Available online at www.sciencedirect.com
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Journal of Consumer Psychology 26, 1 (2016) 98 – 104
Research Report
When perfectionism leads to imperfect consumer choices: The role of
dichotomous thinking☆
Xin He ⁎
Department of Marketing, College of Business Administration, University of Central Florida, Orlando, FL 32816-1400, USA
Accepted by Cornelia Pechmann, Editor; Associate Editor, Steve Posavac
Received 1 August 2013; received in revised form 7 April 2015; accepted 8 April 2015
Available online 12 April 2015
Abstract
In four studies, this research investigates the role of perfectionism in consumer decision making and demonstrates that perfectionists often make
inferior decisions when facing difficult tasks. Although perfectionists outperform those with low need for perfection at medium levels of decision
difficulty, their advantages disappear at high levels of decision difficulty. Driven by dichotomous thinking, perfectionists give up on the task when
they realize that a perfect outcome is no longer possible and make inferior decisions. Paradoxically, when given the opportunity to select their own
task, perfectionists sometimes avoid tasks over which they have comparative advantage but prefer tasks of high complexity, without realizing the
effect of dichotomous thinking on subsequent choices.
© 2015 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
Keywords: Perfectionism; Decision difficulty; Decision accuracy; Task choice
Introduction
It is only natural that people strive for the best and dream of
attaining perfection. Perfection is a virtue promoted in today's
value system and respected in society at large (Carey, 2007). It is
even reflected in popular movies, such as A Perfect World
(Barron, 1995). After all, what could possibly be wrong with
trying to be perfect? This article attempts to answer this question.
Although perfectionism can potentially influence a wide range of
decisions, little research has addressed its implications in the
consumer domain. Two important exceptions in marketing
literature include the works of Wooten (2000), who highlighted
the role of consumer perfectionism in gift-giving anxiety, and
Kopalle and Lehmann (2001), who demonstrated that perfectionism often results in a high level of consumer expectation.
☆ This research is partially supported by the In-House Research Grant
(#13219007) awarded by the Office of Research and Commercialization and a
matching grant awarded by the College of Business Administration, University of
Central Florida.
⁎ Fax: +1 407 823 3891.
E-mail address: [email protected].
The purpose of this research is to examine systematically the
effect of perfectionism on consumer decision making. Across
four studies, perfectionists are shown to make imperfect decisions
when facing difficult tasks and this boomerang effect of
perfectionism is driven by a unique mechanism—dichotomous
thinking (Beck, Freeman, & Associates, 1990). Furthermore, this
research reveals a dilemma in perfectionists' task selection—they
sometimes choose complex tasks although they perform poorly
in such tasks.
Perfectionism and consumer decision making
Perfectionism
Hollender (1965, p. 94) described perfectionism as “the
practice of demanding of oneself or others a higher quality of
performance than is required by the situation.” More recently,
Wooten (2000, p. 90) defined perfectionism as people's tendency
“to set extremely high standards for themselves and be displeased
with anything less.” Given their high standards, it is not surprising
that perfectionists are driven to achieve the best performance.
http://dx.doi.org/10.1016/j.jcps.2015.04.002
1057-7408/© 2015 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
Hewitt and Flett (1991) argued that perfectionism can be
motivational; that is, people with high need for perfection often
expend extra effort to prevent mistakes and achieve excellence in
tasks. Empirical evidence has linked perfectionism to motivation
in academic achievement (Miquelon, Vallerand, Grouzet, &
Cardinal, 2005) and to strong commitment to a wide range of
goals in both work and life (Flett, Sawatzky, & Hewitt, 1995).
More recently, Wigert, Reiter-Palmon, Kaufman, and Silvia
(2012) show that perfectionism is positively correlated with
decision quality, though at the expense of originality. Overall,
these findings suggest that perfectionism often results in positive
outcomes through “perfectionist strivings” (Stoeber & Otto, 2006,
p. 296). Because of their high standards and motivation to excel,
perfectionists are expected to make accurate decisions and
superior choices.
