The impact of infectious disease threat on consumers' pattern‐seeking in sequential choices

Abstract

The pandemic outbreak poses one of the most influential threats. When faced with such a threat, consumers engage in adaptive behaviors, and one way to do so may pertain to pattern-seeking in their choices. Across five studies, we show that consumers exhibit patterns in sequential choice under the threat of COVID-19. Specifically, consumers high (vs. low) in the perceived threat increase sequential patterns in repeated choice regardless of whether the levels of the perceived threat are measured or manipulated. The effect emerges even when a patterned choice option is objectively inferior to a nonpatterned option. The underlying mechanism of the effect is that consumers experience a lower sense of control, which motivates them to seek patterned choices to regain control threatened by the infectious disease. We further show that the effect on patterned choice is stronger for consumers with lower childhood socioeconomic status (SES), who are characterized by a lower sense of control, than their higher childhood SES counterparts. Noting that infectious disease threats are unavoidable, we offer theoretical contributions as well as novel insights into marketing practices under unpredictable and threatening situations.

Full text

Received: 11 April 2021
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Revised: 20 September 2021
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Accepted: 21 September 2021
DOI: 10.1002/mar.21602
RESEARCH ARTICLE
The impact of infectious disease threat on consumers'
pattern‐seeking in sequential choices
Jooyoung Park
1
|Jungkeun Kim
2
|Jihoon Jhang
3
|Jacob C. Lee
4
|
Jaehoon Lee
5
1
Peking University HSBC Business School,
Shenzhen, China
2
Department of Marketing, Auckland
University of Technology, Auckland,
New Zealand
3
Department of Marketing and Management,
University of Central Arkansas, Conway,
Arkansas, USA
4
Department of Business Administration,
Dongguk University, Seoul, Korea
5
Department of Marketing and Logistics,
Florida International University, Miami,
Florida, USA
Correspondence
Jungkeun Kim, Department of Marketing,
Auckland University of Technology, 120
Mayoral Dr., Auckland 1010, New Zealand.
Email: jkkim@aut.ac.nz
Abstract
The pandemic outbreak poses one of the most influential threats. When faced with
such a threat, consumers engage in adaptive behaviors, and one way to do so may
pertain to pattern‐seeking in their choices. Across five studies, we show that con-
sumers exhibit patterns in sequential choice under the threat of COVID‐19. Speci-
fically, consumers high (vs. low) in the perceived threat increase sequential patterns
in repeated choice regardless of whether the levels of the perceived threat are
measured or manipulated. The effect emerges even when a patterned choice option
is objectively inferior to a nonpatterned option. The underlying mechanism of the
effect is that consumers experience a lower sense of control, which motivates them
to seek patterned choices to regain control threatened by the infectious disease. We
further show that the effect on patterned choice is stronger for consumers with
lower childhood socioeconomic status (SES), who are characterized by a lower sense
of control, than their higher childhood SES counterparts. Noting that infectious
disease threats are unavoidable, we offer theoretical contributions as well as novel
insights into marketing practices under unpredictable and threatening situations.
KEYWORDS
childhood socioeconomic status, COVID‐19, decision pattern, disease threats, perceived threat,
repeated decisions
1|INTRODUCTION
Life is full of choices. We often select one option among many op-
tions, but we also frequently encounter situations where we make
repeated choices with a fixed set of items. For example, if we pur-
chase a multiple‐item bundle, such as six packs of different flavored
yogurt, we decide in which order we will consume them (Wang et al.,
2013). In our daily life, we frequently order the sequence of a fixed
set of options or tasks beyond food choices. When traveling, we
decide which place to go to first and later. When creating a daily
work plan, we determine what task to complete first and later.
Prior research provides some empirical evidence regarding where
consumers place their most or least favorite option in a sequence of
available options. For example, Mantonakis et al. (2009)showthat
consumers place their favorite option either in the first (i.e., primacy) or
last sequence (i.e., recency). Kc et al. (2020)findthatpeoplechooseto
complete easier tasks first to manage their load. Hwang et al. (2019)show
that people choose to engage in experiential activities early in their travel
schedule. Alternatively, Frederick and Loewenstein (2008) reveal con-
sumer preferences for improving sequences by delaying their preferred
options.O'BrienandEllsworth(2012) further show positivity bias for end
experiences, suggesting that individuals tend to have more positive atti-
tudes toward the last choices. Although prior work is informative in
predicting the sequence of certain liked or disliked options, it neglects the
overall pattern that may appear in repeated choice decisions. Surprisingly,
only scarce research has examined the emergence of patterns in con-
sumers' repeated choice decisions with a fixed set of options (J. Kim, Cui,
et al., 2020).
