Content uploaded by Tatiana Sokolova
Author content
All content in this area was uploaded by Tatiana Sokolova on Sep 22, 2017
Content may be subject to copyright.
Research Dialogue
A focus on partisanship: How it impacts voting
behaviors and political attitudes
Aradhna Krishna
a,
⁎, Tatiana Sokolova
b
a
University of Michigan, 701 Tappan Ave., Ann Arbor, MI 48109, USA
b
Tilburg University, 2 Warandelaan, Tilburg, 5037 AB, Netherlands
Accepted by Sharon Shavitt, Area Editor
Received 12 July 2017; received in revised form 22 July 2017; accepted 22 July 2017
Available online 25 July 2017
Abstract
The target article by John Jost (2017 –this issue) focuses on political ideology (liberalism vs. conservatism) and its association with personal
characteristics, cognitive processing style, and motivational interests. Jost's arguments and data are very compelling and will inspire consumer
psychologists to do more research in the political domain. To enable this goal further, we complement the target article by focusing on partisanship,
another major determinant of political judgments and decisions. Whereas political ideology refers to people being more liberal or conservative,
partisanship refers to how strongly people identify with a specific political party (e.g., Republicans or Democrats). In reviewing the literature on
partisanship, we concentrate on voting behaviors and attitudes, an area not addressed by Jost, but of great importance for consumer psychologists
given the large expenditures on political advertising. Adding to Jost's discussion of the link between political ideology and systematic processing,
we examine the interplay between these two constructs and partisanship.
© 2017 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Keywords: Partisanship; Voting; Political persuasion
Introduction
“I am a Christian, a conservative, and a Republican, in that
order.”Mike Pence, the 2016 Republican vice presidential
candidate, used this phrase to introduce himself on numerous
occasions, including his vice-presidential nomination acceptance
speech at the Republican National convention. Similarly,
Tim Kaine, the 2016 Democratic vice presidential candidate,
repeatedly defined himself in terms of his political ideology, with
one of his earlier Senate campaign ads titled “Conservative”.As
John Jost explains in his target article (2017 –this issue), political
ideology (liberal/conservative; left/right) refers to a set of beliefs,
opinions, and values that shape how people interpret their
environment and how they think it should be structured. The target
article convincingly shows that conservative (vs. liberal) ideology
is strongly associated with an array of personality characteristics
(e.g. conscientiousness and orderliness for conservatives; com-
passion and openness for liberals) and motivational interests (e.g.
pertaining to stability vs. instigating change) (Jost, 2017). It is
therefore not surprising that many politicians emphasize their
ideology when communicating with the electorate: political
ideology carries a lot of information about political candidates
and can exert substantial influence on voters' behaviors.
Interestingly, some politicians choose to define themselves in
terms of their party affiliations, rather than their ideologies. For
instance, Frederick Douglas defined himself as a “Republican,”
and Franklin Roosevelt –as “Christian, and a Democrat.”
Because party affiliation is often correlated with conservative/
liberal ideology, partisan cues potentially inform voters about the
politician's ideological stance. However, partisan cues also
capitalize on the partisan identification of the electorate. Partisan
DOI of original article: http://dx.doi.org/10.1016/j.jcps.2017.07.003.
⁎Corresponding author.
E-mail addresses: aradhna@umich.edu (A. Krishna), t.sokolova@uvt.nl
(T. Sokolova).
http://dx.doi.org/10.1016/j.jcps.2017.07.005
1057-7408/© 2017 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Available online at www.sciencedirect.com
ScienceDirect
Journal of Consumer Psychology 27, 4 (2017) 537 –545
identification, or partisanship, refers to how strongly people
identify with a specific political party; and denotes a long-standing,
affective, psychological link towards that party (e.g. Democrats
or Republicans in the U.S.; Campbell, Converse, Miller, &
Stokes, 1960). Holding the ideology of a candidate fixed, partisan
voters are more likely to support that candidate if he belongs
to their party (Bankert, Huddy, & Rosema, 2017; Hawkins &
Nosek, 2012).
To further illustrate the difference between political
ideology and partisanship, imagine that Peter is a conservative
who identifies strongly with Republicans, whereas Paul is a
conservative who does not think of himself as Democrat or
Republican, or affiliated strongly with any other party. In this
case, Peter and Paul have similar ideologies, but whereas Peter
is strongly partisan (Republican), Paul is non-Partisan. As such,
Paul may feel less compelled (compared to Peter) to support a
Republican candidate who holds liberal values at odds with his
own conservative ideology.
The target article (Jost, 2017 –this issue) will inspire and
enable consumer psychologists to do more work in the political
domain. To facilitate this goal further, we complement the
target article by focusing on partisanship and its role in political
judgments and decisions. In reviewing the literature on
partisanship, we zoom in on voting behaviors and political
attitudes, areas not discussed by Jost, but of great importance
for consumer psychologists given the large expenditures on
political advertising
1
–e.g., Hillary Clinton's campaign spent
$211.4 million on television advertising between June and
October 2016 alone.
Marketing scholars have already started working in the
domain of political persuasion (Adaval, Isbell, & Wyer, 2007;
Ahluwalia, 2000; Hedgcock, Rao, & Chen, 2009; Kim, Rao, &
Lee, 2008; Klein & Ahluwalia, 2005). We have as well: we
studied why the polls went wrong in the 2016 U.S. election
(Krishna, 2016), and examined how people make voting
decisions when they dislike presidential candidates (Sokolova &
Krishna, 2017). Yet, there remains a large scope for research by
consumer psychologists in the area of political decision-making
and political persuasion.
We start by discussing how partisanship impacts voting
behaviors and political attitudes, and why it does so. We then
add to Jost's discussion of the association between political
ideology and processing style, by examining the interplay be-
tween political ideology, partisanship, and systematic process-
ing. We conclude with a discussion of research directions
stemming from this dialogue.
