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Donald Trump as a Cultural Revolt Against Perceived Communication Restriction: Priming Political Correctness Norms Causes More Trump Support

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Donald Trump has consistently performed better politically than his negative polling indicators suggested he would. Although there is a tendency to think of Trump support as reflecting ideological conservatism, we argue that part of his support during the election came from a non-ideological source: The preponderant salience of norms restricting communication (Political Correctness – or PC – norms). This perspective suggests that these norms, while successfully reducing the amount of negative communication in the short term, may produce more support for negative communication in the long term. In this framework, support for Donald Trump was in part the result of over-exposure to PC norms. Consistent with this, on a sample of largely politically moderate Americans taken during the General Election in the Fall of 2016, we show that temporarily priming PC norms significantly increased support for Donald Trump (but not Hillary Clinton). We further show that chronic emotional reactance towards restrictive communication norms positively predicted support for Trump (but not Clinton), and that this effect remains significant even when controlling for political ideology. In total, this work provides evidence that norms that are designed to increase the overall amount of positive communication can actually backfire by increasing support for a politician who uses extremely negative language that explicitly violates the norm.
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Original Research Reports
Donald Trump as a Cultural Revolt Against Perceived Communication
Restriction: Priming Political Correctness Norms Causes More Trump
Support
Lucian Gideon Conway III*a, Meredith A. Repkea, Shannon C. Houckb
[a] Psychology Department, University of Montana, Missoula, MT, USA. [b] Psychology Department, Syracuse University, Syracuse, NY,
USA.
Abstract
Donald Trump has consistently performed better politically than his negative polling indicators suggested he would. Although
there is a tendency to think of Trump support as reflecting ideological conservatism, we argue that part of his support during
the election came from a non-ideological source: The preponderant salience of norms restricting communication (Political
Correctness – or PC – norms). This perspective suggests that these norms, while successfully reducing the amount of negative
communication in the short term, may produce more support for negative communication in the long term. In this framework,
support for Donald Trump was in part the result of over-exposure to PC norms. Consistent with this, on a sample of largely
politically moderate Americans taken during the General Election in the Fall of 2016, we show that temporarily priming PC
norms significantly increased support for Donald Trump (but not Hillary Clinton). We further show that chronic emotional
reactance towards restrictive communication norms positively predicted support for Trump (but not Clinton), and that this effect
remains significant even when controlling for political ideology. In total, this work provides evidence that norms that are designed
to increase the overall amount of positive communication can actually backfire by increasing support for a politician who uses
extremely negative language that explicitly violates the norm.
Keywords: Donald Trump, political correctness, communication norms, culture, backfiring
Journal of Social and Political Psychology, 2017, Vol. 5(1), 244–259, doi:10.5964/jspp.v5i1.732
Received: 2016-10-28. Accepted: 2017-03-29. Published (VoR): 2017-05-10.
Handling Editor: Małgorzata Kossowska, Jagiellonian University, Kraków, Poland
*Corresponding author at: 143 Skaggs Building, University of Montana, MT, USA. E-mail: luke.conway@umontana.edu
This is an open access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
“I’ve been talking about negatives, and you’re up on him!” said an
astounded [Republican pollster] Frank Luntz. “That’s the story of Trump’s
poll numbers.” (From Guo, 2015)
Controversial United States President Donald Trump presents a bit of a puzzle. Since the beginning of his candi-
dacy during the election season, Trump’s poll numbers suggested that a lot of people did not like him, and yet in
actual election results he has garnered surprising levels of support that belie those negatives (Guo, 2015).
Journal of Social and Political Psychology
jspp.psychopen.eu | 2195-3325
This juxtaposition can be seen in Trump’s often alarming use of non-normatively negative language. From his
comments about Mexicans being “murderers” and “rapists” (Guo, 2015), to questioning POW John McCain as a
war hero (Noble, 2015), to his insult of former GOP rival Carly Fiorina: "Can you imagine that, the face of our next
president?... are we serious?" (Cohen, 2017), Trump has shown that he does not conform to typical political lan-
guage. Yet despite all of his often bracingly insensitive language, Trump won both the Republican Party nomination
for president and ultimately the General Election.
One explanation for Trump’s success involves a socio-historical discussion that is essentially exclusive to conser-
vative American politics (see, e.g., Oliver & Rahn, 2016). For example, evidence suggests that increased author-
itarianism, increased social dominance, and lower cognitive abilities were all predictive of Trump support during
the election (Choma & Hanoch, 2017). Although these dimensions are important in our understanding of Trump
support, they may also lead to a dismissal of Trump’s electoral success as a function solely of traits associated
with ideologically extreme conservatism in the U.S. And while it is true that his supporters are largely ideologically
conservative (e.g., Burnett, 2016;Pew Research Center, 2016;Thompson, 2016), that is not the whole story. In
the present article, we expand upon existing social psychological theories of deviance to argue that part of his
support came from a source that is not directly ideological in nature: Namely, that the surprising levels of support
for Trump were the result of the salience of restrictive communication norms. In order to establish the connection
between psychological theory about cultural norms and Donald Trump, we first discuss a theoretical framework
on what causes deviance from cultural norms in the first place.
