Psychology
Running head: LITERATURE REVIEW INSTRUCTIONS 1
PAPER II: METHODS AND RESULTS INSTRUCTIONS 7
Instructions for Paper I: Study One Literature Review Instructions (Worth 25 Points)
Purpose of Paper I: Study One Literature Review
1). Psychological Purpose
This paper serves several purposes, the first of which is helping you gain insight into research papers in psychology. As this may be your first time reading and writing papers in psychology, one goal of Paper I is to give you insight into what goes into such papers. This study one-lit review will help you a). better understand the psychology topic chosen for the course this semester (Facebook Consensus), b). learn about the various sections of an empirical research report by reading five peer-reviewed articles (that is, articles that have a Title Page, Abstract, Literature Review, Methods Section, Results Section, and References Page), and c). use information gathered from research articles in psychology to help support your hypotheses for your first study this semester (Facebook Consensus). Of course, you’ll be doing a study two literature review later in the semester, so think of this Paper I as the first part of your semester long paper. I recommend looking at the example Paper V, actually, to see what your final paper will look like. It might give you a better idea about how this current paper (as well as Papers II, III, and IV) all fit together into your final paper of the semester.
In this current paper (Paper I), you will read five research articles, summarize what the authors did and what they found, and use those summaries to support your Facebook Consensus study hypothesis. IMPORTANT: Yes you need five references, but keep in mind that you can spend a lot of time (a page or two!) summarizing one of them and a sentence or two summarizing others. Thus spend more time on the more relevant articles!
For this paper, start your paper broadly and then narrow your focus (think about the hourglass example provided in the lecture). My suggestion is to give a brief overview of your paper topic in your opening paragraph, hinting at the research variables you plan to look at for study one. Your next paragraphs will review prior research (those five references required for this paper). Make sure to draw connections between these papers, using smooth transitions between paragraphs. Your final paragraphs should use the research you just summarized to support your research hypothesis. And yes, that means you MUST include your study predictions (which we provided in the researcher instructions and the debriefing statement. Use them!). In other words, this first paper will look like the literature reviews for the five research articles you are summarizing for this assignment. Use the articles you are using as references as examples! See what they did and mimic their style! Here, though, you will end the paper after providing your hypothesis. In Paper II, you will pick the topic up again, but in that future paper you will talk about your own study methods and results.
2). APA Formatting Purpose
The second purpose of Paper I: Study One Literature Review is to teach you proper American Psychological Association (APA) formatting. In the instructions below, I tell you how to format your paper using APA style. There are a lot of very specific requirements in APA papers, so pay attention to the instructions below as well as Chapter 14 in your textbook!
3). Writing Purpose
Finally, this paper is intended to help you grow as a writer. Few psychology classes give you the chance to write papers and receive feedback on your work. This class will! We will give you extensive feedback on your first few paper in terms of content, spelling, and grammar. You will even be able to revise aspects of Paper I and include them in future papers (most notably Papers III and V). My hope is that you craft a paper that could be submitted to an empirical journal. Thus readers may be familiar with APA style but not your specific topic. Your job is to educate them on the topic and make sure they understand how your study design advances the field of psychology.
In fact, your final paper in this class (Paper V), might be read by another professor at FIU and not your instructor / lab assistant. Write your paper for that reader – the one who may know NOTHING about your topic and your specific study.
Note: The plagiarism limit for this paper is 30% (though this excludes any overlap your paper might have with regard to citations, references, and the hypotheses). Make sure your paper falls under 30% (or 35% if including predictions).
Note: I am looking for 2.5 pages minimum with predictions.
Instructions for Paper I: Study One Literature Review (Worth 25 Points)
Students: Below are lengthy instructions on how to write your study one literature review. There is also a checklist document in Canvas, which I recommend you print out and “check off” before submitting your paper (we are sticklers for APA format, so make sure it is correct! We mark off if you have a misplaced “&”, so carefully review all of your work and use the checklist! It will help). Also look at the example paper in Canvas. It will show you what we expect.
1. Title Page: I expect the following format. (5 Points)
a. You must have a header and page numbers on each page.
i. If you don’t know how to insert headers, ask your instructor or watch this very helpful video!
.
ii. The header goes at the top of the paper and it is left justified.
1. Use “Insert Headers” or click on the top of the page to open the header. Make sure to select the “Different first page” option so that your title page header will differ from subsequent pages
2. The R in Running head is capitalized but the “h” is lower case, followed by a colon and a short title (in ALL CAPS). This short running head title can be the same one as the rest of your paper or it can differ – the choice is yours, but it should be no more than 50 characters including spaces and punctuation
3. Insert a page number as well. The header is flush left, but the page number is flush right.
iii. Want an example header? Look at the title page of these instructions! You can use other titles depending on your own preferences (e.g. SOCIAL MEDIA AND CONSENSUS; CONFORMITY; JUDGING OTHERS; etc.).
b. Your Title should be midway up the page. Again, see my “Title” page above as an example of the placement, but for your title try to come up with a title that helps describe your study one. Avoid putting “Paper One”. Rather, consider the titles you saw in PsycInfo. Create a similar title that lets the reader know what your paper is about
c. Your name (First Last) and the name of your institution (FIU) beneath the title. For this class, only your own name will go on this paper. Double space everything!
i. You can also refer to Chapter 14 in your powerpoints and/or Smith and Davis textbook
d. This Title Page section will be on page 1
2. Abstract?
a. You DO NOT need an abstract for Paper I. In fact, you cannot write it until you run both study one and two (as the abstract highlights the results), so omit the abstract for now
3. Literature Review Section (12 points)
a. First page of your literature review (Page 2)
i. Proper header with page numbers. Your running head title will appear in the header of your page WITHOUT the phrase “Running head”. To insert this header, use the headers program.
ii. The title of your paper should be on the first line of page two, centered. It is IDENTICAL to the title on your title page. Just copy and paste it!
iii. The beginning text for your paper follows on the next line
b. Citations for the literature review
i. Your paper must cite a minimum of five (5) empirical research articles that are based on studies conducted in psychology. That is, each of the five citations you use should have a literature review, a methods section, a results section, a conclusion/discussion, and references.
1. For Paper I, you MUST use at least three of the five articles provided in the Canvas folder. You can use four if you like, but you must use three at minimum – however, you cannot use all five. For that fifth article, you must find it using PsycInfo. There are some other conditions for this fifth article that you must follow:
a. First, remember that the fifth article cannot be any of the five found in the Canvas folder.
b. Second, for your fifth article, it can be based on a wide variety of topics, including general priming studies, studies on consensus or conformity (without a social media angle), studies on social media (without a consensus or conformity angle), studies on impression formation, studies on friends, studies on informational social influence or morality etc. Trust me, there are TONS of topics that can help you in your paper. Just choose one that will help you support your experimental hypothesis for your Facebook Consensus study. That is, it has to help you justify your study one hypothesis (all students are using this same hypothesis, so make sure to read it. You can find it in the researcher instructions along with the questionnaires you are giving to participants. I actually suggest copying and pasting that hypothesis into this first paper at the end).
c. Finally, you can have more than five references if you want, but you must have a minimum of five references.
ii. Proper citations must be made in the paper – give credit where credit is due, and don’t make claims that cannot be validated.
iii. If you use a direct quote, make sure to provide a page number for where you found that quote in the citations. Do not directly quote too often, though.
You can have no more than three direct quotes in the whole paper
(though zero quotes would be even better). Instead, I would like you to paraphrase when possible.
c. Requirements for the information in your literature review
i. Your study one literature review should use prior research as a starting point, narrowing down the main theme of your specific project – think about the hourglass example from Chapter 14 in Smith and Davis.
ii. The last part of your literature review should narrow down your focus onto your own study, eventually ending in your study hypothesis. However, DO NOT go into specific details about your methods. You will talk about your specific methods in Paper II in a few weeks.
iii. Again, to make it clear, at the end of your paper you will give an overview of your research question, providing your specific predictions/hypotheses.
d.