Partisanship and voting behaviors
Research accumulated over more than five decades shows
that partisanship influences voting by affecting voter turnouts
and decisions between specific candidates (Campbell et al.,
1960; Hawkins & Nosek, 2012; Petersen, Skov, Serritzlew, &
Ramsøy, 2013; Schaffner & Streb, 2002). Similar to research
on political ideology summarized in the target article, analyses
of partisanship and voting behaviors utilized both self-reports
and actual voting data, obtaining similar results across the two
data types (Bartels, 2000; Miller, 1991; Moore, 2004; Schaffner
& Streb, 2002; Schaffner, Streb, & Wright, 2001; Sen, 2017).
Below we discuss these findings in detail.
Voter turnout
Higher voter turnout
Partisanship can increase voter turnout in multiple ways.
First, partisanship is rooted in group attachment, or group
identification (Binning, Sherman, Cohen, & Heitland, 2010;
Campbell et al., 1960; Dickerson & Ondercin, 2017; Greene,
1999; Petersen et al., 2013). Research suggests that group
identification can serve as a powerful motivator to act in line
with the interests and expectations of the group (Goldstein,
Cialdini, & Griskevicius, 2008; Terry & Hogg, 1996).
Following this logic, partisanship, as a form of group
identification, can stimulate voting because casting a vote for
one's party provides a clear benefit for the group.
Second, partisanship is associated with reduced decision
difficulty. It provides a mental shortcut for making voting
decisions: by merely looking at candidates' party affiliations,
partisan voters get information about the alignment of the
candidates' program with their values and interests (Bullock,
2011; Gant & Luttbeg, 1987; Lau & Redlawsk, 2001; Mérola &
Hitt, 2015; Rahn, 1993). Additionally, partisan labels make the
candidates more discriminable in the eyes of the public (Heit &
Rubinstein, 1994; Mogilner, Rudnick, & Iyengar, 2008;
Sloutsky, 2003). Consumer psychologists have shown that
low decision difficulty and high option discriminability both
lead to lower decision deferral rates (Dhar, 1996, 1997;
Mogilner et al., 2008). Consequently, we could expect that
partisanship, by virtue of reducing voting decision difficulty
and increasing candidate discriminability, should reduce voting
deferral and increase voter turnout.
Several studies support this reasoning. Schaffner and Streb
(2002) report that people were more likely to express vote
preferences in a survey when vote-choice questions provided
party labels, compared to when they did not, and the effect was
especially pronounced among less educated respondents. This
pattern also emerges in actual voting. Schaffner et al. (2001)
examined real election data and found that voter turnout was
suppressed in non-partisan elections in the U.S. For example,
voter turnout went down following the switch from partisan to
non-partisan elections (i.e. having vs. not having candidates'
party affiliations on the ballot) in Asheville (NC) in the 1990's,
and went up following the switch from non-partisan to partisan
elections in Minnesota in the 1970's. In sum, partisanship
affects voting behavior by mobilizing citizens to exercise their
right to vote.
Voting decisions
Diagnostic cue
In addition to mobilizing voters, partisanship can potentially
improve voters' decisions because candidates' party affiliations
1
https://www.fec.gov/data/
538 A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
carry information about their positions on policy issues (Gant &
Luttbeg, 1987; Lau & Redlawsk, 2001; Mérola & Hitt, 2015;
Rahn, 1993). When it comes to making decisions in the voting
booth, voters may no longer recall each candidate's policy
stances. However, they can use candidates' party affiliations to
infer which candidate would best serve their interests. Analyses
of U.S. presidential election data for the period 1952–1980
(Miller, 1991) and presidential and congressional election data
for 1952–1996 (Bartels, 2000) suggest that candidates' party
affiliations indeed support voting decisions: the number of
voters who identified themselves as Republicans or Democrats
in a given electoral cycle is a significant predictor of electoral
outcomes. A more recent Gallup survey conceptually replicates
these results using self-reported data on voting decisions
between Bush and Kerry in the 2004 presidential election
(Moore, 2004). Although partisanship was not the strongest
determinant of choice between Bush and Kerry, it was as
influential in predicting voters' decisions as the candidate's
perceived ability to manage the government effectively and his
perceived honesty and trustworthiness.
Smaller role for non-diagnostic cues
Another reason why partisanship can improve political
decisions is its effect on the use of non-diagnostic information.
When partisan cues are removed, voters tend to switch to other,
often less diagnostic cues, including candidates' incumbency
status (i.e., whether a given candidate currently holds the
position being voted on) and demographic characteristics, to
decide between political candidates. Using real election data,
Schaffner et al. (2001) show that non-partisan elections provide
significant incumbency advantages, meaning that the candidate
currently holding office is more likely to be re-elected when
his or her party affiliation is not mentioned on the ballot.
Demographic factors, such as race and gender, also gain
prominence when people cannot rely on party cues (Kam,
2007; Sen, 2017). Kam (2007) reports that people with positive
attitudes towards Hispanics are more likely to support a
Hispanic candidate when his or her party affiliation is absent
(vs. present). Similarly, Sen (2017) demonstrates that Demo-
crats become more likely to support an African American or a
female Supreme Court candidate, whereas Republicans become
less likely to support female candidates, when the candidates'
party affiliations are not disclosed.
Finally, candidate emotionality can affect voter judgments in
the absence of party information. Stroud, Glaser, and Salovey
(2005) asked study participants to evaluate a fictitious can-
didate based on a video of his speech. For half of the
participants, the candidate displayed a range of emotions in
the video and for another half, the candidate showed little
emotion. When the candidate was labeled as a Democrat or a
Republican, his emotionality had little effect on participant
evaluations. However, in the absence of such labels, partici-
pants were more likely to vote for the candidate, liked him
more, and perceived him as more competent when the
candidate was more emotionally expressive. Taken together,
the above findings indicate that in the absence of partisan cues,
instead of relying on objective facts, people switch to less
diagnostic cues, such as incumbency and emotionality of the
candidates.