Psychol Mark. 2021;1–20. wileyonlinelibrary.com/journal/mar © 2021 Wiley Periodicals LLC
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The present research proposes a novel idea that pattern‐seeking
in repeated, sequential choice may be more pronounced in times of
COVID‐19. Disease cues such as COVID‐19 create health concerns
and a high degree of uncertainty (Fiorillo & Gorwood, 2020; J. Kim,
Lee, et al., 2021; Park et al., 2021). In the face of such an unsettling
and adverse state in a threatening situation, people experience
feelings of low control (Šrol et al., 2021) and engage in adaptive
behaviors to regain control (Kay et al., 2009). For example, consumers
are stockpiling essential products, called hoarding, in response to
disease threats to manage the uncertainty and possible shortage of
the future supply (Sheth, 2020). Product shortage or scarcity is per-
ceived as a loss of control (Gupta & Gentry, 2019), and thus stock-
piling helps them satiate a need for control (Kirk & Rifkin, 2020).
Furthermore, Whitson and Galinsky (2008) show that lacking control
activates an illusionary pattern perception, which refers to “the
identification of a coherent and meaningful relationship among a set
of random or unrelated stimuli”(p. 115). Extending the notion that
lacking control leads to cognitively adaptive strategies such as pat-
tern identification (Whitson & Galinsky, 2008), we propose that in-
fectious disease cues such as COVID‐19 lead to a threat to control,
which in turn motivates people to seek a pattern in their sequential,
repeated choices as an adaptive means to restore threatened control.
However, there may exist some alternative explanations for the
proposed effect. Threatening situations such as a pandemic may re-
sult in cognitive depletion and negative emotional states (Palmwood
& McBride, 2019), which motivate people to seek patterned choice as
an adaptive function in chaos. Without discounting these explana-
tions, the present research highlights the role of controllability in the
effect on pattern‐seeking in multiple choices.
In addition to our main proposition, we identify an individual
difference factor—childhood socioeconomic status (SES)—that may
pertain to a sense of control under threatening situations. Research
shows that early life experiences, which are commonly reflected in
one's childhood SES, shape the way an individual responds to
threatening environments (Griskevicius et al., 2011,2013). Compared
with those who grew up in benign, predictable environments (i.e.,
high childhood SES), individuals who grew up in unpredictable,
resource‐scarce environments (i.e., low childhood SES) tend to per-
ceive low control under threatening environments (Infurna et al.,
2011; Kraus et al., 2012; J. C. Lee et al., 2018) and desire to diminish
the downside costs of uncertainty (Amir et al., 2018). Accordingly,
individuals with lower childhood SES would be more likely to feel a
lack of control and make decisions aimed at minimizing uncertainty
under threats. Along this line, we further propose that childhood SES
provides an important boundary condition for the impact of in-
fectious disease threats on pattern‐seeking in repeated choices.
This study makes several contributions. First, although prior re-
search shows that consumers possess an inherent preference for
patterns when ordering a fixed set of options (J. Kim, Cui, et al.,
2020), the mechanism of consumers' pattern‐seeking in choice re-
mains unclear. Focusing on disease threats, we demonstrate that a
desire for control underlies consumers' pattern‐seeking in their
choice decisions. Second, our research extends the current
knowledge of consumer choice and decision‐making. Previous stu-
dies on repeated choice decisions have largely focused on the factors
deriving different choice outcomes. For instance, research on variety‐
seeking suggests that chosen outcomes in repeated choice depend
on choice modes (e.g., simultaneous vs. sequential choice; Read et al.,
1999; Simonson, 1990). Also, outcomes are influenced by stimulation
(Menon & Kahn, 1995), consumption situation (public vs. private
consumption; Ratner & Kahn, 2002), and/or consumer affective ex-
perience (Kahn & Isen, 1993). Relatively little attention has been paid
to the overall sequence of a fixed set of options consumers consume.
Our research reveals that consumers seek patterns in repeated
choices under the salience of infectious disease threats even when a
patterned choice option is objectively inferior to a nonpatterned
option. Finally, we show our findings' generalizability beyond the
domain of food consumption to various choice tasks.
2|LITERATURE REVIEW
2.1 |Patterns and consumer behavior
According to the Webster dictionary, a pattern is “a discernible
coherent system based on the intended interrelationship of
component parts.”Any sets consisting of multiple elements or
events (e.g., serial numbers, meal choices for a week) may exhibit a
pattern. For example, patterns emerge in a sequence of letters
(Simon & Kotovsky, 1963), numbers (Jones & Zamostny, 1975),
shapes (Leeuwenberg, 1971), pitches (Deutsch & Feroe, 1981),
tempos (Povel & Essens, 1985), finger‐tapping (Povel & Collard,
1982), or even lights (Restle, 1972). A pattern contains an inherent
property of a set (Restle & Brown, 1970).
Although any sets may exhibit a pattern, some patterns are more
easily detected than others (Restle, 1970). Thus, we understand a
pattern better with its degree on a continuum (i.e., high pattern vs.
low pattern) rather than a binary system (i.e., pattern vs. nonpattern).