The Backfiring of Cultural Norms
It is practically an axiom in social psychology that cultural norms influence behavior. If there is a cultural norm to
drive on the right side of the road, then most people drive on the right side. If there is a cultural norm to drive on
the left side, then most people become left-side drivers. And when we shift cultural contexts, we frequently learn
which side of the cultural road to drive on – and drive accordingly. However, it is clear that people do not always
follow cultural norms, and an emerging, theory-driven literature has sought to more clearly illuminate those conditions
under which cultural norms do and do not induce compliance.
One theoretical perspective focuses on the sometimes ironic consequences of top-down, heavy-handed norms.
This communication perspective suggests that while top-down cultural norms may often produce short-term
compliance, the communication of heavy-handed norms sometimes simultaneously produces a sense of forced
artificiality that causes them to ultimately backfire and instead produce long-term deviance (see, e.g., Conway &
Schaller, 2005;Conway & Schaller, 2007;Conway et al., 2009). Thus, the forced consensus that a cultural norm
produces can be like a pane of glass with a small, barely perceptible crack: As long as all else remains equal, it
will remain as a single piece of glass – but a little change in context or pressure, and the glass pane will break
(see Conway & Schaller, 2005;Conway et al., 2009).
Why Do Cultural Norms Backfire? Reactance and Informational Contamination
What specifically causes the cracks on the pane of cultural glass? Prior research and theory suggests that this
can be for two separate reasons. (1) First is emotional reactance (e.g., Brehm, 1966;Crawford, McConnell, Lewis,
& Sherman, 2002;Fuegen & Brehm, 2004). Freedom of choice is a valued psychological commodity, and so we
often will deviate from others’ expectations—both in belief and in action—in order to re-assert our right to choose.
This feeling may be suppressed in the short-term to avoid embarrassment or public sanction; but in the long-term,
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Conway, Repke, & Houck 245
any reason (such as a surprisingly-popular candidate who is not politically correct) to express it may draw it out.
(2) Second is informational contamination (Conway & Schaller, 2005;Conway et al., 2009; see also Fein et al.,
1990;Harkins & Petty, 1987;Maio & Esses, 1998). A heavy-handed norm doesn’t just produce a strong emotional
response – it also cognitively undermines the informational value of any emergent consensus by causing observers
to attribute the consensus to the coercion induced by the norm instead of the potential information contained in
the observed behavior.
Thus, a heavy-handed norm may induce short term compliance, but it simultaneously sows the seeds of its own
later undoing: It makes people feel bad about the norm emotionally, and (independent of that) it cognitively con-
taminates the informational value that the norm otherwise might have produced.
Restrictive Communication Norms and Donald Trump
Viewed from this norm communication perspective, Donald Trump is not the cause of cultural deviance – rather,
support for him is (in part) the product of the salience of restrictive communication norms. To illustrate, we discuss
the specific set of communication norms in question and Donald Trump’s overlap with those norms.
The Backfiring of Positively-Aimed Communication Norms
An extremely powerful set of norms in North American society, often called “political correctness” (or “PC”) norms,
explicitly attempts to remove negative group-relevant language (see Conway et al., 2009). As a result, in situations
where the norms are in evidence, they create particularly strong public pressure to restrict one’s communication.
Although sometimes derided, few academics would disagree that the practical goal of the political correctness
movement is well-aimed.
One of the distinguishing features of modern theories of norm backfiring is that they do not require a norm to have
a negative aim itself in order to ultimately show negative effects. Indeed, some research suggests that over-
salience of PC norms may actually undermine its positive goal and produce more negative communication in the
long-term. In one set of research studies involving fictitious scenarios, the stated presence of a heavy-handed
PC norm caused participants to later report that they would communicate more negatively about a stereotyped
fraternity to a fictional “friend” in the scenarios (Conway et al., 2009; see also Conway & Schaller, 2005).
Donald Trump as Cultural Revolt Against Restrictive Communication Norms
“It’s not just that Trump is willing to be provocative – he’s exciting to
many people because he says things they feel they can’t say.”