Biasing effects of partisanship
Thus far we have been focusing on the positive facets of
partisanship: the effect on voter mobilization, the diagnostic
value of partisan cues, and their role in attenuating the effects of
less diagnostic cues in political judgments. We now move on to
the potentially biasing effects of partisanship. Specifically, we
discuss how objectively identical information can be perceived
differently depending on whether it is associated with partisans'
own (vs. opposing) party.
Policy support
First, people evaluate policy depending on how strongly
they identify with the party proposing the policy (Bergan, 2012;
Cohen, 2003; Hawkins & Nosek, 2012; Petersen et al., 2013).
For example, Americans who are more likely to identify with
the Democratic party are more likely to support a welfare
policy, regardless of how generous or stringent the policy is, if
the policy has been introduced by a Democrat (vs. Republican),
whereas the opposite is true for those more likely to identify
with the Republican party (Cohen, 2003). Similarly, Danes who
associate more with the left-wing Socialist party are more likely
to support an ethnic integration policy when it is proposed by
socialists (vs. the right-wing People's Party; Petersen et al.,
2013).
Political performance evaluation
Second, partisanship leads people to evaluate the actions of a
given administration differently (Anduiza, Gallego, & Muñoz,
2013; Bartels, 2002; Christenson & Kriner, 2017; Huber, Van
Boven, Park, & Pizzi, 2015; Malhotra & Kuo, 2008). For
instance, those identifying themselves more with Republicans
evaluated Republican George W. Bush's administration's
response to Hurricane Katrina more favorably than those
identifying themselves more with Democrats (Huber et al.,
2015; Malhotra & Kuo, 2008). Similarly, those identifying
themselves more with Republicans (vs. Democrats) held more
positive evaluations of the U.S. economy under Republican
President George W. Bush (Bartels, 2002). At the same time,
Republican (vs. Democrat) partisans were less likely to support
unilateral executive action by the Democratic president Barack
Obama (Christenson & Kriner, 2017); and were less likely to
believe that the budget deficit had decreased under Democratic
president Bill Clinton (Bartels, 2002). The latter result is
especially striking because, unlike questions regarding ethnic
integration or the state of the economy, the question regarding a
change in budget deficit during a given time period has an
objectively correct answer. Yet, partisanship affected judg-
ments for this question as well.
We have discussed several partisanship effects, zooming in on
the ways in which partisanship can influence voting behaviors
and political attitudes. Given the focus of psychologists on
539A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
process, we now turn to the underlying mechanisms driving these
partisanship effects.
Why does partisanship affect voting behavior
and attitudes?
The literature offers two opposing arguments for partisan-
ship effects –a heuristic processing account and a motivated
reasoning account. We discuss these next.
Heuristic processing account
On the one hand, partisan judgments could emerge from
heuristic processing. Similar to presenter likeability (Chaiken,
1980) or physical appearance (Alter, Oppenheimer, Epley, &
Eyre, 2007), a candidate's party affiliation is a salient cue that
can reduce the need for costly information search for partisan
decision-makers. Instead of carefully evaluating which candi-
dates' program is more aligned with the voters' values and
interests, voters can use the candidates' party affiliations as a
proxy for their respective positions (Aldrich, 1995; Lau &
Redlawsk, 2001; Rahn, 1993; Schaffner & Streb, 2002). Policy
and economic evaluation data (Bartels, 2002; Cohen, 2003) are
somewhat aligned with the heuristic processing account of
partisanship. For instance, Cohen (2003) shows that even when
participants were provided with the specific terms of a welfare
policy, they evaluated it based on whether it was favored by
Democrats (vs. Republicans), instead of focusing on the policy
content. Similarly, 1990–1992 American National Election
Studies data indicate that evaluations of the G.W. Bush
administration showed significant partisanship effects even
among well-informed respondents (Bartels, 2002). Thus, even
when more diagnostic data (e.g. policy descriptions, state of the
economy under G.W. Bush) were available, people based their
judgments on partisan cues, effectively reducing the cognitive
costs of decision-making.
Although the notion of partisanship as an effort-minimizing
heuristic is intuitively appealing, most convincing empirical
support for the heuristic account comes from studies examining
the role of systematic processing in partisanship. If partisan
judgments are a result of cognitive effort minimization,
prompting more effortful systematic processing should attenu-
ate the effects of partisanship. In line with this notion, factors
linked to systematic processing, such as motivation and ability
to process information, reduce the effects of partisanship on
judgment (Mérola & Hitt, 2015; Mullinix, 2016; Prior, Sood, &
Khanna, 2015). Below we discuss these findings in detail.
Motivation and ability to process information
Partisan cues become less influential when people are
motivated to process additional information about the target of
judgment (Mullinix, 2016; Prior et al., 2015). When given a
monetary incentive to be accurate, people rely less on the match
between their own party affiliation and that of the current
presidential administration when assessing the current econom-
ic climate (Prior et al., 2015). Similarly, when motivated by the
personal relevance of a given issue, people are less likely to rely
on partisan cues when evaluating policy (Mullinix, 2016). By
the same token, partisan cues become less influential when
ability to process non-partisan information is greater (Mérola &
Hitt, 2015): as such, highly numerate people are more likely to
focus on the numerical information supporting a policy, versus
on which party is presenting that information.