According to Restle's (1970) recursive elements intervals (E‐I) model,
the degree of the internal representation of a sequence of events as a
high (vs. low) pattern depends on the similarity of elements and the
regularity of their intervals (Figure 1). Thus, people are more likely to
recognize patterns when similar (vs. dissimilar) events repeat reg-
ularly (vs. irregularly). Consistent with this conceptualization, a pat-
tern in choice sequence involves a coherent repetitive subgrouping of
options (J. Kim, Cui, et al., 2020). For example, when consuming a
fixed set of products, consumers can consume liked items (denoted
L's) first and disliked items (denoted D's) later (e.g., LLDD) or in the
reverse order (e.g., DDLL).
Historically, identifying patterns in nature (e.g., predicting storms
from lunar halos, expecting a hurricane from increased ocean swells,
or interpreting celestial events) has provided humans with many
advantages for survival. By recognizing patterns, humans understand
how the world operates, set up expectations, and prepare for the
future (Hastie, 1984; Pyszczynski & Greenberg, 1981). Indeed, prior
research shows that humans have evolved to prefer certain patterns
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(Enquist & Arak, 1994). Along this line, recent research demonstrates
that consumers evaluate a product more favorably when its adver-
tisement features a regular (vs. irregular) visual pattern (Farace et al.,
2020). This preference for patterns also influences our perception.
When experiencing a lack of control, people tend to identify sig-
nificantly more illusory patterns in randomness (Wang et al., 2012;
Whitson & Galinsky, 2008). Taking this idea forward, we posit that
the preference for patterns may influence how people decide their
consumption sequence. Given that the degree of patterns depends
on the interrelationship among components, people may create
patterns by actively arranging the sequence of their consumption
choices.
To the best of our knowledge, however, very few empirical
studies have investigated this topic. J. Kim, Cui, et al. (2020) explored
consumers' preferences for food consumption sequences. Across
multiple experiments, the authors find that pattern‐seeking is the
most viable strategy among others, such as primacy, recency, or
variety‐seeking. Although J. Kim, Cui, et al. (2020) offer some
FIGURE 1 Two principles in pattern recognition
PARK ET AL.
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interesting insights about pattern‐seeking in repeated choices, the
effect's underlying mechanism remains unanswered. Consequently,
we understand little about why and when people attempt to create
patterns in their sequential, repeated choices. In the present re-
search, we address this issue.
2.2 |Impacts of disease threat
The COVID‐19 pandemic has significantly disrupted and altered con-
sumers' lives. For example, Campbell et al. (2020) suggest that disease
threat adversely affects consumers' ontological security, resulting in
psychological and affective (e.g., fear, anxiety) as well as behavioral (e.g.,
increased indulgent consumption) responses. Also, Sheth (2020)shows
that COVID‐19 experiences such as social distancing and lockdown have
dramatically changed consumers' consumption behavior, such as in-
creased online shopping and consumption.
Broadly, we can classify the extant literature into two streams.
One stream of research suggests that the threat of COVID‐19 gen-
erates the decision outcomes which reflect risk reduction (J. Kim,
Giroux, et al., 2020; S. Li et al., 2021). For example, Huang and
Sengupta (2020) show that consumers prefer atypical (vs. typical)
products in the presence of contagious disease cues because atypical
products are conceptually linked to a small number of people and
thus have a lower chance of infection. Galoni et al. (2020) reveal that
fear and disgust elicited by contagious diseases increase preferences
for familiar (vs. nonfamiliar) options as they help restore a sense of
control. Pena‐Marin et al. (2021) further show that after COVID‐19,
financial investors prefer high‐priced (vs. low‐priced) stocks because
they are perceived to be more stable.
The other stream of research illustrates the impact of disease
threats on consumers' freedom‐seeking (J. Kim, 2020) or social
connection (Kwon et al., 2021). The living environment and govern-
ment policies such as lockdown and social distancing induce a sense
of isolation and lack of freedom. J. Kim (2020) shows that COVID‐19
decreases personal control. Moreover, Kwon et al. (2021) show that
social isolation due to the pandemic increases preferences for not
proximally available products. The authors suggest that strong mo-
tivation for social connection induces such preferences.
Although researchers agree with the theoretical and practical
importance of investigating the influence of disease threats on con-
sumer behavior and decision‐making, empirical evidence is still lim-
ited. More importantly, extant research has focused mainly on single‐
choice occasions and neglected the impact of disease threats on
consumers' sequential choices.
2.3 |Childhood SES
According to an evolutionary perspective, early‐life experiences can
internalize the manners in which people respond to environments
(Ellis et al., 2009). A growing body of research has shown that en-
vironmental conditions encountered during early childhood influence
many aspects of individuals' lives such as cognitive performance
(Nisbett, 2009), altruistic behavior (H. Li et al., 2020), eating behavior
(Hill et al., 2016), impulsivity, (Griskevicius et al., 2011), risk‐taking,
and mental discounting (Griskevicius et al., 2013). Early‐life experi-
ences are commonly reflected in childhood SES—people growing up
in lower SES environments encounter more unpredictable early‐life
experiences than those who grew up in higher SES environments
(Mittal & Griskevicius, 2014).