(From Guo, 2015)
So there is reason to suspect that consistent salience of PC norms might cause a crack on the cultural pane of
glass. Is there reason to suspect that Donald Trump might be a product of that crack? Yes. As the above quote
suggests, it may be in part because of his politically incorrect rhetoric that he garnered support. At a general level,
evidence suggests that Trump’s grandiose rhetorical style was one of the reasons he won the Republican primary
(Ahmadian, Azarshahi, & Paulhus, 2017). Further and more specifically, polls from the election cycle suggested
that people liked his provocative language (Guo, 2015;Thompson, 2016) and that feeling voiceless better predicted
Trump support than multiple other variables, some of which include age, race, and attitudes towards Muslims, il-
legal immigrants, and Hispanics (Thompson, 2016). And indeed, Trump himself has publically recognized the
value of this anti-communication norm stance. As he said at the Republican Primary debate in August 2015: "I
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Donald Trump as Cultural Revolt 246
think the big problem this country has is being politically correct. I’ve been challenged by so many people and I
don’t, frankly, have time for total political correctness” (quoted in Guo, 2015).
The Present Research
In summary, to date evidence exists that (1) restrictive communication norms can sometimes backfire in fictitious
scenarios (e.g. Conway et al., 2009), and (2) some people report they like Trump’s willingness to violate cultural
communication norms (Guo, 2015;Thompson, 2016).
However, this leaves many gaps to fill. (1) First, prior scientific work on the backfiring of PC norms has manipulated
norm salience only in hypothetical role-playing contexts where participants imagined their behavior in fictitious
scenarios using fictitious target groups. (2) Further, although polling and survey data suggest reasons to believe
that Trump is an iconic representative of anti-communication norms, no scientific test has directly tied support for
Trump to political correctness norms in a controlled experimental environment. (3) There is an implication in polling
data and public discourse to focus on support for Trump as a conservative ideological issue (e.g., Burnett, 2016;
Pew Research Center, 2016), and yet some academic analyses also suggest that Trump’s supporters may not
be true ideological conservatives (Noel, 2016). Thus, it is important to parse out the effects of restrictive commu-
nication norms from that of political ideology.
To accomplish these goals, in the midst of the 2016 U.S. Presidential election, we manipulated the salience of
PC norms by randomly assigning some participants to a restrictive communication prime where they read and
responded to a brief description of the benefits of PC norms. We then measured (a) support for Trump and Clinton,
(b) chronic concern with restrictive communication norms, and (c) political conservatism. We did this on a slightly
left-leaning (M= 4.36 on a 1-9 conservatism scale) sample of non-college adults (Mage = 34.38).
We expected that (1) participants’ chronic feelings of concern with restrictive communication norms would predict
support for Trump, and (2) priming restrictive communication norms would increase support for Trump. We further
expected that these effects would either not be in evidence – or be reversed – for Hillary Clinton.
Method
Participants and Disclosures
Three hundred and twelve participants were recruited through Mechanical Turk. We chose Mechanical Turk in
part because it is been particularly validated for use as a representative sample for research related to politics
and political ideology (see, e.g., Clifford, Jewell, & Waggoner, 2015) and generally shows similar results as other
samples (for an example, see Houck, Conway, & Repke, 2014). Participation occurred in early-to-mid September,
a period where the race between Clinton and Trump had tightened, but Clinton still maintained a lead in the polls
(see http://www.realclearpolitics.com/epolls/2016/president/us/). We excluded seven participants who failed to
answer an attention-check question accurately, leaving 305 for final analyses. No data collection occurred after
data analysis, and all relevant manipulations, measures, and exclusions are disclosed.
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Restrictive Communication Prime Manipulation
Prime Condition
Some participants (n= 95) were randomly assigned to the Restrictive Communication Prime Condition. Participants
in this condition read the following introduction:
“First, we would like to get your opinions on societal norms. In our modern society, we have norms that
dictate that we refrain from saying negative things – especially those things deemed as politically incorrect
to say. These norms state that it is better to have rules that constrain us from anything that might sound
too-negative or might be offensive to members of particular groups. These social norms that discourage
too-negative conversation have many good benefits, and we first want to get your opinion on these norms
before moving forward.”
Participants were then asked to respond to four questions expressing either support for or opposition to those
norms. These questions (on a scale anchored by 1 and 7) asked participants the degree they were in favor of
such norms, that such norms have value, that they oppose societal pressures to restrict communication, and that
they believe in the value of norms governing communication.
Control Conditions
The present study used two different control conditions. First, some participants (n= 91) were randomly assigned
to receive a description parallel to Restrictive Communication Prime participants; however, their description con-
tained only positively-framed suggestions rather than negative restrictions (and made no reference to political
correctness). Participants in this condition also answered four questions in support of norms that suggest we
should be positive, sensitive, and respectful in our communication.
A second group of participants (n= 119) was randomly assigned to receive no introduction about communication
or complete any questions relevant to communication norms.
These two control conditions showed largely identical patterns in the present work; for simplicity, we combined
them into one Control Condition.i
Dependent Measures: Support for Trump and Clinton
Voting Intent
In two separate items, participants were first asked to report on a 7-point rating scale how likely they were to vote
for Trump and Clinton, respectively.