In contrast, when systematic processing is reduced, partisan
cues are more likely to affect judgments. For instance, angry
(vs. control) Republicans become more lenient in evaluating a
Republican administration (Huber et al., 2015). This happens
because anger, an emotion associated with certainty, reduces
systematic processing and increases the impact of cognitive
shortcuts in judgment (Bodenhausen, Sheppard, & Susser, 1994;
Rydell et al., 2008). Consequently, partisanship is more likely to
affect judgments of angry (vs. control) decision-makers.
Task type
Task type (rejection vs. choice) is another factor affecting
systematic processing (Sokolova & Krishna, 2016) and, thus,
the role of partisanship in judgment. When making decisions,
people can reject the less attractive alternatives (e.g., selecting
which candidate they would rather not have as a president) or
choose the more attractive ones (e.g., selecting which candidate
they would rather have as a president). Rejection triggers the
consideration of loss of one (or several) foregone options (Dhar
& Wertenbroch, 2000). In turn, consideration of losses has been
linked to greater visual attention (Hochman & Yechiam, 2011),
and more rational decisions in risky choices (Yechiam &
Hochman, 2013) and price evaluations (Chatterjee, Heath,
Milberg, & France, 2000). With loss considerations being
more prominent in rejection, rejection decisions lead to more
systematic processing compared to choice decisions (as shown
by Sokolova & Krishna, 2016). Applied to the context of
partisan judgments, this implies that rejecting the less attractive
candidate (vs. choosing the more attractive one) may lead
people to become less reliant on partisan cues when voting.
To test this prediction, we asked one group of participants
whether they would reject the candidate not from their party,
and another group of participants whether they would choose
the candidate from their party in the 2016 U.S. presidential
election. Consistent with our predictions, people were less
likely to rely on the candidate's party affiliation in the rejection
(vs. choice) group. Similar to high motivation and ability, a task
framed as rejection –which prompted more systematic
processing –reduced the effect of partisan cues in judgment.
Social and economic factors
The effects of social and economic factors parallel those
produced by motivation, ability, and task type. For example,
analysis of the 1980–2012 American National Election Studies
data indicates that as economic conditions deteriorate, partisan-
ship exerts a weaker effect on judgments about the economy
(Dickerson & Ondercin, 2017). Thinking about a negative
stimulus, such as a bad economy, prompts systematic information
processing (Chatterjee & Heath, 1996; Houston, Sherman, &
Baker, 1991; Malkoc, Hedgcock, & Hoeffler, 2013). As such,
540 A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
when the economy prompts people to rely on systematic
processing, the effect of partisanship diminishes.
In a similar vein, the structure of social networks can affect
systematic processing and the strength of partisanship effects.
Homogenous (vs. heterogeneous) social networks provide little
access to novel information (Aral & Van Alstyne, 2011) and
create little potential for conflict, making systematic processing
less likely (Maheswaran & Chaiken, 1991; Maio, Bell, &
Esses, 1996; Savary, Kleiman, Hassin, & Dhar, 2015). As a
result, homogenous networks should increase the reliance on
partisanship. In line with this logic, people supporting Barack
Obama (George Bush) supported him even more after a 15-min
discussion with a few like-minded others (Keating, Van Boven,
& Judd, 2016). In contrast, discussing issues with dissimilar
others reduces the effect of partisanship in attitude formation
(Lupton, Singh, & Thornton, 2015).
Motivated reasoning account
On the other hand, partisan judgments could be a product of
effortful processing, wherein people affiliated with a given
party engage in motivated reasoning to reach the desired
conclusions (Cohen, 2003; Dickerson & Ondercin, 2017;
Kunda, 1990; Petersen et al., 2013). Similar to the motivated
social cognition view of political ideology (Jost, Glaser,
Kruglanski, & Sulloway, 2003), the motivated reasoning
account of partisanship implies that people may purposefully
rely on partisanship to defend their underlying needs and
interests.
Indeed, counter to what the heuristic-processing view
suggests and in line with the motivated reasoning view,
partisanship is not associated with a lack of interest in or
experience with politics. In fact, those more interested in
elections and political parties and more knowledgeable about
candidates' party affiliations are more likely to be biased by
partisanship, compared to their less interested and less
knowledgeable counterparts (Baum, 2005; Lodge & Hamill,
1986; Slothuus & De Vreese, 2010; Strickland, Taber, &
Lodge, 2011). More (vs. less) politically aware voters are less
likely to find the opposing party's candidate likeable, less likely
to cross party lines when voting (Baum, 2005), and more likely
to follow the opinion of their party in policy evaluations
(Slothuus & De Vreese, 2010). Assuming that high political
awareness signals high political involvement, this link between
awareness and partisanship tendencies gives support to the
motivated reasoning account of partisan identification.
Evidence from response-time studies also corroborates the
motivated reasoning account. Petersen et al. (2013) examined
participants' response latencies for party-inconsistent policies
(e.g. a right-wing party proposing that “Public transportation
should be free of charge for the elderly”). To agree with
such policies partisans could merely rely on the party cues
(i.e. heuristic-processing account), or come up with reasons to
support an otherwise disliked policy (i.e. motivated reasoning
account). In the case of heuristic processing, agreeing with a
policy should take less time than disagreeing with it, in the case
of motivated reasoning, the reverse relationship should hold.
The results follow the latter pattern, conforming to the
motivated reasoning account of partisanship.
Thus, although one may jump to an immediate conclusion
that partisanship effects are driven by effort-minimizing
heuristic processing, research suggests that they may in part
be a result of effortful motivated reasoning. As such, it appears
that both accounts –heuristic processing and motivated
reasoning –may explain the use of partisan cues.