The literature review must have minimum of two (2) full pages NOT INCLUDING THE HYPOTHESES (2.5 pages with the hypotheses). It has a maximum of five (5) pages
(thus, with the title page and references page, the paper should be between 4.5 and 7 pages). If it is only four and a half pages (again, including the hypotheses), it better be really, really good. I don’t think I could do this paper justice in fewer than five pages, so if yours isn’t at least five pages, I doubt it will get a good grade.
4. References (6 points)
a. The References section starts on its own page, with the word References centered. Use proper APA format in this section or you will lose points.
b. All five references that you cited in the literature review must be in this section (there should be more than five references here if you cited more than five articles, which is fine in this paper). However, at least three must come from the article folder on Canvas while the remaining two can come from either the last Canvas paper or two new ones from psychinfo. Only peer-reviewed articles are allowed here (no books, journals, websites, or other secondary resources are allowed for paper one).
c. For references, make sure you:
i. use alphabetical ordering (start with the last name of the first author)
ii. use the authors’ last names but only the initials of their first/middle name
iii. give the date in parentheses – e.g. (2007).
iv. italicize the name of the journal article
v. give the volume number, also in italics
vi. give the page numbers (not italicized) for articles
vii. provide the doi (digital object identifier) if present (not italicized)
5. Writing Quality (2 Points)
a. This includes proper grammar and spelling. I recommend getting feedback on your paper from the Pearson Writer program prior uploading it on Canvas.
6. Between the title page, literature review, and reference page, I expect a minimum of 4 pages and a maximum of 7 pages for this assignment. But like I said, the shorter the paper, the less likely it is to get a good grade, so aim for 5 pages minimum.
The above information is required for your paper, but I wanted to provide a few tips about writing your literature review as well. Students often struggle with the first paper, but hopefully this will give you some good directions:
· First, remember that you need 5 references, all of which MUST be peer-reviewed (three coming from the Canvas folder and one or two that you find on your own using PsycInfo).
· Second, I don’t expect a lengthy discussion for each and every article that you cite. You might spend a page talking about Article A and a sentence or two on Article B. The amount of time you spend describing an article you read should be proportional to how important it is in helping you defend your hypotheses. See if there is a prior study that looks a lot like yours (hint – there is at least one, which I based this study on, but you’ll have to find it on your own!). I would expect you to spend more time discussing that prior research since it is hugely relevant to your own study. If an article you read simply supports a global idea that ties into your study but has very different methods (like “frustrated people get mad!”), you can easily mention it in a sentence or two without delving into a lot of detail. Tell a good story in your literature review, but only go into detail about plot elements that have a direct bearing on your study!
· Third, this paper is all about supporting your hypotheses. Know what your hypotheses are before you write the paper, as it will help you determine how much time to spend on each article you are citing. My suggestion is to spend some time describing the nature of consensus and conformity, and then talking about studies that looked at this area. Use those studies to help defend your own study hypothesis. That is, “Since they found X in this prior study, that helps support the hypothesis in the present study”. Do you remember your hypotheses? Okay, I’ll be really helpful here. BELOW are your hypotheses. In your paper, support it! Just remember that the rest of your paper needs to be at least two full pages NOT INCLUDING the hypothesis below. In other words, including the hypotheses below, your actual text for your paper should be at least two and a half pages!
· Fourth, make sure to proofread, proofread, proofread! Use the Pearson Writer for help, but note that their suggestions are just that – suggestions. It is up to you to make sure the flow of the paper is easy to understand. Good luck!
· Fifth, go look at the supporting documents for this paper. There is a checklist, a grade rubric, and an example paper. All will give you more information about what we are specifically looking for as well as a visual example of how to put it all together. Good luck!
· Finally, note that you have a lot of help available to you. You can go to the Research Methods Help Center (which is staffed by research methods instructors and teaching assistants). You can go to the Writing Center in the Green Library (at MMC) and get help with writing quality. You can attend workshops from the Center for Academic success (CfAS) focusing on APA formatting, paraphrasing, and statistics. Your instructor might even be willing to give you extra credit for using these resources, so make sure to ask your instructor about it.
Combating Weight-Based
Cyberbullying
on Facebook with the Dissenter Effect
Jenn Anderson, PhD,
1
Mary Bresnahan, PhD,
2
and Catherine Musatics, MA
2
Abstract
Weight-based cyberbullying is prevalent among youth and adolescents and can have lasting negative psychological
effects on the victims
.
One way to combat these negative effects is through modeling dissenting behavior. When
a
bystander challenges the bully or supports the victim, this models dissenting behavior. In this study, 181 participants
were exposed to message manipulations posted on a Facebook page aimed at testing the conformity effect, the
dissenter effect, and the bystander effect in response to enactment of weight-based bullying. Facebook is a common
social media site where cyberbullying is reported. Results indicate that in the dissenting condition, participants’
comments were significantly more positive or supporting for the victim, as compared to other conditions. This effect
was more pronounced for men than for women. In addition, in the dissenting condition, men were less likely to consider
the victim unhealthy than women and men in other conditions. These results support the effectiveness of efforts to
model dissenting behavior in the face of bullies and extend them to online contexts. Implications are discussed.
Introduction
Cyberbullying is intense personal harassment de-riving from ‘‘comments, information or pictures posted
online for others to see with the intent to embarrass.’’1 Face-
to-face and online weight bullying are common forms of
bullying that are directed at the victim’s weight.
2–4
Weight-
based bullying is associated with negative outcomes, in-
cluding increased risk of depression, anxiety, poor body
image, social isolation, and maladaptive eating behaviors.
2
Weight-based bullying now commonly occurs online,
4,5
that
is, weight-based cyberbullying. This study creates a hypo-
thetical weight-based cyberbullying situation on Facebook to
test how social support for the victim, via dissenting com-
ments, may affect bystanders’ behaviors.
Cyberbullying
Cyberbullying affects children, adolescents, and adults.
6–9
Cyberbullying has negative effects on victims, such as lower-
ing self-esteem, increasing depression, and producing feelings
of powerlessness.6,10–13 Cyberbullying can be more devastat-
ing than traditional bullying for three reasons. First, cyber-
bullying is easier to engage in because of increased anonymity
and decreased internal censorship.
13–14
Second, cyberbullying
is more pervasive than traditional bullying,
13
partly because
perpetrators can use a broad range of platforms, including Web
sites, cell phones, e-mail, and instant messaging.
15
Third, on-
line comments are often permanently, and repeatedly, visible
by peers.
15,16
A particularly troubling form of cyberbullying is
weight-based cyberbullying.
Weight-based cyberbullying. Weight-based (cyber)bul-
lying is pervasive. Adolescents report that weight-based bul-
lying is the most common form of bullying experienced at
school,
3–4
and 53% of parents report that ‘‘being overweight’’
is the most common reason for youth bullying.
4
Across all
demographic categories, weight-based bullying increases in
intensity as the victim’s body mass index (BMI) increases.
2,3
Cyberbullying is the most common form of weight-based bul-
lying among overweight adolescents; 61% have received mean
or embarrassing posts online, and 59% have received mean
texts, e-mails, or instant messages.
4
Weight-based cyber-
bullying can be more serious than in-person weight-based
bullying because comments have high social visibility and
permanence in cyberspace.
10,13
Given the prevalence and
severity of weight-based cyberbullying, it is crucial to con-
sider methods to reduce or prevent this behavior, such as
encouraging bystanders of bullying to defend the victim.
17–20
Conformity and dissenter effects
Public situations with easily observed behavior encourage
social conformity.
21
Conformity occurs when an individual
experiences pressure to act normatively.
22,23
With cyber-
bullying, bystanders (observers of the bullying interaction)
1
Department of Communication Studies and Theatre, South Dakota State University, Brookings, South Dakota.
2
Communication Department, Michigan State University, East Lansing, Michigan.
CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL NETWORKING
Volume 17, Number 5, 2014
ª Mary Ann Liebert, Inc.
DOI: 10.1089/cyber.2013.0370
281
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may conform through joining the bully or providing tacit
support through silence.