Perfectionism is conceptually distinct from other related
constructs, such as maximizing (Schwartz, 2004; Schwartz et al.,
2002), and has a unique psychological property—dichotomous
thinking (Beck, 1976; Beck et al., 1990)—which may ultimately
impede quality decision making. This research examines such
distinctive ways of thinking and the conditions under which
perfectionism may backfire and lead to inferior decisions.
Dichotomous thinking
Dichotomous thinking, also called “black-or-white” thinking, is “the tendency to evaluate experiences in terms of
mutually exclusive categories (e.g., good or bad, success or
failure, trustworthy or deceitful) rather than seeing experiences
as falling along continua” (Beck et al., 1990, p. 187). Such
thinking style is reflected in the comments of a female
consumer in Wooten's (2000, p. 90) study: “Unless I've
found the perfect gift, one that I'm positive they'll like, then I'd
rather not give a gift at all.” Apparently, perfectionists do not
measure their performance on a continuous scale. Instead, their
assessment is based on a binary choice between two extremes,
without any other possibilities in between (Burns, 1980; Shafran,
Cooper, & Fairburn, 2002). Empirical evidence suggests that
perfectionism is correlated with dichotomous and rigid thinking
styles (Egan, Piek, Dyck, & Rees, 2007; Riley & Shafran, 2005)
as well as categorical thinking (Burns & Fedewa, 2005).
Dichotomous thinking is commonly characterized as suboptimal and maladaptive (Beck, 1999; Beck et al., 1990). Although
perfectionists are driven by their high performance standards,
dichotomous thinking, ironically, often prevents them from
achieving those very standards. In their minds, perfection
disappears as soon as they deviate from the rigid standards
(Burns, 1980). As a result, they abandon any further effort
because perfection is no longer possible. Such task abandonment
is consistent with Kopylov's (2012) model of perfectionism and
evidence in education (Enns, Cox, Sareen, & Freeman, 2001) that
perfectionists display task avoidance behavior when perfection
is difficult to achieve. Therefore, perfectionists' performance
depends on the feasibility of achieving perfection. If perfection is
within their reach, they are likely to invest additional time and
effort in the task. This in turn leads to superior performance.
However, if perfection is no longer feasible, perfectionists may
99
quit the task prematurely because of their dichotomous thinking,
which may result in an inferior outcome.
One variable that influences the attainment of perfection is the
difficulty of the decision task. For simple tasks, a minimum
amount of effort should be sufficient to arrive at an optimal
decision. In such cases, consumers with high and low need for
perfection may demonstrate similar decision quality because of a
potential ceiling effect. For tasks in the middle range of decision
difficulty, perfection is still attainable, though additional effort is
required. Perfectionists are likely to invest greater effort in these
tasks because of their high standards and desire to be perfect.
Consequently, they may have higher decision accuracy than
consumers with low need for perfection.
The key question is what would happen when the tasks
become exceedingly difficult. Due to dichotomous thinking,
the positive effect of perfectionism may reverse in such cases.
When facing challenging tasks, perfectionists will experience
increased difficulty in ensuring a perfect outcome even if they
try very hard. Because of dichotomous thinking, perfectionists
may abandon their effort when perfection is no longer feasible
and quit prematurely. As a result, perfectionism may backfire,
and perfectionist consumers may demonstrate lower decision
accuracy than those with low need for perfection. More
formally,
H1. Decision difficulty moderates the effect of need for
perfection on decision accuracy. Specifically, need for perfection
reduces decision accuracy at high decision difficulty.