To explain how early‐life experiences and environments de-
termine individuals' behaviors, Amir et al. (2018) propose a frame-
work, called uncertainty management strategy, suggesting that
individuals who grew up in low childhood SES tend to form their
preferences aimed at diminishing the downside costs of uncertainty.
The authors posit that people with scarce resources cannot afford
negative returns and have to develop strategies to minimize un-
certainty. Thus, under uncertainty, individuals' childhood environ-
ments can influence whether they interpret a certain risk as being
affordable or not. For example, according to the uncertainty man-
agement strategy, risk‐pooling through cooperation with other social
entities is a potential defensive strategy against uncertainty
(Winterhalder, 1986,1990). Therefore, those who are highly vul-
nerable to changes in their environment are likely to defend against
unexpected events by cooperating with others or engaging in pro-
social behavior (Amir et al., 2018). Of note, because childhood SES
internalizes the patterns of responses to the environment over time,
the impact of childhood SES is often more predictive of behaviors in
adulthood than that of current SES (Griskevicius et al., 2011;
Thompson et al., 2020).
3|HYPOTHESES DEVELOPMENT
COVID‐19 is considered one of the most serious threats in human
history (Morens et al., 2020), thus generating a high degree of un-
certainty (Fiorillo & Gorwood, 2020). Prior literature in evolutionary
psychology suggests that disease‐related threats can systematically
shape people's behavior (Griskevicius & Kenrick, 2013; Murray &
Schaller, 2016). Murray and Schaller (2016) argue that humans have
evolutionarily developed a psychological mechanism to avoid in-
fectious diseases. Thus, when a disease cue is present, the psycho-
logical behavioral immune system triggers disease‐avoidance motives
(Miller & Maner, 2012).
More important to the present research, such a psychological
mechanism may pertain to a sense of control. Control is one of the
fundamental motives (Legare & Souza, 2014; Shiu et al., 2011) and
helps people overcome environmental uncertainty (Whitson &
Galinsky, 2008). It is well documented that people lack control during
the pandemic and thus attempt to restore it. For example, when
faced with COVID‐19, people feel a low sense of control, which
increases an endorsement of conspiracy theories to reclaim control
(Šrol et al., 2021) because conspiracy theories make people feel that
they have a better account (Douglas et al., 2019). Furthermore,
people engage in hoarding behavior during COVID‐19 to deal with
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product scarcity (Sheth, 2020). Product scarcity is viewed as a loss of
control (Gupta & Gentry, 2019), and thus stockpiling helps people
regain control (Kirk & Rifkin, 2020). These findings highlight that
consumers' desire for control may play a key role in explaining sys-
tematic differences in behavior as a function of the infectious disease
threat.
As choice enables individuals' preferences and values to be ob-
servable and overt, consumers commonly express themselves
through choice (Aaker & Schmitt, 2001; H. S. Kim & Drolet, 2003;H.
S. Kim & Sherman, 2007). Applying reactance theory, which postu-
lates that individuals react when their freedom is restricted or
threatened, research shows that consumers can restore their threa-
tened freedom through their choice behavior (Clee & Wicklund,
1980; Hinsch et al., 2021). For example, when consumers are spa-
tially confined, they perceive a threat to their freedom. Levav and
Zhu (2009) show that consumers seek variety in their choice against
an incursion to their personal space. Focusing on another type of
threat to one's freedom, Yoon and Kim (2018) find that consumers
with low SES who perceive that they are economically stuck seek
more variety to compensate for their low sense of control. Extending
the reactance theory in which consumers restore threatened freedom
through choice behavior, in the present research, we propose a novel
phenomenon in the consumption choice sequence. While there is no
direct empirical support for pattern‐seeking as a means of regaining
control, prior research indicates that lacking control leads people to
identify a coherent and meaningful relationship among random ele-
ments, namely illusionary pattern‐seeking (Wang et al., 2012;
Whitson & Galinsky, 2008). Extending prior research, we contend
that, as a disease threat triggers the need to structure the world into
a more manageable, simplified form, consumers would respond to the
need by making a coherent sequence of repetitive choices. We hence
predict that consumers will seek a pattern that helps them regain
control when making sequential choices of a related fixed set of
options in a threatening (vs. nonthreatening) situation. We formally
hypothesize:
H1 –The higher threat of COVID‐19 (vs. baseline) will increase con-
sumers' pattern‐seeking in sequential choice of a fixed set of
items.
H2 –The desire for control will mediate the influence of COVID‐19
threat on consumers’pattern‐seeking in sequential choice.