14,24
Challenging a bully is a difficult
social behavior because it dissents from the social norm. The
bystander is faced with the dilemma of choosing between
normative behavior (which offers social approval) or dis-
senting behavior (which risks social disapproval).
22
Previous
research demonstrates that when dissenting behavior was
modeled, participants were less likely to conform to norma-
tive behavior and more likely to engage in dissenting behav-
ior.
21,25,26
For weight-based cyberbullying on Facebook, one
or more dissenting comments may produce a dissenter effect
whereby a bystander would challenge the bully or defend the
victim.
The creation of a ‘‘dissenting’’ condition mimics recent
bullying interventions efforts that equip bystanders to enact
social support to confront bullies.27,28 A recent meta-analysis28
showed that this method effectively increased bystander in-
terventions in face-to-face bulling situations. The current study
extends those efforts by examining the effects of a bystander
intervention online. Specifically, bystander intervention occurs
through dissenting comments responding to weight-based
bullying on a Facebook page. In this study, participants are
exposed to Facebook pages that model conformity (all nega-
tive comments), model dissent (some dissenting comments
among bullying comments), or provide no behavioral model
(i.e., no comments). Then, participants may provide their own
comments. The conformity manipulation in this study should
produce behavior similar to the control condition because
negative weight-based communication is normative in Amer-
ican society.29–33 However, comments in the dissent condition
should be more positive than in other conditions.
H1: Comments posted by participants in the dissenter
condition will be more positive than comments posted by
participants in the conformity or control conditions.
In addition to affecting participants’ comments toward the
victim, exposure to dissenting comments may also affect
participants’ empathy toward the victim, and their percep-
tions of the victims’ health and attractiveness. Thus, the
following research question is investigated:
RQ1: How will participant empathy, perception of victim
health, and perception of victim attractiveness differ by
condition?
The effects of gender on cyberbullying have been mixed.
While some research has observed that women held more
negative attitudes toward bullying than men,
28
other research
has shown that women are more prone to criticize over-
weight women for their size.
30–32
This prompts the second
research question.
RQ2: Will there be an interaction between gender and
condition that affects the dependent variables (com-
ment valence, participant empathy for the victim,
perception of victim health, and perception of victim
attractiveness)?
Materials and Method
Participants and procedures
All procedures and materials were approved by the insti-
tutional review board at a large Midwestern university, and
consent was obtained from each participant. A total of 190
students enrolled in three service classes were invited to
participate in this study; 181 students (97 men, 84 women)
entered the portal link (95% compliance), and all participated
in this study. Participants were 18–24 years old (M = 19.38
years, SD = 1.28). The majority of participants (66.3%)
identified as Caucasian, 13.5% as Asian, 12.4% as African
American, 3.4% as Hispanic, and 4.5% as other.
A series of Facebook accounts were created to simulate
online interactions. We created a main account and six ad-
ditional friend accounts. The main account belonged to a
person we named ‘‘Jessica.’’ Jessica is a pseudonym for
a mildly overweight, moderately attractive, Caucasian,
college-aged female. The stimuli for the study were created
by capturing a screenshot of a picture posted to Jessica’s
page, along with corresponding comments. Posing as Jessica,
we posted a picture of Jessica on her page, with the caption
‘‘dear roomies, thanks for the junk food celebration for
passing my exams, but next time let me know when you’re
taking a picture. kthx. &&&.’’ In the picture, Jessica is
sitting comfortably in an armchair, looking down and to her
left, and eating potato chips, cookies, and candy.
a
Participants were randomly assigned to conditions via an
online survey built through Survey Gizmo (conformity,
n = 71; dissenter, n = 65; control, n = 45). Each condition in-
cluded enough participants to allow for adequate statistical
power in analysis. The dropout rate for the survey was less
than 3%. There were no significant differences by condition
for age F(2, 162) = 0.61, p = 0.54; gender F(2, 178) = 0.26,
p = 0.77; or race F(2, 175) = 0.50, p = 0.61.
In the control condition, participants viewed a screenshot
that only showed Jessica’s picture and her own caption. In
the conformity condition, participants viewed a screenshot
that captured Jessica’s picture, her own caption, and com-
ments from six of her friends (three men, three women).
b
In
the dissenter condition, participants viewed a screenshot that
captured Jessica’s picture, her own caption, and comments
from six of her friends (three men, three women). In this
condition, two conformity comments were replaced with these
dissenting comments: (a) ‘‘Don’t listen to HATERS! You
look good!’’; (b) ‘‘You guys are SO MEAN! Leave her alone
about her weight!’’ After viewing the screenshot, participants
were prompted to post their own comment as though they
were posting on this Facebook page; 155 participants provided
comments. Participants then completed measures of study
variables. Unless otherwise indicated, all variables were
measured using 7-point Likert-type scales where higher
numbers indicate a greater presence of the variable. Psycho-
metric properties of scales are presented in Table 1.
Possible covariates
Participant body size. A single item measure asked
participants to describe their bodies (1 = ‘‘very thin,’’
7 = ‘‘obese’’; M = 3.71, SD = 1.13). In addition, participants
also reported their weight and height; we calculated their
BMI with the following formula: weight/height
2 · 703.34
The average BMI score was in the normal range (M = 23.29,
SD = 4.29), and the majority of participants (58.2%) were in
the normal weight category. In addition, 22% of participants
were in the overweight category, 9% were in the obese cat-
egory, and 7% were in the underweight category.
282 ANDERSON ET AL.
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Body appreciation. Participants completed a shortened
version of the Body Appreciation Scale.35 This 7-item scale
was unidimensional and reliable (a = 0.93). Sample items
included ‘‘On the whole, I am satisfied with my body’’ and
‘‘Despite its flaws, I accept my body for what it is.’’
Self-esteem. Participants completed the Short State Self-
esteem Scale.
36
This 5-item scale was unidimensional and
reliable (a = 0.82). Sample items included ‘‘I feel good about
myself’’ and ‘‘I feel that others respect and admire me.’’
Experience of weight-based teasing. Participants com-
pleted the weight-related teasing subscale of the Perception
of Teasing Scale (POTS).
37
The 6-item scale was unidimen-
sional and reliable (a = 0.87). Sample items included ‘‘People
sometimes make jokes about me being too heavy’’ and
‘‘People point at me and laugh because I am overweight.’’
Dependent variables
Comment valence. The valence (i.e., degree of negativity
or positivity) of each participant’s comment was evaluated by
two trained coders independent from the project, who were
blind to the conditions. Coders used a 5-point scale to evaluate
comment valence (1 = ‘‘negative,’’ 2 = ‘‘somewhat negative,’’
3 = ‘‘neutral,’’ 4 = ‘‘somewhat positive,’’ 5 = ‘‘positive’’). Ne-
gative comments made explicit negative remarks about Jessica
or her body, for example ‘‘are you happy with yourself?’’
Somewhat negative comments referred to the situation in a
negative way but did not personalize it to ‘‘Jessica,’’ for ex-
ample ‘‘Haha hate when I have pictures taken when I’m pig-
ging out!’’ Neutral comments were directed at other aspects of
the situation that did not have to do with Jessica, for example
‘‘Those chips look good!’’ The somewhat positive comments
made positive remarks about Jessica that were unrelated to her
body or that did not defend Jessica against bullying, for ex-
ample ‘‘Eat up! You deserve it!’’ Finally, positive comments
made explicit positive remarks about Jessica and/or encour-
aged Jessica in response to bullying, for example ‘‘I don’t
know what everyone is talking about. You’re wonderful, don’t
let them influence you.’’ Coders reached acceptable intercoder
reliability (Cohen’s j = 0.89).
Empathy for the victim. The authors developed a 6-item
scale that measured the extent to which participants in the
conformity and dissenter conditions felt empathy toward the
victim. Sample items included ‘‘I was touched by Jessica’s
situation of getting negative comments about her size’’ and
‘‘I felt bad for Jessica when I read these comments.’’ The
scale was unidimensional and reliable (a = 0.87).