Study 1: the boomerang effect of perfectionism
Method
Participants were 207 undergraduate business students who
were presented with an online choice task in the laboratory. In
this task, they were looking for a furnished apartment to rent for
the next academic year. Participants were randomly assigned to
the three levels of decision difficulty manipulated through task
complexity (amount of information) (Olshavsky, 1979; Payne,
1976; Payne, Bettman, & Johnson, 1993). In the high-difficulty
condition, participants selected from 12 apartments described in
nine attributes (Appendix A). In the medium-difficulty condition,
participants chose from six apartments described in six attributes.
In the low-difficulty condition, participants saw three apartments
described in three attributes. Participants were told that these
apartments charged similar monthly rents and were all within
their budget.
Decision accuracy is measured as the extent to which the
choice maximizes the expected value (Johnson & Payne, 1985;
Payne et al., 1993). To measure attribute weight, participants
rated the importance of each attribute on a 7-point scale (1 = not
at all important, 7 = very important). The expected value of
each alternative was then calculated using a weighted additive
model, which is commonly employed as a normative benchmark
(Bettman, Luce, & Payne, 1998; Payne et al., 1993). In line with
Johnson and Payne (1985) and Payne, Bettman, and Johnson
(1988), decision accuracy is computed as a continuous variable,
100
X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
in which the expected value of the chosen alternative is evaluated
against the expected values of the random choice and the optimal
choice. Need for perfection was measured using an eight-item
perfectionism scale adapted from Kopalle and Lehmann (2001,
p. 392) (e.g., “I hate being less than the best at things”), with
each item rated on a 7-point scale (Cronbach's α = .87).
Results and discussion
Decision accuracy was subjected to an analysis of variance
(ANOVA), with decision difficulty as a categorical variable and
need for perfection as a continuous variable. Consistent with H1,
results showed a significant need for perfection × decision
difficulty interaction (F(2, 201) = 5.15, p b .01). While need for
perfection was shown to increase decision accuracy at medium
decision difficulty (b = .05, S.E. = .02, t(69) = 3.18, p b .01),
the effect was reversed at high decision difficulty—the higher the
need for perfection, the lower the decision accuracy (b = − .13,
S.E. = .06, t(68) = − 2.10, p b .05). Need for perfection had little
effect at low decision difficulty (p = .58).
Study 1 demonstrates that perfectionism backfires at high
decision difficulty. While this boomerang effect is consistent with
dichotomous thinking (Beck et al., 1990; Shafran et al., 2002),
Study 1 does not directly test this mechanism. If dichotomous
thinking is indeed the underlying process, it should mediate the
observed effects at high decision difficulty. Study 2 is designed to
test this hypothesis.
H2. Dichotomous thinking mediates the effect of need for
perfection on decision accuracy at high decision difficulty.
Study 2: the mediating role of dichotomous thinking
Method
One hundred sixty-three undergraduate business students
participated in a paper-and-pencil study in the laboratory.
Adapted from Luce's (1998) automobile choice task, decision
difficulty was manipulated through attribute tradeoffs. Certain
attributes are inherently more difficult to trade off than others.
Through meticulous pretests, Luce manipulated tradeoff difficulty through pairs of attributes that differ in attribute loss aversion
but are equivalent in attribute importance. In particular, she
demonstrated that it is more challenging to make a tradeoff
between occupant survival and pollution caused than between
routine handling and sound system.
Participants were randomly assigned to either a medium or
a high decision difficulty condition. They were given the
definition, the best value, and the worst value of each car
attribute, adapted from Luce (1998). Each participant was
asked to choose a car from six options described in four
attributes (Appendix B). From the perspective of information
load, this task was moderately complex. In the condition of
medium decision difficulty, participants faced this moderately
complex choice task without added tradeoff difficulty. They
made a relatively easy tradeoff between routine handling and
sound system. In the condition of high decision difficulty,
participants faced a similar task but with increased tradeoff
difficulty. They made the more challenging tradeoff between
occupant survival and pollution caused. In addition to these two
pairs of attributes, all participants evaluated the two common
features (price and styling).