If the desire for control is a key mechanism underlying con-
sumers' pattern‐seeking, an individual difference in perceived control
over the environment should influence our prediction. As described
earlier, individuals' early‐life environments shape various aspects of
behavior in adulthood (Griskevicius et al., 2011; Hill et al., 2016;H.Li
et al., 2020; Wang et al., 2020; Whelan & Hingston, 2018). Along this
line of research, we propose that childhood SES affects individuals'
pattern‐seeking under infectious disease threats.
Specifically, uncertainty management strategy (Amir et al., 2018)
suggests that individuals who grew up in unpredictable resource‐
scarce environments tend to develop preferences in a way to mini-
mize the costs of uncertainty. Low childhood SES is characterized by
scarce resources and environmental unpredictability (Mittal &
Griskevicius, 2014). Because the scarce resources and unpredictable
environments in early life could lower individuals' ability to com-
pensate for losses or change the future, those with low childhood
SES would perceive a lack of control (Infurna et al., 2011; Kraus et al.,
2012). Supporting this, Mittal et al. (2015) show that individuals who
grew up in unpredictable environments perform better at switching
between tasks because repeated exposure to unpredictable en-
vironments could enhance their ability to effectively adapt to chan-
ging environments. In contrast, individuals who grew up in resource‐
affluent environments tend to have a higher sense of control even
when encountering uncertain situations (Mittal & Griskevicius, 2014),
decreasing their motivation to employ an adaptive strategy.
In sum, because individuals with lower childhood SES perceive
low control over their environments but need to minimize losses,
they would develop their preferences aimed at decreasing un-
certainty. To reduce uncertainty but gain control under a disease
threat, individuals with lower childhood SES would prefer patterns in
repeated choices. In comparison, characterized by abundant re-
sources to compensate for adverse outcomes, individuals with higher
childhood SES would be less likely to develop such an adaptive
strategy. Thus, those with higher childhood SES would be less re-
sponsive to an infectious disease threat than those with lower
childhood SES. We thus propose that childhood SES will moderate
our main hypothesis such that:
H3 –Pattern‐seeking in sequential choices will be stronger for people
with lower (vs. higher) childhood SES.
Five studies test these three hypotheses (see Table 1, for a
summary of the detailed empirical studies). Study 1 provides initial
evidence of the relationship between the COVID‐19 threat and
pattern‐seeking in repeated choices. Study 2 replicates the previous
study by directly manipulating the different levels of COVID‐19
threat and tests the proposed mechanism. Study 3 provides addi-
tional support in actual picture‐evaluation tasks. Unlike Studies 1, 2,
and 3 in which we examined pattern‐seeking in negatively and po-
sitively valenced options (e.g., how people consume favorite and
nonfavorite options in repeated choices), Study 4 employs only po-
sitively valanced options in a choice task (i.e., all favorite options) to
enhance generalizability. In addition, Study 4 demonstrates the un-
ique impact of the COVID‐19 threat by including a general health risk
condition as a comparison group. Finally, Study 5 tests the boundary
condition of childhood SES and shows that people with low (vs. high)
childhood are more likely to seek patterns under an infectious disease
threat. All studies were conducted from August 2020 to July 2021 in
the United States to reduce any potential country‐specific effects.
PARK ET AL.
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TABLE 1 Summary of empirical results
Study 1—Initial evidence (N= 188 m Turkers; M
age
= 40.35; 54.8% Women)
IV: Covid‐19 threat (measured) Test statistics and pvalues
Low (−1SD) High (+1 SD)
Context Eat 6 jellybeans
•3 favorite flavors (F)
•3 not‐so‐favorite flavors (N)
Choice task Choose the sequence of 6 jellybeans
DV Choice of high pattern
(i.e., FFFNNN, NNNFFF, FNFNFN, NFNFNF)
68.9% 82.1% β= 0.247, se = 0.12,
Wald = 4.44, p= 0.035,
−2LL = 204.67
Finding(s) •Perceived COVID‐19 threat positively correlates with preference for pattern.
Study 2—Causal relationship and mediation (N=198 m Turkers; M
age
=42.08; 49.5% Women)
IV: Covid‐19 threat (manipulated) Test statistics and pvalues
Low (n= 96) High (n= 102)
Context Eat 6 jellybeans
•Favorite (F)
•Not‐so‐favorite (NSF)
•Least‐favorite (LF)
Choice task Given LF‐NSF‐F‐()‐()‐(), choose the remaining
sequence between option A and B. Option B is
objectively inferior to option A.
A: NSF‐NSF‐F (low pattern)
B: LF‐NSF‐F (high pattern)
Mediator Perceived uncontrollability M= 3.27(1.60) M= 3.96(1.70) F(1, 196) = 8.61, p= 0.004
DV % of option B 42.7% (41/96) 58.8% (60/102) χ
2
= 5.14, p= 0.023
Finding(s) •COVID‐19 threat increases preference for choice pattern.
•Participants in the high COVID‐19 threat condition chose a high pattern even when the option was inferior.