Trait Positivity Ratings
Participants were then asked to rate each candidate on a series of parallel trait rating scales drawn from prior
work (e.g., Conway et al., 2012). We organized these by semantic meaning: Five questions pertained to honesty
(honest, liar [reverse], speaks mind, goes against norms, truthful; alphas = .92 and .87), four pertained to adapt-
ability (flexible, thoughtful, complex, smart; alphas = .87 and .90), three pertained to strength (strong leader, tough,
consistent; alphas = .85 and .88), two to general likability (like, respect; alphas = .93 and .91), and one each to
kindness and attractiveness.
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Donald Trump as Cultural Revolt 248
In order to produce an overall trait positivity rating that was unbiased by this semantic organization, we converted
all sixteen trait rating items to z-scores and averaged them into a single trait positivity score for each candidate
(alphas = .96).
Cumulative Trump and Clinton Support Measures
To capture the overall degree across all measures that participants supported Trump and Clinton, we converted
each candidate’s voter preference and trait positivity scores to z-scores and averaged them into cumulative Trump
Support (alpha = .92) and Clinton Support (alpha = .88) measurements.ii
Restrictive Communication Concern
We measured two constructs related to restrictive communication concern by adapting items from prior studies
(Conway & Schaller, 2005;Conway et al., 2009). Three items measured reactance (alpha = .55): “I often feel
pressured by society to keep my own negative political opinions to myself – and when I do, it makes me feel like
I want to say my opinions anyway so no one can tell me how to think or talk,” “I often feel like political pundits are
trying to make me fit their own narrow views and it makes me upset,” and “Political correctness norms generally
aggravate me.”
Six items measured informational contamination (alpha = .71):“I believe that a lot of what politicians say is just
to avoid saying things that might offend particular groups,” “I distrust a lot of what politicians say because I assume
it is reflective of some political agenda,” “I believe that a lot of what politicians say is just to avoid saying things
that might offend particular groups,” “I believe that politicians generally keep their real opinions to themselves
because if they didn’t, they would be in trouble with powerful groups in society,” “I believe that politicians do not
respond to social norms telling them what to say and, therefore, what they say is what they generally really believe”
(reverse-scored), and “I distrust most of what members of the Democratic party say because I assume they are
just manipulating people,” and “I distrust most of what members of the Republican party say because I assume
they are just manipulating people.” Reactance and informational contamination were modestly correlated (r= .43).
Political Ideology
Participants completed two standard ideology items anchored by democrat/republican and liberal/conservative
on 1-9 rating scales (see, e.g., Conway, Gornick, et al., 2016;Conway, Houck, Gornick, & Repke, 2016). We av-
eraged them into a single measurement of political conservatism (alpha = .92, M= 4.36; SD = 2.30).
Results
Chronic Concern With Restrictive Communication Predicting Support for Trump and
Clinton
We correlated our two primary measurements of restrictive communication concern (reactance, informational
contamination) with measurements of support for Trump and Clinton. Table 1 presents the outcome of these
analyses both for zero-order predictions as well as those which control for political ideology.
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Table 1
Chronic Restrictive Communication Concern Predicting Support for Trump and Clinton
Informational ContaminationReactance
Controlling for IdeologyZero-OrderControlling for IdeologyZero-Order
Trump Trait Ratings
Honest .10-.09.20***.40***
Strong .02-.12*.18***.37***
Kind .13*-.04.18**.37***
Adaptable .04-.11.12*.32***
Attractive .18***-.07-.04.19***
General Liking .07-.10.25***.43***
Trump Total Trait Positivity .09-.09.18***.39***
Trump Voting Likelihood .14*-.08.10.35***
Total Trump Support .12*-.09.16**.38***
Clinton Trait Ratings
Honest .26***-.33***-.20***-.33***-
Strong .08-.18**-.22***-.37***-
Kind .23***-.30***-.22***-.36***-
Adaptable .01-.11-.16**-.30***-
Attractive .22***-.28***-.19***-.30***-
General Liking .17**-.26***-.21***-.38***-
Clinton Total Trait Positivity .17**-.26***-.23***-.37***-
Clinton Voting Likelihood .12*-.23***-.20***-.39***-
Total Clinton Support .16**-.26***-.24***-.40***-
Note. N = 305.
*p≤ .05. **p≤ .01. **p≤ .001.
As can be seen there, a pattern emerged for Reactance consistent with expectations: Persons who felt chronic
reactance at restrictive communication norms were significantly more likely to support Trump (overall support r=
.38) and significantly less likely to support Clinton (overall support r= -.40) – and both of those effects remained
significant even when controlling for political ideology (p’s < .01).