Political ideology, partisanship, and systematic processing
In the previous section, we have elaborated upon the
relationship between the use of partisan cues and systematic
processing. We argue that when systematic processing is
facilitated, the effect of partisan cues is reduced (Dickerson &
Ondercin, 2017; Huber et al., 2015; Lupton et al., 2015; Mérola
& Hitt, 2015; Mullinix, 2016; Prior et al., 2015; Sokolova &
Krishna, 2017). Building on Jost's article, we now expand on
the interplay between systematic processing, political ideology,
and partisanship in political decision-making.
Jost (2017) suggests that liberal ideology is positively
associated with systematic processing. Evidence from over 100
empirical tests shows that liberalism is correlated with higher
cognitive reflection, need for cognition, and integrative
complexity –all of which signal more systematic processing.
In addition to correlational evidence, Jost cites studies
supporting a causal link between systematic processing and
liberal ideology (Eidelman, Crandall, Goodman, & Blanchar,
2012; Hansson, Keating, & Terry, 1974; Thorisdottir & Jost,
2011; Van Berkel, Crandall, Eidelman, & Blanchar, 2015),
meaning that systematic processing is not only associated with
liberal ideology, but can also drive it. Table 1 summarizes
findings on the association between systematic processing and
liberalism; it then outlines the links between systematic
processing and partisanship discussed in the previous section.
Relating political ideology to the use of partisanship cues
So, how do these two variables impact one another? Based
on these literatures, we would hypothesize that liberals should
be less prone to fall back on partisan cues in their political
decisions (e.g. vote for Democrats), compared to conservatives.
In fact, recent trends in political ideology and voting decisions
reported by Gallup support this reasoning. Whereas the
proportion of liberal Americans has been steadily increasing
over the last 10 years (from 19% in 2004 to 24% in 2015),
2
the
number of Democratic seats in congress has, in fact, gone down
during the same period (from 48 in 2004 to 44 in 2015).
3
Relatedly, if systematic processing reduces the weight of
partisanship, it can increase the relative weight of political
ideology (liberal or conservative) in political decision-making.
Consider a scenario where voter A is deciding whether to vote
for a liberal Republican or a conservative Democrat. Normally,
2
http://www.gallup.com/poll/201152/conservative-liberal-gap-continues-
narrow-tuesday.aspx
3
https://www.senate.gov/history/partydiv.htm
541A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
voter A will base her decisions on the party affiliation (i.e.
Republican vs. Democrat) and political ideology (i.e. liberal vs.
conservative) of the candidates –giving some non-zero weight
to the two attributes (e.g. w
party affiliation
=x;w
political ideology
=
1−x). In contrast, under systematic processing, she will focus
less on the candidates' party affiliation, thus giving greater rela-
tive weight to the candidates' ideologies (e.g. w
party affiliation
=
x−y; w
political ideology
=1−x + y). In line with this reasoning,
Mullinix (2016) demonstrates that personal values trump
partisanship when people make decisions about more (vs. less)
important issues (and, thus, should be more likely to rely on
systematic processing).
Taken together, studies of the effect of systematic process-
ing related to ideology, and related to partisanship, suggest that
systematic processing plays a dual role in political decision-
making: by directly affecting which ideological values people
will rely on, but also by affecting how much people will rely on
these values (vs. partisanship) in their decisions. Consider a
moderate Democrat voting on whether to raise property taxes.
The taxes will finance a local school that wants to bus in
students from less wealthy neighborhoods. The raise is
proposed by a Republican politician. With more systematic
processing, the voter will be more liberal in her ideology and be
more likely to support this raise. In addition, the voter will be
less partisan, meaning that her support for the raise will not be
muted by the fact that the raise is proposed by a Republican.
Similarly, if the policy is proposed by a Democrat, the voter
will not be more likely to support the raise just because the raise
is proposed by a member of the voter's own party. Thus, under
systematic processing the decision to support a policy will
depend less on which party proposes it and more on whether it
is liberal (vs. conservative).
Fig. 1 provides a schematic representation of the dual role of
systematic processing in political decisions. Of course, much
more research needs to be done on this model.
Conclusion
There are several takeaways for consumer behavior re-
searchers from the political ideology and partisanship literatures
discussed in the current dialogue. First, these literatures provide
insight into the mechanisms underlying voting. Given the
multimillion dollar expenditures on political advertising, con-
sumer psychologists can benefit from better understanding how
voting decisions are made and, more specifically, how political
advertising affects these decisions. As stated earlier, between
June and October 2016, Hillary Clinton's campaign spent
$211.4 million on television advertising and Donald Trump's
campaign spent $74.0 million. Over a similar period in 2012,
Barack Obama and Mitt Romney spent $241.5 M and $158.8 M,
Table 1
Political ideology, partisanship, and systematic processing.
Political ideology and systematic processing
Need for cognition Jost, 2017 Higher need for cognition among liberals
Cognitive reflection Higher performance on cognitive reflection test among liberals
Integrative complexity More complex speech preferences among liberals
Inhibition of systematic processing Alcohol intoxication, cognitive load, time pressure, anxiety lead to
more conservative/less liberal views
Partisanship and systematic processing
Motivation Mullinix, 2016; Prior et al., 2015 Monetary incentives and personal relevance reduce the effect of partisan cues
Ability Huber et al., 2015; Mérola & Hitt, 2015 Numeracy reduces the effect of partisan cues;
Anger increases the effect
Task type Sokolova & Krishna, 2017 Rejection (vs. choice) decisions reduce the effect of partisan cues
Economic factors Dickerson & Ondercin, 2017 Worsening economic conditions reduce the effect of partisan cues
Social factors Keating et al., 2016; Lupton et al., 2015 Homogenous social networks increase partisanship
Fig. 1. Political ideology, partisanship, and systematic processing.