Perception of victim’s health. The authors developed a 6-
item scale that measured the extent to which participants
considered the victim unhealthy as a result of her eating
behaviors (as suggested by the photo). Higher scores indi-
cated that participants perceived Jessica as unhealthy due to
her eating behaviors. Sample items included ‘‘If Jessica
keeps on eating like that, she is going to have a heart at-
tack’’ and ‘‘Jessica is killing herself by stuffing food into
her mouth.’’ The scale was unidimensional and reliable
(a = 0.84).
Perception of victim’s attractiveness. The authors de-
veloped a 6-item scale that measured the extent to which
participants considered the victim physically unattractive
due to her weight. Higher scores indicated that participants
perceived Jessica as unattractive because of her weight.
Sample items included ‘‘Jessica’s large size makes her to-
tally unattractive’’ and ‘‘Jessica is really hefty; people
probably don’t think she is attractive.’’ The scale was uni-
dimensional and reliable (a = 0.88).
Results
The overall distribution of comments (N = 155) included
26 (16.8%) negative comments, 22 (14.2%) somewhat neg-
ative comments, 41 (26.5%) neutral comments, 46 (29.7%)
somewhat positive comments, and 20 (12.9%) positive
comments. Twenty-six participants did not provide a com-
ment (3 control, 8 dissent, 15 bullying). See Table 2 for the
distribution of response type by condition. Respondents who
did not post any comments thought that Jessica was healthier,
Table 1. Psychometric Properties of Study Variables
Mean (SD)
Scale Men Women Overall Alpha
Body appreciation 5.42 (1.18) 4.84 (1.32) 5.15 (1.18) 0.93
Self-esteem 5.19 (1.09) 4.66 (1.12) 4.94 (1.13) 0.82
Teasing experience 1.57 (.60) 1.48 (.57) 1.53 (.49) 0.87
Empathy 4.68 (1.03) 5.42 (.92) 5.03 (1.04) 0.87
Perceived lack of health 4.98 (1.00) 4.99 (1.00) 4.99 (.99) 0.84
Perceived unattractiveness 4.22 (1.17) 3.59 (1.33) 3.93 (1.28) 0.88
Comment valence 3.15 (1.25) 2.99 (1.31) 3.08 (1.28) —
Note. Teasing experience and comment valence were measured with 5-point scales. All other mean scores are based on 7-point scales.
Table 2. Distribution of
Comment Valence
by Condition
Comment Valence
Condition Negative Neutral Positive No Comment
Control 15 15 12 8
Dissent 12 16 29 8
Conformity 22 17 17 15
Overall 49 48 58 26
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t(95) = 2.41, p < 0.05, and more attractive, t(95) = 3.31, p < 0.001, and they showed greater empathy for Jessica t(95) = -2.70, p < 0.01, compared to respondents who posted negative comments.
Hypothesis 1 predicted that comments would be most
positive in the dissenting condition. A one-way analysis of
variance (ANOVA) was used to test this hypothesis.
c
This
hypothesis was supported. Comments in the dissenting
condition (M = 3.42, SD = 0.1.21) were more positive than
those in the conformity (M = 2.80, SD = 1.33) or control
(M = 2.98, SD = 1.22) conditions, F(2, 152) = 3.61, p = 0.03,
partial g2 = 0.05.
RQ1 asked whether participants’ empathy toward the
victim and perceptions of victim health and attractiveness
would differ based on condition. One-way ANOVAs were
used to answer this research question. There were no sig-
nificant differences by condition for empathy toward the
victim, F(1, 136) = 0.43, p = 0.51, partial g2 = 0.003; per-
ceptions of victim health, F(2, 178) = 2.77, p = .07, partial
g2 = 0.03; or perceptions of victim appearance, F(2,
178) = 1.62, p = 0.20, partial g2 = 0.02.
RQ2 asked whether there would be a condition · gender
interaction that affected the dependent variables. A series of
3 · 2 (condition: control, conformity, dissent · gender: male,
female) ANOVAs were used to answer RQ2. For comment
valence, comments were most positive among men in the
dissent condition, F(2, 132) = 3.98, p = 0.02, partial g2 = 0.05.
Figure 1 displays this finding. For perceived victim health,
compared to all other conditions, men in the control condi-
tion found the victim the most unhealthy, whereas men in the
dissent condition found the victim the least unhealthy, F(2,
132) = 3.48, p = 0.03, partial g2 = 0.04. And compared to
women in the other conditions, women in the conformity
condition found the victim to be significantly unhealthier.
Figure 2 displays this finding.
Discussion and Implications
The results of this study indicate that modeling dissenting
behavior in weight-based cyberbullying situations can en-
courage bystanders to provide verbal support to the victim.
This finding confirms previous research on the effectiveness
of modeling dissenting behavior, which is a form of social
support, when faced with a bully
4,
21,27,28,30
and extends it to
an online context. In addition, it confirms that without a
model for positive communication about bodies, people are
far less likely to formulate such messages.
33
The support for
the dissenter effect found in this study can serve as a model
for cyberbullying prevention efforts.39 Future interventions
should develop dissenting communication models for by-
standers to emulate when faced with bullying situations.
The effectiveness of modeling dissenting behavior was
particularly pronounced for men. Specifically, men in the
dissent condition provided the most positive comments and
found the victim the least unhealthy, compared to all other
conditions. This finding is significant because, without any
behavioral modeling (in the control condition), men provided
the most negative comments and found the victim the most
unhealthy. Gender differences in weight-based bullying may
account for this effect. Male perpetrators of weight-based
bullying typically use teasing or direct verbal bullying,
whereas women typically use relational forms of victimiza-
tion like social isolation.2 In this study, direct verbal bullying
was targeted. Future interventions that include models of
dissent for verbal and relational forms of bullying may be
more effective for both genders.
Women in the control condition rated the victim as less
unhealthy than did men; this appears inconsistent with pre-
vious research that suggests women are highly critical of other
women’s bodies.
30–32
However, women’s ratings of the vic-
tim’s unhealthiness (M = 4.99) in that condition were signifi-
cantly above the midpoint of that scale (3.5), t(83) = 13.71,
p < 0.001, and women’s evaluations of the victim’s health
remained negative across conditions—even though men’s
perceptions changed when exposed to peer comments. This
stability of women’s negative evaluations of overweight wom-
en’s health is consistent with previous research.30–32 Future
research should continue to examine such gender differences
and explore the patterns when the victim is male.
Importantly, 14% of participants (n = 26) provided no
comments on the Facebook page; they acted as passive by-
standers to online weight bullying. Across conditions, silent
bystanders had greater empathy for the victim and perceived
the victim to be less unhealthy and more attractive than those
who posted negative comments. In addition, the bystander
FIG. 1. Effect of gender · condition interaction on com-
ment valence.
FIG. 2. Effect of gender · condition interaction on per-
ception of unhealthiness.
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effect was more common in the bullying condition than in
other conditions. This suggests that some passive bystanders
appeared not to agree with the bullying. However, they did
nothing to intervene or give support to the victim. This kind
of response is consistent with claims about diffusion of re-
sponsibility related to the bystander effect.
24
Future studies
should more fully probe what is experienced by passive
bystanders to weight-based cyberbullying.
Limitations of this study must also be considered. First,
this study’s dissenting behavior only targets one type of
bullying: negative verbal comments directed at the victim’s
weight. This limits the generalizability of these findings to
interventions that target direct verbal teasing. Second, the
victim in this study is a Caucasian female. A male victim
may have elicited different comments from participants, and
future studies should examine this potential gender effect.
Finally, this study was limited by the unrealistic nature of the
Facebook page (i.e., a static screenshot, rather than interac-
tive page). Participants were aware that their comments
would not be seen by anyone and that they would not be
associated with the victim. This may have altered their be-
havior in unknown ways, reducing the external validity of
these results.
In conclusion, this study found support for the dissenter
effect in combatting weight-based cyberbullying. This effect
was particularly pronounced for male participants who pro-
vided the most positive comments after exposure to a dis-
senting model. This study provides additional support for
bullying interventions that model dissenting behavior
21,27,28,30
and establishes the usefulness of this method in the case of
weight-based cyberbullying.