The pattern for informational contamination was less consistent. Informational contamination weakly but positively
predicted support for Trump, but this pattern was reversed when controlling for ideology. Support for Clinton was,
consistent with expectations, significantly negatively predicted by informational contamination (and this remained
significant when controlling for ideology).iii
Overall, these data suggest qualified support for the perspective outlined here: It is clear that support for Trump
(and opposition to Clinton) is especially likely amongst people who feel emotional reactance to restrictive commu-
nication norms – and importantly, this effect goes beyond political ideology. To a lesser degree, persons who express
concern about informational contamination conformed to the expected pattern for zero-order correlations; but
while a significant effect remained for Clinton in the expected direction when controlling for ideology, the effect
for Trump actually reversed (albeit weakly). We return to what these differences might mean in the Discussion.
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Donald Trump as Cultural Revolt 250
Priming of Restrictive Communication Norms
A series of One-Way ANOVAs compared the Restrictive Communication Prime versus Control Conditions. Table
2presents these results in full. As can be seen there, these results provided clear support for the norm backfiring
perspective outlined in the introduction: Participants made to think about restrictive communication norms showed
more support for Trump than controls (cumulative Trump Support F[1,304] = 5.39, p=.02). Also consistent with
expectations, this effect was exclusive to Trump (cumulative Clinton Support F[1,303] = 0.05, p=.82).iv
Table 2
Effects of Restrictive Communication Prime on Support for Trump and Clinton via One-Way ANOVAS
Difference p-valueEffect Size rRestrictive PrimeControl Conditions
Trump Trait Ratings
Honest .009*.15**.19.09-
Strong .137.09.11.05-
Kind .020*.13*.20.09-
Adaptable .157.08.10.05-
Attractive .398.05.07.03-
General Liking .047*.11*.16.08-
Trump Total Trait Positivity .039*.12*.14.06-
Trump Voting Likelihood .019*.13*.20.09-
Total Trump Support .021*.13*.19.09-
Clinton Trait Ratings
Honest .418.05.06.03-
Strong .852.01-.01-.01
Kind .667.03-.04-.02
Adaptable .560.04.05.02-
Attractive .872.01.01.01-
General Liking .920.01.01.00-
Clinton Total Trait Positivity .760.02.02.01-
Clinton Voting Likelihood .556.03-.05-.02
Total Clinton Support .823.01-.01-.01
Note. N = 305.
*p≤ .05. **p≤ .01. **p≤ .001.
Moderation and Mediation of Priming Effect
We expected that the effect of the prime would likely be especially in operation for persons who had higher levels
of chronic concern with restrictive norms. To test this, we performed simultaneous regression analyses (using the
SPSS macro developed by Hayes, 2013) entering (a) the dummy-coded prime, (b) each moderator, and (c) the
interaction of each moderator with the prime (for exemplars, see e.g., Conway & Schaller, 2005; Conway et al.,
2009). For brevity, we focus here on analyses predicting the cumulative support measures which incorporate all
the support-based measurements.
These results revealed that reactance was a significant moderator for the Prime – Trump Support effect (interaction
beta = .16, p= .03, lower CI = .01, upper CI = .32). Subsequent analyses (Hayes, 2013) revealed the expected
pattern: When participants felt low levels of reactance against restrictive norms, the effect of the prime on Trump
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Conway, Repke, & Houck 251
Support was small (beta = .13, p= .36); but at high levels, this effect was much larger and significant (beta = .57,
p< .001). This same moderating pattern held for informational contamination on Trump Support; however, the
interaction did not approach statistical significance (beta = .16, p=.184, LCI = -.08, UCI = .39).
A conceptually identical pattern emerged for Clinton Support, although which restrictive communication concern
variable was the strongest moderator was reversed: Informational Contamination was a significant moderator
(interaction beta = -.24, p= .034, lower CI = -.47, upper CI = -.02). Subsequent analyses revealed a pattern
largely consistent with expectations: When participants felt low levels of informational contamination, the effect
of the prime on Clinton Support was negative (beta = -.27); but at high levels, this effect was actually somewhat
reversed (beta = .22). This same moderating pattern held for reactance on Clinton Support; however, the interaction
did not approach statistical significance (beta = -.08, p= .33, LCI = -.23, LCI = .08).v,vi
To test the mediating effect of reactance and informational contamination, we tested their indirect effects in a series
of A(Prime)→B(Mediator)→C(Candidate Support) analyses (Hayes, 2013). These analyses revealed weak and
non-significant indirect effects of the two general concern measurements with restrictive communication norm
measurements (all Sobel tests of indirect effect ps > .16)vii – indeed, the prime did not significantly affect either
reactance or informational contamination, making the A→B→C path untenable. We return to what this might mean
for interpretation in the discussion.viii
General Discussion
Taken in total, these results provide support for a connection between the salience of restrictive communication
norms and support for Donald Trump. Participants primed to think about restrictive communication norms showed
more support for Trump than participants who were not so primed. Further, participants who felt chronic reactance
to communication norms were especially likely to express support for Trump (above and beyond their political
ideology), and were further especially likely to respond to the prime by increasing Trump support. In the main,
these results either did not hold – or were reversed – for supporting Clinton.