•Perceived uncontrollability mediates the relationship (Hayes Model #4, Indirect effect = 0.06, 95% CI [0.001, 0.152]
Study 3—Actual choice (N=175 m Turkers; M
age
=41.54; 53.1% Women)
IV: Covid‐19 threat (manipulated) Test statistics and pvalues
Low (n= 91) High (n= 84)
Context Evaluate 6 pictures
•3 positive pictures (P)
•3 negative pictures (N)
M
positive
= 6.08 (SD = 0.84),
M
negative
= 2.23
(SD = 1.07),
t(167) = 33.20, p< 0.001
Choice task Given P‐N‐()‐()‐()‐(), choose the remaining 4 pictures
sequentially
M
enjoyment
= 5.09
(SD = 1.45)
M
enjoyment
= 5.08
(SD = 1.57)
F(1, 173) = 0.01,
p= 0.984, η
2
< 0.001
DV1 % of PNPN 6.6% (6/91) 14.3% (12/84) χ
2
= 2.80, p= 0.094
DV2 % of PNPN & NPNP 11.0% (10/91) 23.8% (20/84) χ
2
= 5.06, p= 0.025
Finding(s) •Preference for pattern is replicated in the actual sequential choices.
Study 4—Evaluation of a patterned sequence (N=196 mTurkers; M
age
=42.06; 52.0% Women)
IV: Covid‐19 threat (manipulated) Test statistics and pvalues
Low (n= 102) High (n= 94)
Context Eat 8 jelly beans from each of the two equally
preferred flavors (X and Y)
Evaluation
task
Evaluate either the patterned or nonpatterned
sequence
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4|STUDY 1: PROVIDING INITIAL
EVIDENCE
Study 1 aims to provide initial empirical evidence of the relationship
between the perceived threat of COVID‐19 and pattern‐seeking in
sequential choices of fixed options.
4.1 |Method: Participants, design, and procedure
Participants were 188 U.S. adults (54.8% female; M
age
= 40.35,
SD = 13.82) recruited from Amazon Mechanical Turk (MTurk) on-
line panel with a nominal payment. We first presented participants
with information about COVID‐19 (i.e., a description of COVID‐19
with the World Health Organization's definition and its image) and
asked them to provide their perceived threat of COVID‐19. We
measured the perceived threat using two items (i.e., “What are the
chances of you getting infected with the coronavirus?”and “What
are the chances of an average person getting infected with the
coronavirus?”)ona7‐point scale (1 = extremely low, 7 = extremely
high, r= 0.809, p< 0.001). These items were adopted from J. Kim
and Lee (2020).
We then asked participants to imagine that they were about to
eat six jellybeans consisting of three favorite flavors and three not‐
so‐favorite flavors (the same task as in Study 1 A from J. Kim, Cui,
et al., 2020). Following this, we asked participants to choose the
sequence of six choices,
1
asshowninAppendixA. Finally,
participants reported their demographic information (i.e., age and
gender).
4.2 |Results and implications
Overall, there were 20 different possible choice outcomes. Following
the procedure of J. Kim, Cui, et al. (2020), we coded four specific
choice outcomes as patterned choices (i.e., FFFNNN, NNNFFF,
FNFNFN, and NFNFNF)
2
and 16 others as nonpatterned choices
(e.g., FFNNFN, NFFNNF, or FNNNFF). Overall, participants showed
relatively higher choice pattern (M= 75.5% [=142/188] vs. random
share = 20% [=37.6/188], χ
2
= 116.18, p< 0.001), consistent with the
results of J. Kim, Cui, et al. (2020).
More importantly, we conducted a bi‐logistic analysis to test the
impact of the perceived threat on choice patterns. We found a po-
sitive association between the perceived threat and choice patterns
(−2 Log‐Likelihood = 204.67, β= 0.247, SE = 0.12, Wald = 4.44,
p= 0.035). This pattern remained significant (p= 0.026) when
DV Evaluation of the patterned sequence
(XXYYXXYY)
5.46 (1.54)
p= 0.232
5.09 (1.55)
5.71 (1.31)
p< 0.001
4.44 (1.76)
The two‐way interaction
F(1, 192) = 4.11, p= 0.044,
η
2
= 0.021
nonpatterned sequence
(XXYXXYYY)
Finding(s) •The same pattern of results emerged when the sequence consists of equally preferred options.
•The same pattern of results emerged when evaluative rather than choice task was given.
•Neither cognitive depletion nor emotional nervousness explained the pattern of results.