542 A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
respectively, on television advertising (Goldstein, McCormick, &
Tartar, 2016). It is therefore not surprising that marketing
researchers have started analyzing the mechanisms of political
persuasion (Adaval et al., 2007; Ahluwalia, 2000; Hedgcock
et al., 2009; Kim et al., 2008; Klein & Ahluwalia, 2005; Krishna,
2016; Sokolova & Krishna, 2017). However, there is much more
room for research by consumer psychologists in the political
domain –examining the impact of political ideologies and of
partisanship provide two such opportunities.
Second, the current dialogue highlights the role of
micro-level factors (e.g. ideology, motivation, personal rele-
vance), as well as more macro-level factors (e.g. social network
structures, state of the economy) in shaping individual
cognitive processes and decisions in the political context.
Although the effects of micro-level factors have been studied
extensively in consumer research, the effects of macro-level
factors on consumers have received relatively little attention
and, thus, present a promising research avenue.
Third, this article touches upon the debate on whether
partisanship is driven by the low-effort heuristic processing or
by effortful motivated reasoning (Cohen, 2003; Petersen,
Giessing, & Nielsen, 2015; Petersen et al., 2013), and illustrates
how response time data can be used to disentangle the two
mechanisms (Petersen et al., 2013). Consumer research often
uses these mechanisms to explain consumer perceptions and
behavior, yet it rarely pits these against one another. For instance,
heuristic processing has been used to explain, among others,
default option effects (Johnson, Bellman, & Lohse, 2002),
numerical anchoring (Frederick, Kahneman, & Mochon, 2010),
alphanumeric brand evaluations (Gunasti & Ross, 2010), and
spatial perception biases (Krider, Raghubir, & Krishna, 2001;
Raghubir & Krishna, 1996). Motivated reasoning has been used
to explain unhealthy eating behaviors (Hagen, Krishna, &
McFerran, 2016), preferences between hedonic and utilitarian
goods (Okada, 2005), outcome-biased product evaluations
(Agrawal & Maheswaran, 2005), and consumer responses to
negative publicity (Ahluwalia, Burnkrant, & Unnava, 2000).
More research disentangling and comparing the relative strength
of heuristics and motivated reasoning in decision-making would
enable a better understanding of the psychological processes
driving consumer decisions.
More broadly speaking there are (at least) two ways in
which this dialogue can generate additional research by
consumer psychologists –using learnings from the political
arena within a consumer behavior context, and using learnings
from consumer psychology in a political context. We hope that
it does both.
References
Adaval, R., Isbell, L. M., & Wyer, R. S. (2007). The impact of pictures on
narrative-and list-based impression formation: A process interference
model. Journal of Experimental Social Psychology,43(3), 352–364.
Agrawal, N., & Maheswaran, D. (2005). Motivated reasoning in outcome-bias
effects. Journal of Consumer Research,31(4), 798–805.
Ahluwalia, R. (2000). Examination of psychological processes underlying
resistance to persuasion. Journal of Consumer Research,27(2), 217–232.
Ahluwalia, R., Burnkrant, R. E., & Unnava, H. R. (2000). Consumer response
to negative publicity: The moderating role of commitment. Journal of
Marketing Research,37(2), 203–214.
Aldrich, J. H. (1995). Why parties?: The origin and transformation of political
parties in America. University of Chicago Press.
Alter, A. L., Oppenheimer, D. M., Epley, N., & Eyre, R. N. (2007).
Overcoming intuition: Metacognitive difficulty activates analytic reasoning.
Journal of Experimental Psychology: General,136(4), 569.
Anduiza, E., Gallego, A., & Muñoz, J. (2013). Turning a blind eye:
Experimental evidence of partisan bias in attitudes toward corruption.
Comparative Political Studies,46(12), 1664–1692.
Aral, S., & Van Alstyne, M. (2011). The diversity-bandwidth trade-off.
American Journal of Sociology,117(1), 90–171.
Bankert, A., Huddy, L., & Rosema, M. (2017). Measuring partisanship as
a social identity in multi-party systems. Political Behavior,39(1),
103–132.
Bartels, L. M. (2000). Partisanship and voting behavior, 1952–1996. American
Journal of Political Science,35–50.
Bartels, L. M. (2002). Beyond the running tally: Partisan bias in political
perceptions. Political Behavior,24(2), 117–150.
Baum, M. A. (2005). Talking the vote: Why presidential candidates hit the talk
show circuit. American Journal of Political Science,49(2), 213–234.
Bergan, D. E. (2012). Partisan stereotypes and policy attitudes. Journal of
Communication,62(6), 1102–1120.
Binning, K. R., Sherman, D. K., Cohen, G. L., & Heitland, K. (2010). Seeing
the other side: Reducing political partisanship via self-affirmation in the
2008 presidential election. Analyses of Social Issues and Public Policy,
10(1), 276–292.
Bodenhausen, G. V., Sheppard, L. A., & Susser, K. (1994). Negative affect in
social judgment: The differential impact of anger and sadness. European
Journal of Social Psychology,24(1), 45–62.
Bullock, J. G. (2011). Elite influence on public opinion in an informed
electorate. American Political Science Review,105(3), 496–515.
Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The
American voter. New York: John Wiley & Sons.
Chaiken, S. (1980). Heuristic versus systematic information processing and the
use of source versus message cues in persuasion. Journal of Personality and
Social Psychology,39(5), 752.
Chatterjee, S., & Heath, T. B. (1996). Conflict and loss aversion in
multiattribute choice: The effects of trade-off size and reference dependence
on decision difficulty. Organizational Behavior and Human Decision
Processes,67(2), 144–155.
Chatterjee, S., Heath, T. B., Milberg, S. J., & France, K. R. (2000). The
differential processing of price in gains and losses: The effects of frame
and need for cognition. Journal of Behavioral Decision Making,13(1),
61–75.