Decision accuracy was measured in the same way as in Study
1, as was need for perfection (Cronbach's α = .83). In addition to
these measures, participants responded to a seven-item dichotomous thinking scale adapted from Byrne, Allen, Dove, Watt, and
Nathan (2008, p. 161) (e.g., “I think of things in ‘black and white’
terms”), each anchored by 1 (not at all true of me) and 4 (very true
of me) (Cronbach's α = .73). Participants also responded to a
13-item maximization scale developed by Schwartz et al. (2002)
(Cronbach's α = .60).
Results and discussion
Decision accuracy was analyzed using ANOVA as in Study 1.
Because of missing values, 161 observations were used in this
analysis. Consistent with H1, there was a significant decision
difficulty × need for perfection interaction (F(1, 157) = 13.86,
p b .001). While need for perfection increased decision accuracy
at medium decision difficulty (b = .12, S.E. = .04, t(80) = 3.29,
p b .01), it reduced decision accuracy at high decision difficulty
(b = − .10, S.E. = .05, t(77) = − 2.10, p b .05). To demonstrate
discriminant validity, a similar analysis was conducted with
maximization as the independent variable. Results showed that
maximization was not a significant predictor of decision
accuracy, nor did it interact with decision difficulty. Furthermore,
both the decision difficulty × need for perfection interaction and
the boomerang effect remained significant when maximization
was included as a covariate in the original model.
In line with Zhao, Lynch, and Chen's (2010) recommendations,
the mediating role of dichotomous thinking (H2) was examined
using the bootstrapping procedures developed by Preacher and
Hayes (2004). The details of mediation analyses are reported in
Fig. 1. In support of H2, dichotomous thinking was shown to fully
mediate the boomerang effect of perfectionism at high decision
difficulty (95% confidence interval (CI): [− .12, −.01]; 5000
resamples). In contrast, at medium decision difficulty, dichotomous thinking was not a significant mediator (95% CI: [− .06, .02];
5000 resamples). The overall model of moderated mediation was
tested using bootstrapping procedures (Hayes, 2013), and results
indicated a significant conditional indirect effect of dichotomous
thinking (95% CI: [− .12, − .01]; 5000 resamples). These results
provide support for the theorizing that the mediating role of
dichotomous thinking is conditioned on decision difficulty and is
only activated when the task is challenging and perfection is
difficult to attain.
A core argument for the boomerang effect is that perfectionists
tend to abandon the task at high decision difficulty due to
dichotomous thinking. While the mediation analyses provide
initial support for this underlying mechanism, task abandonment
is not tested directly. Study 3 addresses this issue by offering
participants an avoidance (no-choice) option. Because dichotomous thinking is activated only at high decision difficulty,
perfectionists are expected to increase task avoidance at high but
X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
101
Fig. 1. The mediating role of dichotomous thinking (Study 2).
not medium decision difficulty and such task avoidance should
be mediated by dichotomous thinking.
H3. Decision difficulty moderates the effect of need for
perfection on task avoidance. Specifically, need for perfection
increases task avoidance at high decision difficulty.
H4. Dichotomous thinking mediates the effect of need for
perfection on task avoidance at high decision difficulty.
Study 3: task abandonment
Method
One hundred fifty-four participants took part in this study,
which used the same choice task and decision-difficulty manipulation as in Study 2 but with one important
difference—participants were given the option of not choosing
any of the cars. Instead, they could opt to delay the decision
and continue searching for a better car. This measure of task
avoidance was adapted from Luce (1998) and was used as the
dependent variable in this study. The same measure of need for
perfection was used as in the previous studies (Cronbach's
α = .85), as was dichotomous thinking (Cronbach's α = .76)
and maximization (Cronbach's α = .65).