Notes
a. To select this image, the researchers gathered a range of
photos from Google images, all depicting large women eat-
ing cake and junk food. Previous research eliciting partici-
pant reactions to images of overweight individuals suggested
that extreme examples of large body size could potentially be
biasing.
33
From several images of large women, two repre-
sentative images of a moderately obese attractive woman
controlling for race were selected. These two images were
pretested with 60 participants (males and females) to ascer-
tain that both images were seen as somewhat overweight and
more or less attractive to some participants. Both yielded
acceptable results, and the better of the two images was se-
lected to represent Jessica.
b. (a) Get off ur ass and onto the Biggest Loser; (b) OMG,
ur so unhealthy; (c) This pic makes me sad; (d) Srsly girl, ur
such a fat ass tub of lard; (e) Constant eating = obesity;
evidence—this picture; (f ) Go ahead & stuff your face with
junk!
c. Prior to conducting hypothesis tests, we examined the
data according to previously established guidelines to de-
termine whether any of the potential covariates should be
included in the analyses.
38
No covariates were significantly
related to any of the outcome variables, thus none were in-
cluded in hypothesis tests.
Author Disclosure Statement
No competing financial interests exist.
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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.
T
http://www.sciencedirect.com/science/journal/07475632
https://www.elsevier.com/locate/comphumbeh
https://doi.org/10.1016/j.chb.2018.11.017
https://doi.org/10.1016/j.chb.2018.11.017
mailto:serena.perfumi@sociology.su.se
https://doi.org/10.1016/j.chb.2018.11.017
http://crossmark.crossref.org/dialog/?doi=10.1016/j.chb.2018.11.017&domain=pdf
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
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
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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:
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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.
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.
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.
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).
We thank Phil Costanzo for his helpful feedback.
No potential conflict of interest was reported by the authors.
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- Abstract
- Study 2: rational arguments elicit more conformity than emotional arguments
Introduction
Study 1: impersonal statistics influence moral judgments
Method
Participants
Materials
Procedure
Results
Discussion
Method
Participants
Procedure
Results
Discussion
General discussion
Acknowledgments
Disclosure statement
References
Journal of Applied Psychology
1972. Vol. 56, No. 1,
54
-59
INFORMATIONAL SOCIAL INFLUENCE AND
PRODUCT EVALUATION 1
JOEL B. COHEN2 AND ELLEN GOLDEN
University of Illinois
Two groups of 5s were exposed to 16 scaled product evaluations (supposedly from
peers). High uniformity and low uniformity conditions, respectively, were deter-
mined by degree of dispersion. Both expected their evaluations to be visible to
others. Two remaining groups were given no information regarding others’ evalua-
tions. One group, however, expected their evaluations to be visible to others. The
5s’ subsequent evaluations were significantly influenced by others’ ratings, the
greatest influence occurring under the high uniformity-visibility condition. There
was, however, no significant difference due to 5s’ expectations that their ratings
would be visible to others. Individual differences in interpersonal response orienta-
tions were not significantly related to the acceptance of information from others,
although the direction of results was in accord with predictions.
For many, the application of social influence
research is limited to rather specialized settings
(e.g., formal group interaction or structured
authority relationships) or tied to the notion
of conformity or conformity proneness. This
view tends to understate the pervasiveness of
social influence and its importance to human
behavior. Informational social influence, espe-
cially, has not received its due consideration
in many settings and under many circum-
stances in which it is likely to be a significant
factor in decision making and overt behavior.
Product evaluation may prove to be an
especially fertile setting within which informa-
tional social influence is likely to operate.
Products are typically evaluated relative to a
number of competing needs and demands on
individual and family resources. Resulting
questions of value judgments, which are them-
selves not completely reducible to objective
evidence and matters of fact, are without doubt
subject to social frames of reference. Appro-
priate or correct behavior is such, in large
part, because of the evidence we have that
others agree with or accept the behavior.
Aside from questions of value, the very com-
plexity of product evaluation itself (e.g., the
number of brands and models, the claims and
counterclaims, and the difficulty of obtaining
1 Appreciation is expressed to Raymond Suh for his
help on the study.
2 Requests for reprints should be sent to Joel B.
Cohen, Department of Business Administration, Uni-
versity of Illinois at Urbana-Champaign, Urbana,
Illinois 61801.
objective evidence) and the time it would take
to resolve the many uncertainties combine to
favor the utilization of information from others.
The study to be reported focuses specifically
on three potential sources of influence on a
consumer’s judgment in social situations: (a)
the uniformity of relevant information pro-
vided by others, (6) the extent to which one’s
judgment (evaluation) is known to others, and
(c) one’s interpersonal response orientations.
Many of the early conformity studies failed
to distinguish clearly between two processes
of social influence whose differences are of
considerable importance (Asch, 1958; Crutch-
field, 1955; Sherif, 1958). The first, normative
social influence, refers to influence to conform
with certain expectations held by others. The
second, informational social influence, refers
to influence to accept information provided by
others which is taken as evidence about
reality.3 The former might be termed conform-
ity in the sense that one accepts influence
either to establish or enhance a favora
ble
reward-punishment relationship with certain
individuals or because of a desire to identify
with such individuals or their points of view
(Kelman, 1961). The second, however, is not
true conformity in the sense that a lack of
3 We are relying most strongly here on the distinction
made by Deutsch and Gerard (19SS), although a
number of researchers have proposed fairly similar ap-
proaches. See Jones and Gerard (1967) for a partic-
ularly insightful discussion of these as social compari-
son processes, especially in the context of “information
dependence” and “effect dependence.”
54
SOCIAL INFLUENCE AND PRODUCT EVALUATION
55
information, an ambiguous situation, or prema-
ture demands for action or decision lead the
person to substitute seemingly competent in-
formation from others for his own search for
direct evidence. Indeed direct, physical, and
objective evidence regarding the truth of many
of our beliefs (and especially values) is simply
not easily obtainable. For many of these our
primary point of reference may be other indi-
viduals or groups, and our reality, therefore,
is socially as well as physically determined.
Under either informational or normative
conditions, the uniformity of information pro-
vided by others regarding the relative quality
of a product should have a direct bearing on
consumers’ evaluations. This should be espe-
cially true when (a) quality is somewhat am-
biguous because of a lack of clear standards,
and (b) one’s own ability to discriminate is
not thought satisfactory. Venkatesan (1966)
demonstrates that social influence is operative
in this type of product evaluation situation.
We prefer to characterize the process he studied
not as “conformity to group pressure” (as he
has done) but, rather, as “informational social
influence.”
Stafford (1966) provides an interesting pic-
ture of informal group influence on brand
preferences within sociometrically determined
“natural” groups. Here the setting is conducive
to both influence processes, although the rela-
tive strength of normative influence would
almost certainly be greater for an object or
issue of greater relevance to the group (around
which norms could develop) than bread.
In order to more adequately study conditions
underlying the acceptance of social influence,
it is necessary to go beyond a one-way flow of
information and influence (from the group to
the individual). Such a conceptualization is too
narrow and does not consider others’ subse-
quent reactions to the behavior of the individ-
ual, especially the extent of his acceptance or
rejection of group influence. It seems espe-
cially important to separate out the effects of
factors which influence public acceptance of
information from those which influence adher-
ence to such information. Adherence should
follow directly from uniformity, for example,
under conditipns supportive of informational
social influence. If an individual has merely
expressed public acceptance (under conditions
favoring normative influence), his perception
that others are able to maintain surveilance
and impose sanctions may be necessary condi-
tions for adherence. In the classic conformity
studies, either 5s’ evaluations or behaviors
were perceived to be visible to others. In this
study we will specifically examine the impor-
tance of this factor under informational social
influence conditions.