Christenson, D. P., & Kriner, D. L. (2017). Constitutional qualms or politics as
usual? The factors shaping public support for unilateral action. American
Journal of Political Science,61(2), 335–349.
Cohen, G. L. (2003). Party over policy: The dominating impact of group
influence on political beliefs. Journal of Personality and Social Psychology,
85(5), 808.
Dhar, R. (1996). The effect of decision strategy on deciding to defer choice.
Journal of Behavioral Decision Making,9(4), 265–281.
Dhar, R. (1997). Consumer preference for a no-choice option. Journal of
Consumer Research,24(2), 215–231.
Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and
utilitarian goods. Journal of Marketing Research,37(1), 60–71.
Dickerson, B. T., & Ondercin, H. L. (2017). Conditional motivated reasoning:
How the local economy moderates partisan motivations in economic
perceptions. Political Research Quarterly,70(1), 194–208.
Eidelman, S., Crandall, C. S., Goodman, J. A., & Blanchar, J. C. (2012). Low-
effort thought promotes political conservatism. Personality and Social
Psychology Bulletin,38, 808–820.
Frederick, S., Kahneman, D., & Mochon, D. (2010). Elaborating a simpler
theory of anchoring. Journal of Consumer Psychology,20(1), 17–19.
Gant, M. M., & Luttbeg, N. R. (1987). The cognitive utility of partisanship.
Western Political Quarterly,40(3), 499–517.
543A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a
viewpoint: Using social norms to motivate environmental conservation in
hotels. Journal of Consumer Research,35(3), 472–482.
Goldstein, K., McCormick, J., & Tartar, A. (2016). Candidates make last ditch
ad spending push across 14-state electoral map. https://www.bloomberg.
com/politics/graphics/2016-presidential-campaign-tv-ads/.
Greene, S. (1999). Understanding party identification: A social identity
approach. Political Psychology,20(2), 393–403.
Gunasti, K., & Ross, W. T., Jr. (2010). How and when alphanumeric brand
names affect consumer preferences. Journal of Marketing Research,47(6),
1177–1192.
Hagen, L., Krishna, A., & McFerran, B. (2016). Rejecting responsibility: Low
physical involvement in obtaining food promotes unhealthy eating. Journal
of Marketing Research.http://dx.doi.org/10.1509/jmr.14.0125.
Hansson, R. O., Keating, J. P., & Terry, C. (1974). The effects of mandatory
time limits in the voting booth on liberal-conservative voting patterns.
Journal of Applied Social Psychology,4, 336–342.
Hawkins, C. B., & Nosek, B. A. (2012). Motivated independence? Implicit
party identity predicts political judgments among self-proclaimed
independents. Personality and Social Psychology Bulletin,38(11),
1437–1452.
Hedgcock, W., Rao, A. R., & Chen, H. (2009). Could Ralph Nader's entrance
and exit have helped Al Gore? The impact of decoy dynamics on consumer
choice. Journal of Marketing Research,46(3), 330–343.
Heit, E., & Rubinstein, J. (1994). Similarity and property effects in inductive
reasoning. Journal of Experimental Psychology: Learning, Memory, and
Cognition,20(2), 411.
Hochman, G., & Yechiam, E. (2011). Loss aversion in the eye and in the heart:
The autonomic nervous system's responses to losses. Journal of Behavioral
Decision Making,24(2), 140–156.
Houston, D. A., Sherman, S. J., & Baker, S. M. (1991). Feature matching,
unique features, and the dynamics of the choice process: Predecision
conflict and postdecision satisfaction. Journal of Experimental Social
Psychology,27(5), 411–430.
Huber, M., Van Boven, L., Park, B., & Pizzi, W. T. (2015). Seeing red: Anger
increases how much republican identification predicts partisan attitudes and
perceived polarization. PLoS One,10(9), e0139193.
Johnson, E. J., Bellman, S., & Lohse, G. L. (2002). Defaults, framing and
privacy: Why opting in-opting out. Marketing Letters,13(1), 5–15.
Jost, J. T. (2017). The marketplace of ideology: “Elective Affinities”in political
psychology and their implications for consumer behavior. Journal of
Consumer Psychology,27(4), 502–520.
Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. J. (2003). Political
conservatism as motivated social cognition. Psychological Bulletin,129,
339–375.
Kam, C. D. (2007). Implicit attitudes, explicit choices: When subliminal
priming predicts candidate preference. Political Behavior,29(3), 343–367.
Keating, J., Van Boven, L., & Judd, C. M. (2016). Partisan underestimation of
the polarizing influence of group discussion. Journal of Experimental
Social Psychology,65,52–58.
Kim, H., Rao, A. R., & Lee, A. Y. (2008). It's time to vote: The effect of
matching message orientation and temporal frame on political persuasion.
Journal of Consumer Research,35(6), 877–889.
Klein, J. G., & Ahluwalia, R. (2005). Negativity in the evaluation of political
candidates. Journal of Marketing,69(1), 131–142.
Krider, R. E., Raghubir, P., & Krishna, A. (2001). Pizzas: πor square?
Psychophysical biases in area comparisons. Marketing Science,20(4),
405–425.
Krishna, A. (2016). Voters' embarrassment and fear of social stigma messed
with pollsters' predictions. https://theconversation.com/voters-embarrassment-
and-fear-of-social-stigma-messed-with-pollsters-predictions-68640.
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin,
108(3), 480.
Lau, R. R., & Redlawsk, D. P. (2001). Advantages and disadvantages of
cognitive heuristics in political decision making. American Journal of
Political Science, 951–971.
Lodge, M., & Hamill, R. (1986). A partisan schema for political information
processing. American Political Science Review,80(2), 505–519.