Results and discussion
The choice of task avoidance option was submitted to a
decision difficulty × need for perfection logistic regression. In
support of H3, there was a significant decision difficulty × need
for perfection interaction (χ2(1) = 4.81, p b .05), such that need
for perfection led to an increase in task avoidance at high decision
difficulty (b = .64, S.E. = .32, χ2(1) = 4.06, p b .05) but not at
medium decision difficulty (p = .25). In contrast, maximization
did not have a significant effect on task avoidance, nor did it
interact with decision difficulty. Furthermore, including maximization as a covariate did not weaken the effects in the original
model.
Next, Preacher and Hayes's (2004) bootstrapping procedures were used to examine the mediating role of dichotomous
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X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
thinking. Consistent with H4, dichotomous thinking fully
mediated the effect of perfectionism at high decision difficulty
(95% CI: [.11, .90]; 5000 resamples) but not at medium
decision difficulty. In all, these results were fully in line with
moderated mediation—the mediating effect of dichotomous
thinking was moderated by decision difficulty, and this
conditional indirect effect was statistically significant (95%
CI: [.09, 1.28]; 5000 resamples).
A common feature of these three studies is that each
participant is assigned to a given task. However, it remains
unclear, when given the opportunity to select their own tasks,
whether perfectionists may avoid the boomerang effect by
steering clear of difficult tasks on which they have a tendency
to perform poorly.
The answer to this question again lies in the psychological
mechanism of dichotomous thinking (Beck et al., 1990;
Shafran et al., 2002). Although perfectionists are driven to be
perfect in every aspect, they do not necessarily realize the
effect of dichotomous thinking until they are confronted by a
challenging task. Research shows that dichotomous thinking
arises from a unique set of schemas that are only activated in
specific circumstances (Arntz, 1999). Therefore, dichotomous
thinking is often below people's conscious awareness and is
inaccessible unless the nature of decision task sets it in motion.
The hidden nature of dichotomous thinking makes it difficult
for decision makers to anticipate its effect in task planning. On
the other hand, driven by their desire for perfection, they may find
the high-complexity task particularly appealing. Rich information
in such a task may give perfectionists an impression that they can
secure a perfect outcome later in the choice stage, without
realizing the downside of dichotomous thinking when actually
going through such task. Thus:
H5. Need for perfection influences task selection, such that the
higher the need for perfection, the more likely consumers will
choose high-complexity tasks.
While Study 3 demonstrates task avoidance among perfectionists when they realize that perfection is difficult to achieve,
Study 4 examines a somewhat different issue—task selection
when perfectionists do not realize the downside of a task. To this
end, Study 4 tests an interesting paradox—although perfectionists may prefer complex tasks (H5), these are the very tasks on
which they perform poorly (H1). To examine this paradox, Study
4 includes measures of both “objective” task performance based
on decision accuracy (Johnson & Payne, 1985; Payne et al.,
1988) and “subjective” task performance based on decision
satisfaction (Heitmann, Lehmann, & Herrmann, 2007).
Study 4: task selection versus task performance
Method
Two hundred eighty-seven undergraduate business students participated in an apartment rental task. Three real estate
agents were available in the local area to assist them in finding
an apartment (a free service). They represented three levels of
task complexity (amount of information) manipulated in the
same way as in Study 1. In the low-complexity scenario,
Realtor A would show three apartments, each described in three
dimensions. In the medium-complexity scenario, Realtor B
would show six apartments, each described in six dimensions.
In the high-complexity scenario, Realtor C would show 12
apartments, each described in nine dimensions. Rather than being
assigned to the three levels of task complexity, each participant
selected his or her own task by choosing the realtor with whom he
or she wanted to work.
Next, each participant was presented with a choice set that
corresponded to his or her chosen task complexity and was
asked to choose an apartment within the choice set. Decision
accuracy was measured in the same way as in the previous
studies, as was need for perfection (Cronbach's α = .88).
Decision satisfaction was measured using a six-item scale
adapted from Heitmann et al. (2007), with minor wording
changes to fit with the apartment rental scenario (e.g., “I found
the process of deciding which apartment to rent frustrating”
[reverse coded]) (Cronbach's α = .72).