Interpersonal response orientations refer to
people’s predominant modes of response to
others. They can be thought of as interpersonal
aspects of personality. Using Horney’s (1945)
tripartite classification of moving toward,
against, or away from others, Cohen (1967)
developed the Compliant, Aggressive, De-
tached (CAD) scale to measure the extent of a
person’s corresponding compliant, aggressive,
and detached interpersonal orientations. As
predicted, compliant people were more sus-
ceptible to information regarding group judg-
ments than were aggressive people, although
(at least in the absence of group pressure and
overt influence attempts) no significant differ-
ences in detached orientations among high- and
low-opinion changers were observed (Campbell,
1966).
Most people seem to have a reasonable
balance among the orientations so that al-
though one is usually preferred (more con-
sistent with other values or more often rein-
forced in social interaction) the person remains
flexible to the demands of the situation. Even
a highly aggressive person may refrain from
aggressive behavior under certain physical or
moral constraints. To the extent that more
specific situational influences (e.g., substantive
issues, objects, other people’s identity, task
requirements, etc.) encourage the expression
of individual differences, we should find some
correspondence between behavior and pre-
ferred modes of relating to others. Accordingly,
interpersonal response orientations were an
additional factor incorporated into the design
of the study.
METHOD
Each of three groups of 48 introductory marketing
students at the University of Illinois was randomly
assigned to four treatment conditions to form three
blocks of 12 Ss within each. Treatments are summarized
in Table 1. Each of the three groups was made up
entirely of individuals scoring at least one standard
56 JOEL B. COHEN AND ELLEN GOLDEN
TABLE 1
MEAN VALUES UNDER EACH TREATMENT CONDITION
Treatment
Uniformity in
others’ evaluations
High uniformity
Low uniformity
No information
No information
Visibility of
5s’ Behavior
Visible
Visible
Visible
Not
visible
X product
evaluation
10.75
9.83
9.17
8.50
deviation above the sample mean on one of the traits
measured by the CAD scale, a set of 35 items each
calling for a response relative to the desirability of
engaging in particularly characteristic types of inter-
personal behavior (Cohen, 1967).
Students were given to believe that a marketing
research project was being conducted to predict the
likely success of a new coffee product recently intro-
duced in the area. Under both the high uniformity-
visible and low uniformity-visible conditions, 5s were
individually shown a rating board containing other
5s’ evaluations of the coffee they were instructed to
taste and evaluate. The rating board was a large and
attractive piece of heavy cardboard subdivided into
five general categories for evaluation (from “worst
I’ve ever tasted” to “best I’ve ever tasted”), each, in
turn, broken down into three degrees of favorability.
(There were 15 response categories in all.) Under each
category were a set of small nails. Name tags were hung
on a predetermined number; the effect, in total, looked
very much like a frequency distribution histogram.
Name tags (many similar to, but none identical with,
other 5s’ names) were written in a large number of hand-
writing styles and with different pens and colors of ink.
Each 5 in these two treatments saw 16 name tags
representing others’ prior evaluations of the coffee.
In both treatments, the modal “evaluation” (preset
by £) was 12 (compared to the control group’s mean
evaluation of 8.5). We wished to produce a reasonable
discrepancy for those whose own estimates were at
the mean or several rating points above it, yet without
danger of a ceiling effect.
In the high-uniformity condition, nine of the name
tags were placed on the modal rating with the remaining
seven concentrated as follows: one on 10, two on 11,
and four on 13. In the low-uniformity condition, 5
name tags were placed on the modal position with the
others as follows: one on 5, one on 7, two on 8, one on
9, one on 10, two on 11, and three on 13. Thus, each 5
in the high-uniformity condition was exposed to the
same information (without risk of bias by confederates’
actions) with a substantially greater consensus than
5s in the low-uniformity condition. Any number of
variations in the dispersion of others’ evaluations
(including the identification of certain 5s) could be
used to easily vary and standardize the information
provided under possible treatments. Only the two
variations discussed above, however, were incorporated
into this study.
After tasting the coffee, each 5 in these two conditions
wrote his name on a tag and placed it on the board.
Since the name tag would always be placed last in any
column chosen, 5s could not reasonably expect their
evaluations to be hidden from others, no matter where
it was placed. After each 5 left the room, E removed
his name tag from the board.
The third treatment, the no information-visible con-
dition, was used to separate out the effects of informa-
tion presumably provided by others from the expecta-
tion that others will know how one has evaluated the
product. As such, this provides a control group for the
factor “uniformity of information,” as well as a direct
comparison with the no information-no visibility control
group (Treatment 4).4 The 5s in Treatment 3 were
given to believe that theirs was the first name tag to
be placed on the chart for a “new group of tasters.”
The E explained simply that the procedure was to let
the board get fairly well filled, copy a summary of the
evaluations, take the tags off, and start all over again.
This procedure was used for each of the 36 5s in this
condition.
The fourth treatment used a rating form identical
in scale to the rating board. Evaluations of the coffee
were obtained in the absence of information from others.
The rating form was simply taken from 5s and placed
in a stack.
In total, the methodology was designed to create a
setting in which a small to moderate amount of un-
certainty regarding a correct product evaluation could
be tied to variations in informational input from others.
No attempt was made to build in factors which would
tend to produce normative influences. In such a setting
it was hypothesized that informational social influence
would be accepted for its own sake and not for reasons
of conformity.
RESULTS
A 4 X 3 factorial analysis of variance (Treat-
ments X Interpersonal orientations) was run.
Differences in treatment effects were signifi-
cant and in the predicted direction (see Table
2).
Analysis of the significant treatment effect
by orthogonal trend components revealed that
99,01% of the variation in evaluation by treat-
ments (SS treatments = 99.69) may be pre-
dicted from a linear regression equation
(Winer, 1962). This tends to indicate (a) that
the acceptance of social influence was a linear
4 A before-after design with an initial private rating
and one after seeing others’ ratings would have per-
mitted equivalent comparisons. This present design
was chosen (a) to avoid sensitizing 5s to the fact that
the information is “supposed to” make a difference in
your evaluation and (b) to prevent postcommitment
dissonance from influencing the results.
SOCIAL INFLUENCE AND PRODUCT EVALUATION 57
function of the degree of uniformity or con-
sensus in the information presented, and (6)
that no complex interaction between uniform-
ity and visibility was present. Further analysis
of these interrelationships was conducted
using an orthogonal decomposition of the treat-
ment sum of squares and comparisons among
treatment sums (Winer, 1962). Table 3 sum-
marizes the four comparisons used to separate
out the effects of uniformity and visibility.
Comparison 1 in Table 3, for example, looks at
the following weighted linear comparison of
treatment sums: [(7\ + T2 + F3)/3] – T4.
Approximately 55% of the variation among
treatments (54.19/99.69) is due to the differ-
ence between the control group (no informa-
tion-no visibility) and the other treatments
combined.
To what extent is this difference due to the
information seemingly provided by other 5s
or to the known visibility of one’s own evalua-
tion? If the latter, then the informational social
influence hypothesis (i.e., influence is accepted
largely because it reduces uncertainty) cannot
be supported since 5s would appear to be more
concerned with anticipating others’ positive
or negative reactions. F ratios on compari-
son sums of squares (e.g., SSci/MS error)
permitted more definitive answers to these
questions.
Comparison 2 (see Table 3) reveals a signi-
ficant difference (and in the predicted direc-
tion) between the two groups provided with
information regarding others’ evaluations and
the group not given such information, all
three groups believing their evaluations to be
visible to others. Comparison 4, on the other
hand, indicates that visibility, per se, is not a
significant source of variation when informa-
tion is held constant. Approximately 30% of
the variation among treatments is due to
TABLE 2
ANALYSIS or VARIANCE
TABLE 3
COMPARISONS ON TREATMENT SU
MS
Source of variation
Treatments
Interpersonal orientations
Interaction
Error
df
3
2
6
132
MS
33.23
6.27
2.55
7.15
F
4.65*
.88
.36
parison
2
Ci
c,
Ca
Ci
High
uni-
formity
-visi-
ble
387
1
1
1
0
Low
uni-
formity
-visi-
ble
354
1
1
-1
0
No in-
forma-
tion
-visi-
ble
330
1
-2
0
1
No in-
forma-
tion
-not
visible
306
-3
0
0
-1
55
54.19
30.38
15.13
8.00
F
7.58**
4.25*
2.12
1.12
*p < .005.