Study 5—Moderating role of childhood SES (N=225 mTurkers; M
age
=40.35; 52.9% Women)
IV: Covid‐19 threat (measured) Test statistics and pvalues
Low (−1SD) High (+1 SD)
Context Listen to 6 songs
3 favorite songs (F)
3 not‐so‐favorite songs (N)
Choice task Choose the sequence of 6 songs
DV/Low
C‐SES
Choice of high pattern
(i.e., FFFNNN, NNNFFF, FNFNFN, NFNFNF)
62.6% 78.7% β= 0.27, se = 0.14, z= 2.01,
p= 0.044, 95% CI:
[0.007, 0.539]
DV/High
C‐SES
58.3% 50.6% β=−0.11,
se = 0.14, z=−0.78,
p= 0.433, 95% CI:
[−0.371, 0.159]
Finding(s) •Moderating role of childhood SES; Preference for pattern was replicated only for those low in childhood SES.
•Current SES does not moderate the effect/Overall preference for music does not change the results.
1
Eighteen participants were excluded from the analysis because their choices were not three
choices from each of the favorite and not‐so‐favorite categories. Nevertheless, when
we recorded their choices as nonpattern choices, the results remained consistent
(−2 Log‐Likelihood = 246.80, β=0.199, SE = 0.10, Wald = 3.72, p= 0.054).
2
“F”represents favorite flavors, whereas “N”represents not‐so‐favorite flavors.
PARK ET AL.
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7

participants' current income (p= 0.354), gender (p= 0.258), and age
(p= 0.747) were added as covariates.
In sum, Study 1 provides initial support for the relationship be-
tween the perceived threat of COVID‐19 and pattern‐seeking in
sequential choices. However, the study bases its results on correla-
tional data. To directly test a causal relationship, we manipulated the
COVID‐19 threat in Study 2.
5|STUDY 2: MANIPULATING THREAT
AND PROVIDING MEDIATION EVIDENCE
Study 2 uses a direct manipulation of disease threat to test its impact
on pattern‐seeking in sequential choice. Furthermore, we test the
proposed mechanism of perceived controllability that underlies the
impact of the COVID‐19 threat on the pattern choice decision. Fi-
nally, we test whether the effect of COVID‐19 on pattern‐seeking in
sequential choice emerges even when a patterned choice option is
objectively inferior to a nonpatterned option.
5.1 |Method: Participants, design, and procedure
We recruited 198 participants (49.5% female; M
age
= 42.08, SD =
12.70) from MTurk for a nominal payment. We employed a two‐
group (COVID‐19 threat: high vs. control) between‐subjects design
and randomly assigned participants to one of the two experimental
conditions. We informed participants that the study involved several
unrelated tasks. We first asked participants to read a newspaper
article purportedly published in The New York Times. The articles'
length and format in the two experimental conditions were similar,
but their content was different. Participants in the high‐threat con-
dition read an article titled “Can COVID‐19 Damage the Brain?”In
contrast, participants in the control condition read an article about a
food titled “This One‐Pan Meal Shows Just How Joyful Tofu Can Be”
(Appendix B).
3
To test whether consumers preferred patterned op-
tions as an adaptive strategy to regain control under an infectious
threat, we measured perceived uncontrollability using two items (i.e.,
How much uncontrollability/unpredictability do you feel at the mo-
ment?) on a 7‐point scale (1 = not at all, 7 = very much, Cronbach's
α= 0.910).
Next, participants imagined consuming six jellybeans consisting
of two favorite, two not‐so‐favorite, and two least‐favorite flavors
(based on Study 4B in J. Kim, Cui, et al., 2020, See Appendix C). We
further asked them to imagine that they had already consumed three
in the sequence of “least‐favorite”[#1 choice] →“not‐so‐favorite”
[#2 choice] →“favorite”[#3 choice]. We then asked participants to
choose one of two sequences for the three remaining consumption
cases. Option A was (“not‐so‐favorite”[#4 choice] →“not‐so‐
favorite”[#5 choice] →“favorite”[#6 choice]), whereas Option B was
(“least‐favorite”[#4 choice] →“not‐so‐favorite”[#5 choice] →“fa-
vorite”[#6 choice]). When considering which one is objectively better
(option A or option B) from the three consumption cases, the non-
patterned option (i.e., option A) is objectively better than the pat-
terned option (i.e., option B).
5.2 |Results and implications
First, we compared preferences for the pattern choice across two
conditions. Consistent with our prediction, the results showed that
the preference for the pattern option was higher even when the
option was objectively inferior to the other under the COVID‐19
threat (M= 58.8% [=60/102]), relative to the control condition
(M= 42.7% [=41/96], χ
2
= 5.14, p= 0.023). Second, the COVID threat
salience also influenced perceived uncontrollability in that partici-
pants in the high threat condition perceived greater threat than those
in the control condition (M
high threat
= 3.96, SD = 1.70 vs. M
control
=
3.27, SD = 1.60; F(1, 196) = 8.61, p= 0.004, η
2
= 0.042).
Finally, to test the mediation model (i.e., COVID‐19 threat [−1:
control and 1: high threat] →perceived uncontrollability →choice
pattern), we used the Hayes' macros process (2017, model #4 with
5000 bootstrapping). The indirect effect was significant (Effect =
0.06, SE = 0.04, 95% CI: [0.001, 0.152]), and the residual direct effect
became marginally significant (Effect = 0.27, SE = 0.15, p= 0.065,
95% CI: [−0.017, 0.561]). This result suggests that the effect of
COVID‐19 threat on the choice pattern is fully mediated by per-
ceived uncontrollability.