Results and discussion
Task (realtor) selection was subjected to a logistic regression, which revealed a significant effect of need for perfection
(χ2(2) = 7.32, p b .05). Consistent with H5, need for perfection
increased the choice of the high-complexity task (Realtor C)
(b = .27, S.E. = .10, χ2(1) = 7.14, p b .01) but reduced the
choice of the medium-complexity task (Realtor B) (b = − .23,
S.E. = .10, χ2(1) = 5.61, p b .05). For the low-complexity task
(Realtor A), the effect of need for perfection was non-significant
(p = .65).
Decision accuracy was submitted to the ANOVA model as
in the previous studies and results showed a significant need
for perfection × task complexity interaction (F(2, 281) = 4.51,
p b .05). Need for perfection led to higher decision accuracy at
medium task complexity (b = .03, S.E. = .02, t(127) = 2.00,
p b .05) but lower decision accuracy at high task complexity
(b = − .06, S.E. = .03, t(121) = − 2.07, p b .05). Perfectionism
showed little effect at low task complexity (p = .34). The need
for perfection × task complexity interaction was also significant in the ANOVA on decision satisfaction (F(2, 281) = 3.98,
p b .05). While need for perfection increased decision satisfaction at medium task complexity (b = .13, S.E. = .06, t(127) =
2.00, p b .05), it resulted in lower decision satisfaction at high
task complexity (b = − .13, S.E. = .06, t(121) = − 2.10, p b .05).
The effect of need for perfection was non-significant at low task
complexity (p = .93).
These results reveal an important dilemma in perfectionists'
behavior. Although they prefer to tackle information-rich tasks,
they are comparatively worse off in these tasks than those with
low need for perfection.
General discussion
Through four studies, this research sheds light on consumer
perfectionism—an important but under-researched construct
X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
in marketing. Building on prior works (Kopalle & Lehmann,
2001; Wooten, 2000), this research extends the understanding
of consumer perfectionism in two ways. First, this research
shows that decision difficulty moderates the role of perfectionism, such that the boomerang effect is most likely to occur at
high decision difficulty. This research also demonstrates a
paradox in perfectionists' decision process—they sometimes
opt for complex tasks even though their subsequent performance in such tasks is impaired. Second, this research
demonstrates the underlying mechanism through dichotomous
thinking (Beck et al., 1990; Shafran et al., 2002). Because of
this unique thought process, perfectionists tend to abandon the
effort when they realize that perfection is no longer attainable.
This work helps uncover the root cause as to why perfectionists
sometimes make imperfect decisions.
Future research might continue this line of inquiry. In addition
to mediation analyses, it would be useful to examine the role of
dichotomous thinking more directly, in terms of task abandonment. Study 3 is a step in this direction through the choice of
avoidance option. Additional research is needed to fully unveil
the psychological process of dichotomous thinking. While
perfectionists have a tendency to avoid difficult task when they
realize that a perfect outcome is no longer achievable, Study 4
shows that they may gravitate towards such a task when they fail
to recognize its downside. These behavioral implications of
perfectionism warrant further investigation.
Consistent with prior research (Kopalle & Lehmann, 2001),
this study investigates consumer perfectionism as a unitary
trait. Future research might compare good “normal” perfectionism and bad “neurotic” perfectionism (Hamachek, 1978;
Sironic & Reeve, 2012), the latter of which might exacerbate
the boomerang effect. Another avenue for future research is to
study perfectionism as a situational variable—it is conceivable
that the same consumer may experience varying degrees of
103
perfectionism under different contexts. Furthermore, future
research could manipulate perfectionism through priming and
activate goals relevant to perfectionists.