Note.—C = comparison.
* p < .05.
** p < .01.
Comparison 2, while only 8% is due to Com-
parison 4. We must conclude that visibility is
not a significant feature of this social influence
situation in which informational social in-
fluence appears to predominate over normative
social influence.
Comparison 3 indicates that acceptance of
social influence is not significantly greater
under high uniformity than under low uni-
formity, although results are in the predicted
direction (see Table 1).
Interpersonal response orientations did not
prove to be a significant source of variation,
although the direction of results fits the under-
lying model. Compliant 5s were the most
favorable in their product evaluations
(X = 9.96). Aggressive 5s were least favorable
(X = 9.25_), while detached 5s were inter-
mediate (X = 9.48).
DISCUSSION
These results provide strong confirmation
that social influence is operative in situations
not characterized by strong normative pres-
sures (cohesive groups, relevant issues, estab-
lished norms, sanctions, etc.). Buying decisions,
even when the product or brand being judged
is not novel or unfamiliar, seem to be char-
acterized by uncertainty. This may stem, in
part, from a lack of objective standards and a
lack of reliable comparative brand information.
Such conditions should tend to produce a
heightened readiness to respond to apparently
competent information from others.
The absence of a more pronounced differ-
ence between high- and low-uniformity treat-
ment groups is somewhat surprising. Our
manipulation of uniformity was tied to a range
58 JOEL B. COHEN AND ELLEN GOLDEN
of 5s’ coffee evaluations, however, rather than
markedly contrasting conditions of unanimous
agreement among others versus sharp disagree-
ment. Uniformity, in this study, is a somewhat
more involved notion than in most similar
studies. In many previous studies, information
from others was uniform if it was absolutely
identical (i.e., each confederate gave the exact
same answer or caused the exact same light to
go on). Here, the focus is on product evalua-
tion which can only be forced into a similar
conception of uniformity either by collapsing
the evaluation task into two or three cate-
gories (so as to make perfect consensus believa-
ble) or by telling S you are providing him with
consensus data (e.g., group means).
In reality, of course, it is seldom that no
variation exists in the advice and opinions
others so thoughtfully supply. We do not move
instantly from uncertainty to certainty by
virtue of the information received. There is
doubt and disagreement, and it may be of
some value for researchers to more realistically
deal with variance in information, specifically
in so far as learning how consumers respond to
it. It may be that consumers (or at least our
5s) tend to rely on specific information aggrega-
tion schemes such as a modal evaluation or
some other simplifying rule of thumb in dealing
with the results of diversity in product ratings.
Since the mode was the same in both the high-
uniformity and low-uniformity conditions (12
in both cases), we might possibly have pro-
vided much less of a difference in the two
uniformity conditions than was desirable for
maximal effect upon evaluations.
The failure of interpersonal response traits
to be a more discriminating predictor variable
may, to a large extent, be an artifact of the
methodology employed. We note with interest
that compliant 5s gave evaluations closest to
the mode, and, hence, more similar to their
peers. Aggressive 5s were furthest from the
mode, thus consistent with a movement against
the typical response. Detached 5s were inter-
mediate, neither responding strongly pro norm
nor counter norm. It may be recalled that the
methodology minimized social interaction and
direct influence attempts, two of the factors in
social influence situations which one would
expect to be most strongly related to this type
of treatment of individual differences.
CONCLUSION
The 5s asked to evaluate an unknown brand
of coffee were significantly influenced by rating
distributions (other 5s evaluations) of both
relatively high and low concentration (uniform-
ity). There was some tendency for acceptance
of the modal evaluation to be greater under
conditions of higher uniformity. The difference
between high- and low-uniformity conditions
was, however, not significant. This may have
been due to the uniformity manipulations
which dealt more with degree of dispersion
than more absolute dichotomies. Perceived
visibility of 5s’ subsequent ratings was not a
significant factor leading to the acceptance of
information from others. Differences in 5s’
interpersonal orientations did not prove to
be a significant factor, although results were
in the predicted direction.
Our data suggest that even for a familiar
product whose taste was the sole criterion for
evaluation, individual judgments may be
modifiable by the perceived evaluations of
others. No attempt was made to convey infor-
mation of a more expert nature or in any way
encourage 5s to feel the information was
somehow reliable or accurate. Thus, even under
minimal conditions for social influence, such
information had a significant effect on product
evaluation. These results are interpreted as
supporting the pervasiveness and significance
of informational social influence even when
conditions favoring normative compliance are
largely absent.
REFERENCES
ASCH, S. E. Effects of group pressure upon the modifica-
tion and distortion of judgments. In E. E. Maccoby,
T. M. Newcomb, & E. L. Hartley (Eds.), Readings
in social psychology. (3rd ed.) New York: Holt,
Rinehart & Winston, 1958.
CAMPBELL, R. The utilization of expert information in
business forecasting. Unpublished doctoral disserta-
tion, Graduate School of Business Administration,
University of California at Los Angeles, 1966.
COHEN, J. B. An interpersonal orientation to the study
of consumer behavior. Journal of Marketing Research,
1967, 4, 270-278.
CRUTCHMELD, R. S. Conformity and character.
American Psychologist, 1955, 10, 191-198.
DETJTSCH, M., & GERARD, H. B. A study of normative
and informational social influence upon individual
judgment. Journal of Abnormal and Social Psy-
chology, 1955, 51, 629-636.
SOCIAL INFLUENCE AND PRODUCT EVALUATION 59
HORNEY, K. Our inner conflicts. New York: W. W.
Norton, 1945.
JONES, E. E., & GERARD, H. B. Foundations of social
psychology. New York: Wiley, 1967.
KELMAN, H. C. Processes of opinion change. Public
Opinion Quarterly, 1961, 25, 57-78.
SHERIF, M. Group influences upon the formation of
norms and attitudes. In E. E. Maccoby, T. M.
Newcomb, & E. L. Hartley (Eds.), Readings in
social psychology. (3rd ed.) New York: Holt, Rine-
hart and Winston, 1958.
STAFFORD, J. E. Effects of group influence on consumer
brand preferences. Journal of Marketing Research,
1966, 3, 68-75.
VENKATESAN, M. Consumer behavior: Conformity and
independence. Journal of Marketing Research, 1966,
3, 384r-387.
WINER, B. J. Statistical principles in experimental design,
New York: McGraw-Hill, 1962.
(Received November 13, 1970)
1
Running head: COUNTERFACTUAL THINKING
[Type text] [Type text] [Type text]
2
COUNTERFACTUAL THINKING
Comment by Ryan Winter: Do you know how to enter a header? Click on the “Insert” menu at the top of word, click on “Header”, and then type in the header whatever you want. There is even a box that you can check that allows you to have a different header on the first page than subsequent pages.
Counterfactual Thinking: Appointing Blame Comment by Ryan Winter: Note the title here as well. It is descriptive of the paper to come, and falls within the 12 words recommended by the APA. The first letter of all words over 4 letters is capitalized, as is the first word after the colon if it less than 4 letters. “Counterfactual Thinking: An Analysis of the Phenomenon” would thus be correct as well
Former Student Comment by Ryan Winter: Your name goes here
Florida International University Comment by Ryan Winter: Your university affiliation goes here
Counterfactual Thinking: Appointing Blame Comment by Ryan Winter: Note APA formatting for this page. Above, you have the header (the same header as on the title page in the same location, except now you omit the words Running head)
The page number is also present. Normally this literature review would start on page three with the abstract on page two, but for this assignment there is no abstract, so page 2 is fine!
Finally, your paper title goes at the top of the page, and it is identical to the title you used on the title page.