These results are not without their limitations and interpretational ambiguities, however. We first address those
limitations and then discuss, in larger perspective, what we can learn from these results.
Interpretation Obstacles
Lack of Mediation by Chronic Concern Measurements
One of the interpretational ambiguities in the present results involves the fact that, although chronic concern
measurements largely predicted support for Trump (and opposition to Clinton) across the whole sample, these
measurements did not also mediate the relationship between the Restrictive Communication Prime and Trump
Support. If the Prime operates by heightening the salience of restrictive communication norms, why would these
measurements not partially account for the effect of the prime on candidate preference?
A few points suggest that this lack of mediation was likely the result of our reactance and informational contami-
nation measurements being too stable to effectively serve as the mediator for the effect of a temporary prime.
First, at face value, the questions were written at a very general level to apply to any political group, norm, or
politician. Prior work that showed the mediation of emotional reactance and informational contamination on PC
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norm priming was written specifically for a very clearly identified scenario – thus the overall psychological distance
between the prime, proposed mediator, and dependent measure was far less in that work than in the present
study (see Conway et al., 2009). As an initial test of the possibility that this helps account for our results, we took
the three questions from the trait positivity rating that were most related to scenario-specific concern with commu-
nication norms and combined them into a single measurement of scenario-specific concern with communication
norms (all reverse-scored; alpha = .87). The three items were: “I appreciate that Donald Trump seems to say
whatever is on his mind, even if it upsets people,” “For the most part, I trust Donald Trump to speak truthfully,”
and “Donald Trump goes against cultural norms that restrict free speech, and that’s a good thing.” We then used
this measurement as the mediator an A(Prime)→B(Mediator)→C(Outcome) analysis of indirect effects. (To avoid
too much overlap between related measurements, we used only voting preference as the outcome measurement.
Results are identical if we use the overall Trump Support measurement). Results suggested a significant indirect
effect of the newly-constructed, Trump-specific communication norms measurement (beta = .50; lower CI = .04;
upper CI = .96; Sobel test z= 2.16; p=.03). We recognize that this measurement has some method overlap (and
that is one of the reasons why we did not use it as the primary measurement of the construct in the first place)
and it should be interpreted cautiously. However, it is at least consistent with the notion that there was a more
transient communication-relevant mediator of the present results.
Second, the chronic measurements of emotional reactance and informational contamination did interface with the
prime; however, they interfaced as one would expect a measurement that is more chronic and thus less subject
to direct temporal fluctuations. Specifically, these measurements served as fairly consistent moderators of the
prime→support relationship.
Third, these chronic measurements were predictive overall of support for Trump (and especially, opposition to
Clinton) in ways that are generally consistent with predictions (with some caveats we return to later in the discus-
sion). This is again consistent with their status as a more stable measurement that is relevant to the proposed
outcomes at a larger, more stable level.
Finally, these results do not stand alone. Although they are unique in their application of political correctness
priming to Trump support, other work shows the potential backfiring of communication norms and artificial consensus
in other domains (Conway & Schaller, 2005;Conway et al., 2009). As a result, in the larger theoretical landscape,
there is reason to believe that concern with restrictive communication norms may have an impact in the present
work.
Taken together, this evidence suggests that while the lack of direct mediation by those measurements is a
weakness in the present results, we feel there is ample reason to nonetheless trust that the larger picture
emerging here is one best captured by a theoretical angle focusing on concern with restrictive communication.
Inconsistency of Chronic Concern With Communication Norms?
Another interpretational ambiguity lies in the fact that Trump Support was predicted by chronic emotional reactance
but not chronic informational contamination. What are we to make of this inconsistency?
One possibility is that Trump simply evokes more emotional deviance than cognitive deviance. Reactance is more
purely emotive (see Knowles & Linn, 2004); informational contamination is more purely cognitive (see Conway &
Schaller, 2005;Conway et al., 2009). Recall that in prior research and theory these two types of deviance are in-
dependent predictors of cultural norms backfiring. Every theoretically sound predictor cannot predict every phe-
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Conway, Repke, & Houck 253
nomenon – and in this case, it may be rather instructive that it is emotional reactance, and not informational con-
tamination, that predicts Trump support. To the degree this is true, it suggests that it is more of a guttural emotive
dislike of restriction than a cognitive erosion of information that is behind Donald Trump’s support.