Lupton, R. N., Singh, S. P., & Thornton, J. R. (2015). The moderating impact of
social networks on the relationships among core values, partisanship, and
candidate evaluations. Political Psychology,36(4), 399–414.
Maheswaran, D., & Chaiken, S. (1991). Promoting systematic processing in
low-motivation settings: Effect of incongruent information on processing
and judgment. Journal of Personality and Social Psychology,61(1), 13.
Maio, G. R., Bell, D. W., & Esses, V. M. (1996). Ambivalence and persuasion:
The processing of messages about immigrant groups. Journal of
Experimental Social Psychology,32(6), 513–536.
Malhotra, N., & Kuo, A. G. (2008). Attributing blame: The public's response to
Hurricane Katrina. The Journal of Politics,70(1), 120–135.
Malkoc, S. A., Hedgcock, W., & Hoeffler, S. (2013). Between a rock and a hard
place: The failure of the attraction effect among unattractive alternatives.
Journal of Consumer Psychology,23(3), 317–329.
Mérola, V., & Hitt, M. P. (2015). Numeracy and the persuasive effect of policy
information and party cues. Public Opinion Quarterly,80(2), 554–562.
Miller, W. E. (1991). Party identification, realignment, and party voting: Back
to the basics. American Political Science Review,85(2), 557–568.
Mogilner, C., Rudnick, T., & Iyengar, S. S. (2008). The mere categorization
effect: How the presence of categories increases choosers' perceptions of
assortment variety and outcome satisfaction. Journal of Consumer
Research,35(2), 202–215.
Moore, D. (2004). “Values”seen as most important characteristic of presidential
candidates. http://www.gallup.com/poll/12544/values-seen-most-important-
characteristic-presidential-candidates.aspx.
Mullinix, K. J. (2016). Partisanship and preference formation: Competing
motivations, elite polarization, and issue importance. Political Behavior,
38(2), 383–411.
Okada, E. M. (2005). Justification effects on consumer choice of hedonic and
utilitarian goods. Journal of Marketing Research,42(1), 43–53.
Petersen, M. B., Giessing, A., & Nielsen, J. (2015). Physiological responses and
partisan bias: Beyond self-reported measures of party identification. PLoS
One,10(5), e0126922.
Petersen, M. B., Skov, M., Serritzlew, S., & Ramsøy, T. (2013). Motivated
reasoning and political parties: Evidence for increased processing in the face
of party cues. Political Behavior,35(4), 831–854.
Prior, M., Sood, G., & Khanna, K. (2015). You cannot be serious: The impact
of accuracy incentives on partisan bias in reports of economic perceptions.
Quarterly Journal of Political Science,10(4), 489–518.
Raghubir, P., & Krishna, A. (1996). As the crow flies: Bias in consumers' map-
based distance judgments. Journal of Consumer Research,23(1), 26–39.
Rahn, W. M. (1993). The role of partisan stereotypes in information processing
about political candidates. American Journal of Political Science, 472–496.
Rydell, R. J., Mackie, D. M., Maitner, A. T., Claypool, H. M., Ryan, M. J., &
Smith, E. R. (2008). Arousal, processing, and risk taking: Consequences of
intergroup anger. Personality and Social Psychology Bulletin,34(8),
1141–1152.
Savary, J., Kleiman, T., Hassin, R. R., & Dhar, R. (2015). Positive
consequences of conflict on decision making: When a conflict mindset
facilitates choice. Journal of Experimental Psychology: General,144(1), 1.
Schaffner, B. F., & Streb, M. J. (2002). The partisan heuristic in low-
information elections. Public Opinion Quarterly,66(4), 559–581.
Schaffner, B. F., Streb, M., & Wright, G. (2001). Teams without uniforms: The
nonpartisan ballot in state and local elections. Political Research Quarterly,
54(1), 7–30.
Sen, M. (2017). How political signals affect public support for judicial
nominations: Evidence from a conjoint experiment. Political Research
Quarterly,70(2), 374–393.
Slothuus, R., & De Vreese, C. H. (2010). Political parties, motivated reasoning,
and issue framing effects. The Journal of Politics,72(3), 630–645.
Sloutsky, V. M. (2003). The role of similarity in the development of
categorization. Trends in Cognitive Sciences,7(6), 246–251.
Sokolova, T., & Krishna, A. (2016). Take it or leave it: How choosing versus
rejecting alternatives affects information processing. Journal of Consumer
Research,43(4), 614–635.
Sokolova, T., & Krishna, A. (2017). The effect of consideration set
unattractiveness on decision-making: A study of the U.S. presidential
elections. Working paper.
544 A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545
Strickland, A. A., Taber, C. S., & Lodge, M. (2011). Motivated reasoning
and public opinion. Journal of Health Politics, Policy and Law,36(6),
935–944.
Stroud, L. R., Glaser, J., & Salovey, P. (2005). The effects of partisanship and
candidate emotionality on voter preference. Imagination, Cognition and
Personality,25(1), 25–44.
Terry, D. J., & Hogg, M. A. (1996). Group norms and the attitude-behavior
relationship: A role for group identification. Personality and Social
Psychology Bulletin,22(8), 776–793.
Thorisdottir, H., & Jost, J. T. (2011). Motivated closed-mindedness mediates
the effect of threat on political conservatism. Political Psychology,32,
785–811.
Van Berkel, L., Crandall, C. S., Eidelman, S., & Blanchar, J. C. (2015).
Hierarchy, dominance, and deliberation: Egalitarian values require mental
effort. Personality and Social Psychology Bulletin,41, 1207–1222.
Yechiam, E., & Hochman, G. (2013). Loss-aversion or loss-attention: The
impact of losses on cognitive performance. Cognitive Psychology,66(2),
212–231.
545A. Krishna, T. Sokolova / Journal of Consumer Psychology 27, 4 (2017) 537–545