Last but not least, it might be useful to examine other
consumption contexts where need for perfection plays a
significant role in consumer decision making. One such context
is gift giving where perfectionism is shown to increase anxiety
among consumers (Wooten, 2000). Additional research may
investigate whether need for perfection would decrease the
quality of gift choices. Furthermore, it might be fruitful to study
the role of perfectionism beyond decision accuracy. An
interesting avenue for future research is to examine whether
perfectionists use task abandonment as a tactic to improve their
image among others.
From a managerial perspective, perfectionism may be used to
segment the market (Kopalle & Lehmann, 2001). One way to
gauge consumers' need for perfection is through buyer questionnaires. For example, National Association of Realtors recommends
using such questionnaires to identify prospective home buyers'
characteristics and preferences (http://www.realtor.org/rmagprin.
nsf/pages/buyerquesprint). Likewise, Vanguard has developed an
investor questionnaire to measure potential clients' needs and
investment aptitude (https://advisors.vanguard.com/iwe/pdf/
investor_questionnaire.pdf). Adding the perfectionism scale
to these questionnaires may go a long way to improve
consumers' decision quality and satisfaction through customized
product offerings. Research shows that it may be counterproductive to offer people too many choices (Botti & Iyengar,
2006; Iyengar & Lepper, 2000). To enhance decision quality and
customer satisfaction, it might be useful to reduce the decision
difficulty, especially for perfectionist consumers. As this research
shows, maintaining medium levels of challenge may be the best
way to motivate perfectionists and guide them to ultimate
success.
Appendix A. Choice set (Studies 1 and 4).
Apartment
Cleanliness
Driving distance
from campus
Apartment
size
Brightness
of rooms
Noise
level
Laundry
facilities
Furniture
quality
Parking
space
Recreational
facilities
A
B
C
D
E
F
G
H
I
J
K
L
Excellent
Good
Excellent
Good
Good
Good
Good
Excellent
Average
Excellent
Average
Good
20 min
20 min
40 min
5 min
5 min
20 min
40 min
20 min
5 min
20 min
20 min
5 min
Small
Large
Moderate
Moderate
Small
Large
Large
Moderate
Moderate
Moderate
Large
Moderate
Average
Average
Poor
Good
Good
Average
Poor
Average
Good
Average
Average
Good
Low
Medium
Low
Medium
Medium
Medium
Medium
Low
High
Low
High
Medium
Average
Excellent
Good
Good
Average
Excellent
Excellent
Good
Good
Good
Excellent
Good
Average
Average
Below average
Above average
Above average
Average
Below average
Average
Above average
Average
Average
Above average
Limited
Plenty
Adequate
Adequate
Limited
Plenty
Plenty
Adequate
Adequate
Adequate
Plenty
Adequate
Excellent
Good
Excellent
Good
Good
Good
Good
Excellent
Average
Excellent
Average
Good
Note. The high-complexity condition contains 12 apartments described in nine attributes (shown above). Among the nine attributes, seven were adapted from Payne
(1976), some with minor modifications. These include cleanliness, driving distance from campus, apartment size, brightness of room, noise level, furniture quality,
and parking space. The other two attributes are laundry facilities and recreational facilities. The medium-complexity condition contains six apartments described in six
attributes (cleanliness, driving distance from campus, apartment size, brightness of rooms, noise level, and laundry facilities). The low-complexity condition contains
three apartments described in three attributes (cleanliness, driving distance from campus, and apartment size).
104
X. He / Journal of Consumer Psychology 26, 1 (2016) 98–104
Appendix B. Choice set (Studies 2 and 3).
Car
Price
Routine handling
(occupant survival)
Styling
Sound system
(pollution caused)
Car A
Car B
Car C
Car D
Car E
Car F
Worst
Good
Very poor
Very good
Average
Best
Very good
Poor
Best
Worst
Average
Very poor
Very poor
Average
Very good
Best
Good
Worst
Best
Average
Worst
Very poor
Poor
Very good
Note. High decision-difficulty condition is shown in parentheses.
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