Indeed, it is noteworthy that these results actually show an interesting set of consistencies across analyses that
partially support this conclusion. (1) First, not only was emotional reactance a significant predictor of Trump support
overall, it (and not informational contamination) was a significant moderator of the effect of the prime on Trump
support. This suggests the prime is having its effect on people who have a chronic emotional dislike of restrictive
communication norms, more so than people who are cognitively concerned with informational contamination. (2)
Equally as interestingly, informational contamination had much more of an impact on support for Clinton (compared
to support for Trump) in the opposite direction, both as a direct predictor and as a moderator of the prime→Clinton
support relationship. While it is difficult to fully know how to interpret this and any interpretation must remain
speculative, it is at least consistent with the notion that support for Clinton is more of a function of cognitive concerns
than is support for Trump.
What Can We Learn?
At a minimum, these results provide evidence that (a) priming restrictive communication norms and (b) chronic
emotional reactance against those norms both increased support for Donald Trump during the general election.
These results help us in our theoretical understanding of culture in multiple ways. First, they provide for the first
time to the authors’ knowledge an experimentally-manipulated example of a cultural norm’s ironic effect on a real-
world political example. Indeed, a reasonable criticism of much of the past work on the backfiring effect of cultural
norms is that it focuses on hypothetical scenarios that are removed from the real world (see, e.g., Conway et al.,
2009). While this prior work has value, it is important to establish any set of effects in real contexts that truly
matter to people, and the present study does just that.
Second, these results also contribute to the larger theoretical discussion about how cultures emerge, change,
and have influence. It is noteworthy that, unlike in prior research on the backfiring of communication norms, the
prime in this study bore little direct linguistic overlap with the outcome measurement (support for Donald Trump).
As a result, it suggests the pervasive value of understanding the eventual emergence of controversial political
figures through the lens of communication-based theories of cultural norms and cultural deviance.
Cultures emerge and change for many reasons. These can be considered in two basic categories: (1) Top-down
ecological or political pressures – such as frontier topography or dictatorial political systems – may directly constrain
how culture emerges (e.g., Conway et al., 2014;Kitayama, Conway, Pietromonaco, Park, & Plaut, 2010;Murray
& Schaller, 2014;Van de Vliert, 2013). (2) However, often communication processes operate independent of
these top-down pressures to shape the emergence and influence of culture (e.g., Conway, 2004;Conway &
Schaller, 2007;Conway, Sexton, & Tweed, 2006;Schaller, Conway, & Tanchuk, 2002). The present study falls
squarely at the intersection of these two categories. It suggests that the top-down pressures that often shape
culture are themselves subject to communication and psychological processes that direct their influence. If the
top-down communication norms are too heavy-handed, they may indeed create a forced consensus; but that
consensus is psychologically fragile and prone to break. The present results thus complement prior work on hy-
pothetical scenarios, providing a very important bellwether test for the degree that large-scale cultural norms might
ironically rebound. They suggest that instead of producing the niceness that they are intended to produce, these
norms instead might lead to more nasty political discourse in the long-term.
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Donald Trump as Cultural Revolt 254
Third, these results contribute to the larger literature on the importance of the immediate context to political deci-
sions. Consider, for example, work on framing that demonstrates the specific way a particular finding is discussed
– or framed – can have a great deal of impact on political attitudes and behaviors (Bizer, Larsen, & Petty, 2011;
Bizer & Petty, 2005;Brewer & Gross, 2005;de Vreese, Boomgaarden, & Semetko, 2011). Some of this works
suggests that positive framing of news articles can increase the likelihood of endorsement of a specific opinion
(e.g., de Vreese et al., 2011). Other work shows that framing an attitude as an “opposition” to something increases
the subsequent longevity of the opinion and its strength (Bizer & Petty, 2005;Bizer et al., 2011).ix
Like this prior work on framing, our work also suggests a mechanism by which the immediate context can influence
political attitudes. Specifically, our work suggests that the immediate salience of chronic communication norms
may aid candidates who appear to violate those norms. Thus, communication norms can function psychologically
in much the same way that frames can by focusing persons on a part of the context that ultimately shapes their
attitudes and decisions.
Finally, these results also demonstrate the value of applying both existing social psychological theory and exper-
imental methods to understand puzzles in the modern world. A descriptive illustration of this power can be seen
in Figure 1. Looking at the raw mean scores for voting preference – the clearest marker in our study of the likelihood
of potential cultural change – reveals a powerful pattern: In control conditions, Clinton was soundly preferred to
Trump (Clinton M= 3.66, SD = 2.41; Trump M= 2.63, SD = 2.28), but when Political Correctness norms were
made salient, this gap virtually disappeared (Clinton M= 3.48, SD = 2.51; Trump M= 3.32, SD = 2.51).
Figure 1. Voting likelihood for Trump and Clinton by restrictive communication prime.
Concluding Thoughts
Although Donald Trump presents an interesting paradox of sorts to modern political pundits, his emergence is
precisely what a theory focusing on the backfiring of social norms would expect. It is a paradox, but a theoretically-
expected one: As restrictive norms become ever more salient and heavy-handed, the more they will work in the
short-term. But in the long-term, this salient heavy-handedness increases the likelihood that they will ultimately
backfire. And this backfiring doesn’t just occur for norms that are genuinely repressive to political freedom – it also
occurs for norms that have a clearly good and noble aim.