As free-willed beings, we can often become the victims of our own decisions. Imagine accidentally running over a stray cat because you decided to look away from the road at the exact moment the kitten decided to cross the street. Following the accident, most people would be plagued with thoughts of how alternative circumstances or decisions could have prevented such an unfortunate situation. Every time an individual forms a ‘what if’ scenario in which he or she mentally alters the course of events occurred, they are participating in a process that is known as counterfactual thinking (Ruiselová, Prokopčáková, & Kresánek, 2007; Williams, Lees-Haley, & Price 1996). This process allows individuals to consider the multiple factors at play in a situation (i.e mutability), and to decide what specific condition was responsible for the ultimate outcome of the event. The primary focus of our study is to analyze the extent of culpability people place on a particular factor depending on the preventability of the outcome. That is, if it is easy to “undue” an event that ends in a tragic outcome, will participants find an actor who fails to engage in that easy behavior more at fault? Comment by Ryan Winter: This is a good introduction that talks about the focus of this paper while starting off a good story. Watch how it flows from one paragraph to the next as it build the story, focusing on researchers and studies that came before and using those ideas as the author nears his own predictions Comment by Ryan Winter: I want you to notice something important here. The student author cited two different papers in the same sentence. This is fine! Both citations talk about “what if” scenarios, so both can support the statement written by the student author. Further, both citations are only mentioned once. The student author did not spend a paragraph or page on the citation. Feel free to do the same. Spend only as much time as you need. If it is a sentence, that’s fine. If more, that’s fine too!
The development of counterfactual thoughts relies on the variability of the situation, as well as the knowledge that different actions could have resulted in alternate outcomes (Alquist, Ainsworth, Baumeister, Daly, & Stillman, 2015). According to Alquist et al., situations that are believed to be highly changeable generate more counterfactual thoughts than events that seem unavoidable. However, ruminating on every conceivable alternative of a situation would take an unlimited amount of time and resources. Instead of allotting so much time and energy on a cognitive task, people tend to narrow down the different scenarios that come to mind according to the degree of controllability of the factors involved (McCloy & Byrne, 2000). For example, the deliberate decisions individuals make that ultimately lead to a certain outcome is considered to be a controllable event, whereas uncontrollable events are unavoidable circumstances, such as traffic jams or natural disasters (McCloy & Byrne, 2000). When mentally forming a scenario different than the one occurred, individuals tend to change controllable rather than uncontrollable events (2000). Therefore, events that are within an individual’s jurisdiction generally receive the brunt of the blame for the resulting situation. Comment by Ryan Winter: Using et al. is fine here, since the author provided all author names the first time. In general, provide all author names EVERY time if there are three or fewer authors. If four or more, give them all the first time and then use et al. thereafter after the first authors last name. If there are more than six authors, you can use et al the first time, but having more than six authors is pretty rare.
In a similar light, a study performed by McCloy and Byrne (2000), discovered that inappropriate events are more often changed through the process of counterfactual thinking than appropriate ones, especially when the outcome of these events was negative. Inappropriate events include the decisions individuals make that are considered to be ‘socially wrong’, whereas appropriate events are ‘socially acceptable’ actions. Due to these results, we can conclude that what McCloy and Byrne consider to be “inappropriate controllable” events, will likely be regarded as highly culpable factors in the outcome of a situation. Comment by Ryan Winter: This citation is a good example of APA formatting, When that word “and” falls outside of the parentheses, use the word “and” and not an ampersand (&) Comment by Ryan Winter: You will notice here that he doesn’t have the dates for these authors. This is okay, since he already gave the date in this paragraph, and the author names the second time around continue from that same analysis.
Another contributing factor to perceived culpability is the extent of knowledge of the actors involved in an event, as well as the intent of their actions (Gilbert, Tenney, Holland, & Spellman, 2015). For example, in the aforementioned scenario, had the driver known that looking away from the road would have caused her to run over the stray cat, the driver would have been more likely to be perceived guilty, even though the actions and the outcome of the situation remained the same. This rationalization is the product of a bottom-up method of thinking in which individuals are able to generate more counterfactual thoughts due to the actor’s knowledge of the outcome (Gilbert et al., 2015). As these authors have noted, the increased development of counterfactual thoughts will in turn attribute more responsibility to the actor, which will ultimately increase perceived blame. But this is not the full picture when it comes to focusing on the role of counterfactual thoughts in altering participant responses. Comment by Ryan Winter: Notice the flow from one paragraph to the next. This author isn’t just listing out studies he read; he is making connections between them and has connections from one paragraph to the next Comment by Ryan Winter: Here, the paper author did another nice job of using proper APA formatting. Since the cite is all within parentheses, he used the “&” properly.
In pursuance of counterfactual thinking and its relationship to perceived blame, we have devised a study that analyzed the extent of culpability people place on a particular factor depending on the preventability of the outcome. We provided participants with one of three scenarios, each of which depicted a variation of the same situation where alternate events lead to different conclusions. In the changeable condition, an actor engaged in a behavior that led to an undesirable outcome (death) that could have been avoided had he acted differently. In the unchangeable condition, the same actor engaged in a behavior that once again led to an undesirable outcome, but here the outcome could not have been avoided if he acted differently. In the neutral condition, the actor engaged in an alternative behavior, but the outcome was still undesirable. We predicted that participants would place more blame on the actor in the changeable condition where the actor could have avoided the undesirable outcome had he behaved differently than in both the unchangeable and neutral conditions, where the actor’s behavior could not be altered. This is because we expected changeable participants to generate more counterfactuals (more statements about how the actor could have behaved) in the changeable condition. Comment by Ryan Winter: Now we get into the heart of this student author’s paper. She had prior information on counterfactual thinking and noted the research that had been done in the area. She then uses that info to help set up her own study idea. That is, she gets more narrow as she reaches the end of the paper (ala the hourglass) Comment by Ryan Winter: She notes the design a bit without going into too much detail about the methods (which she will do in the next paper), but gives the reader a taste as she leads them to the cliffhanger! Comment by Ryan Winter: Now we get to the hypotheses. Aren’t you just dying to see what the study tells you now that you have some idea about his predictions!
References Comment by Ryan Winter: References start on its own page. My advice is to enter a page break in Word so that this will always start at the top of the page. Pay close attention to proper APA formatting here
Alquist, J. L., Ainsworth, S. E., Baumeister, R. F., Daly, M., & Stillman, T. F. (2015). The making of might-have-beens: Effects of free will belief on counterfactual thinking. Personality and Social Psychology Bulletin, 41(2), 268-283. doi: Comment by Ryan Winter: Notice how this list is in alphabetical order, starting with Alquist and ending with Williams. This is good APA format
10.1177/0146167214563673
Gilbert, E. A., Tenney, E. R., Holland, C. R., & Spellman, B. A. (2015). Counterfactuals, control, and causation: Why knowledgeable people get blamed more. Personality and Social Psychology Bulletin, 41(5), 643-658. doi: 10.1177/0146167215572137 Comment by Ryan Winter: You will notice that this word is capitalized. This is ok, since it follows a colon
McCloy, R., & Byrne, R. M. J. (2000). Counterfactual thinking about controllable events. Memory & Cognition, 28(6), 1071-1078. doi: 10.3758/BF03209355 Comment by Ryan Winter: Note the form for this peer-reviewed, primary resource. It is good. Author names and their first initial followed by the date. Then the title, with only the first word capitalized. The journal name is italicized, as is the volume number, and the page numbers are good. It also include the DOI
Ruiselová, Z., Prokopčáková, A., & Kresánek, J. (2007). Counterfactual thinking in relation to the personality of women–doctors and nurses. Studia Psychologica, 49(4), 333-339.
Williams, C. W., Lees-Haley, P., & Price, J. R. (1996). The role of counterfactual thinking and causal attribution in accident-related judgments. Journal of Applied Social Psychology, 26(23), 2076-2099. doi: 10.1111/j.1559-1816.1996.tb01789 Comment by Ryan Winter: My advice is to turn on the ruler when using a Word document. Using the ruler, you can make sure that you properly indent all but the first line of each reference (instead of using the space bar or tab) Comment by Ryan Winter: Use the & before listing the name of the last author