions versus supporting opinions (2X, 5X, 10X and

20X). In order to further investigate the relationship

between the ratio of the opposing opinions and the tendency

to revert, it would be better to include more fine-grained

conditions in the ratio of opposing opinions. The ideal

situation would be a graph with the continuous opposing

versus supporting ratio as the x-axis and the reversion rate

as the y-axis.

Also, additional manipulation checks or modification of the

design of the experiment would be needed to establish

whether processes such as psychological reactance or the

intent to influence others have been operating. For example,

the degree of perceived freedom in the task could be

measured. And it would be revealing to manipulate whether

or not people’s choices would be visible to other

participants to see whether the intention of influencing

others takes effect.

Regarding the usage of Mechanical Turk as a new source of

experimental data, we agree with Paolacci and his

colleagues that, “workers in Mechanical Turk exhibit the

classic heuristics and biases and pay attention to directions

at least as much as subjects from traditional sources” [22].

Particularly, compared to recruiting people from a college

campus, we believe the use of Mechanical Turk has a lower

risk of introducing the Hawthorne Effect (i.e., people alter

their behaviors due to the awareness that they are being

observed in experiments), which is itself a form of social

influence and might contaminate our results.

In our experiment, we used several methods such as an

honest test and IP address checking to further ensure that

we collected earnest responses from Mechanical Turk

workers. The average time they spent on the task, the

statistically significant results of the experiment and the

comments participants left all indicate that our results are

believable. However, there is still a limitation of our

honesty test. On one hand, the honesty test (the consecutive

two pairs with positions of items switched) was unable to

identify all the users who tried to cheat the system by

randomly clicking on the results, which added noise in our

data. On the other hand, the honesty test might also exclude

some earnest responses. It is possible that, immediately

after people made a choice, they regret it.

CONCLUSION & IMPLICATION

In this paper, we present results of a series of online

experiments designed to investigate whether online

recommendations can sway peoples’ own opinions. These

experiments exposed participants making choices to

different levels of confirmation and conformity pressures.

Our results show that people’s own choices are significantly

swayed by the perceived opinions of others. The influence

is weaker when people have just made their own choices.

Additionally, we showed that people are most likely to

reverse their choices when facing a moderate, as opposed to

large, number of opposing opinions. And last but not least,

the time people spend making the first decision

significantly predicts whether they will reverse their own

later on.

Our results have three implications for consumer behavior

research as well as online marketing strategies. 1) The

temporal presentation of the recommendation is important;

it will be more effective if the recommendation is provided

not immediately after the consumer has made a similar

decision. 2) The fact that people can reverse their choices

when presented with a moderate countervailing opinion

suggests that rather than overwhelming consumers with

strident messages about an alternative product or service, a

more gentle reporting of a few people having chosen that

product or service can be more persuasive than stating that

thousands have chosen it. 3) Equally important is the fact

that a simple monitoring of the time spent on a choice is a

good indicator of whether or not that choice can be reversed

through social influence. There is enough information in

most websites to capture these decision times and act

accordingly.

ACKNOWLEDGMENTS

We thank Anupriva Ankolekar, Christina Aperjis, Sitaram

Asur, Dave Levin, Thomas Sanholm, Louis Yu, Mao Ye at

HP labs and the members of the Social Computing Group at

Carnegie Mellon University for helpful feedback.

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