Journal of Social and Political Psychology
2017, Vol. 5(1), 244–259
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Conway, Repke, & Houck 255
The present study suggests that communication norms that are designed to increase the amount of positive
communication may ultimately backfire in political figures like Donald Trump – figures who do anything but increase
the amount of positive communication. His emergence should serve as a lesson for students of cultural change
and deviance. He is not the first and will certainly not be the last example of popular figures emerging in response
to restrictive norms – and the present work illustrates specific psychological processes that help us better understand
why that occurs and when it will occur.
Notes
i) Originally, we included the positive communication control as a way of (a) delineating the exact psychological locus of any
differences between the Restrictive Communication Prime and the no-introduction Control (e.g., was a negative restrictive
tone necessary to find the effect as we suspected?), and (b) accounting for the general introduction confound (e.g., the positive
communication control, unlike the no introduction control, contains instructions and questions parallel in structure to the Prime,
but differing in content). Because our predictions for the positive communication Control were less clear than those for the no
introduction Control, this leaves open the possibility that lumping the positive communication Control with the no introduction
Control artificially inflates power. To account for this, we also performed analyses for major findings that excluded the positive
communication Control condition. We report these analyses in footnotes. As will be seen there, these analyses reveal a pattern
largely identical, both descriptively and inferentially, to that reported in the main text. Because we believe the positive
communication Control adds value to the manuscript (see discussion), we opted to include it here. However, excluding it would
not change the basic conclusions drawn from this study.
ii) We also included a categorical measurement of who participants indicated they would vote for. This measurement was
collected largely for sample description purposes and was never analyzed by condition for hypothesis testing. Like the continuous
measurement, it revealed a sample that was pro-Clinton (43% Clinton; 25% Trump); but it also revealed that 32% of the sample
reported that they were going to vote for a third party, would not vote at all, or were undecided.
iii) These results remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control. Reactance was positively correlated with cumulative Trump Support, both zero-order and while controlling for ideology
(zero-order r= .38, p< .001; partial r= .19, p = .006). Neither zero-order nor partial correlations were significant for Informational
Contamination predicting Trump Support (zero-order r= .08, p= .27; partial r= -.11, p = .12). Both Reactance and Informational
Contamination were negatively related to Clinton support at for both zero-order and partial correlations controlling for ideology
(r’s < -.22, p’s ≤ .001).
iv) These results remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control: The effect of the Prime on overall Trump Support remained significant (F[1, 212] = 4.51, p= .03), and the effect of
the Prime on overall Clinton Support remained non-significant (F[1,212] = 0.33, p= .56).
v) We also performed parallel moderation analyses for political ideology. These results revealed that ideology did not significantly
moderate the effect of the prime on either Trump Support (interaction p= .271) or Clinton Support (interaction p= .49), although
– descriptively speaking – the Prime worked much more effectively on political conservatives than on political liberals.
vi) These effects remained virtually identical – both descriptively and inferentially – when excluding the positive communication
Control: The Prime X Reactance interaction remained significant for Trump Support (p= .02) and the Prime X Informational
Contamination interaction was nearly-significant for Clinton Support (p= .05). The Prime X Informational Contamination on
Trump Support and Prime X Reactance Interaction on Clinton Support were in the same direction descriptively but, as in the
main text analyses including all participants, were not significant.
vii) When excluding the positive communication Control, these results were virtually identical (all Sobel test p’s > .34).
viii) Interestingly and unexpectedly, the Prime made people more report more general conservatism based on our self-report
political ideology measure, and this ideology measurement showed a significant indirect effect in an A→B→C model for both
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Donald Trump as Cultural Revolt 256
Trump Support and Clinton Support (Sobel test p’s < .03). This suggests the interesting possibility that the Prime’s effects on
Trump and Clinton are being partially carried through a general shift in conservatism.
ix) Although it was not our primary focus, one might draw a parallel in our study to positive versus negative framing. In particular,
as we suspected, a control condition that had a parallel, softer, “positive” discussion of communication norms showed essentially
the same effects as a no-introduction control. This is further consistent with the fact that the results for Trump are more a
function of emotional reactance than informational contamination, because emotional reactance (unlike informational
contamination) is more dependent on the emotional tone of a message (see Conway & Schaller, 2005;Conway et al., 2009).
Thus, it is in the heavy-handed restrictive framing of the norm that people find a desire to culturally revolt and support Trump.
This corroborates the basic lesson of this study – and provides hope that softer, more positively-framed instantiations of
communication norms may be more effective.
Funding
The authors have no funding to report.
Competing Interests
The authors have declared that no competing interests exist.
Acknowledgments
The authors have no support to report.
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