In sum, this result provides direct evidence of the causal influ-
ence of the COVID‐19 threat on consumers' pattern‐seeking in se-
quential choices. Furthermore, unlike Study 1 where participants
ordered the sequence of consumption, this study simultaneously
presented two sequences of consumption choices. Regardless of the
choice mode (i.e., simultaneous vs. sequential), pattern‐seeking is
more likely to emerge when the COVID‐19 threat is present. Fur-
thermore, this study empirically demonstrated that the effect ob-
served in the study was explained by perceived uncontrollability.
6|STUDY 3: USING ACTUAL
EXPERIENCE
In Studies 1 and 2, we have examined the impact of the COVID‐19
threat on consumers' pattern‐seeking using hypothetical choice
tasks. Although J. Kim, Cui, et al. (2020) suggest that hypothetical and
real choices can be similarly valid, real choices will guarantee ecolo-
gical validity. Using real picture‐evaluation tasks, we examine con-
sumers' pattern‐seeking in Study 3.
3
We conducted a pretest to compare three experimental conditions (one threat condition
and two control conditions) that were used in Study 2 or 3 (n= 87, 46.0% women from
MTurk) by following prior research (Galoni et al., 2020). Specifically, after reading a
newspaper article, participants reported their perceived fear (i.e., scared, anxious, and
nervous), disgust (i.e., unclean, disgusted, and dirty), and mood (i.e., secure, grateful, and
lucky) on a 7‐point scale (1 = not at all, 7 = very much). The results indicated that perceived
fear was higher in the high threat condition than in the two control conditions. In contrast,
perceived disgust and mood were similar across the three conditions. The detailed results are
available from the corresponding author.
8
|
PARK ET AL.

6.1 |Method: Participants, design, and procedure
We recruited 175 participants (53.1% female; M
age
= 41.54, SD =
13.07) from MTurk for a nominal payment. As in Study 2, we ran-
domly assigned participants to one of two (COVID‐19 threat: high vs.
control) experimental conditions.
WeusedthesameCOVID‐19 threat manipulation task as
in Study 2, but we used a different newspaper article in the
control condition. Specifically, participants in the control
condition read an article about a golf tournament titled “ANerve‐
Racking Final Round Adds Drama to Golf's Fan‐Free Return”
(Appendix B).
We then asked participants to complete a picture‐evaluation
task. We first presented participants with six different pictures in-
volving three relatively positive (i.e., Positive #1, Positive #2, and
Positive #3) and three relatively negative (i.e., Negative #1, Negative
#2, and Negative #3) pictures. We told them that they had to eval-
uate all six pictures (See Appendix D).
Both experimental conditions included the first two pictures.
We asked the participants to evaluate the first positive picture (i.e.,
Positive#1)asthefirstevaluationtask(task#1)andthenthefirst
negative picture (i.e., Negative #1) as the second evaluation task
(task #2) on a 7‐point scale (1 =not at all attractive, 7 =very at-
tractive). Next, participants chose one picture for the third eva-
luation task (task #3) out of the two remaining positive pictures
and two negative pictures (i.e., Positive #2, Positive #3, Negative
#2, and Negative #3). After choosing the third picture, participants
evaluated it on the same 7‐point scale. Participants repeatedly
chose the pictures to evaluate the remaining ones until they fin-
ished selecting and evaluating all the pictures.
4
We also measured
participants' overall enjoyment (1 = not at all, 7 = very much) in the
final stage.
6.2 |Results and implications
The manipulation of the positivity (negativity) of pictures was suc-
cessful in that participants evaluated the three positive pictures
(M
average
= 6.08, SD = 0.84) as more attractive than the three negative
ones (M
average
= 2.23, SD = 1.07, t(167) = 33.20, p< 0.001).
In the experiment, participants chose the sequence of four
evaluation tasks (i.e., task #3 →task #4 →task #5 →task #6).
In the main analysis, we did not consider the specific picture within
the positive and negative pictures. Therefore, all six different
sequences were possible (i.e., PPNN, PNPN, PNNP, NNPP, NPNP,
NPPN).
5
We coded “PNPN”as a patterned Sequence I and five
remaining sequences as nonpatterned ones because participants
evaluatedthePNsequenceinthefirstandsecondevaluation
tasks. We also coded “PNPN”and “NPNP”as a patterned
Sequence II and four remaining others as a nonpatterned
sequence.
6
We compared preferences for patterned sequences across two
conditions, as in Study 2. The results supported our expectations. Pat-
terned Sequence I was higher when the COVID‐19 threat was high
(M= 14.3% [=12/84]) than when the threat was absent (M= 6.6% [=6/
91], χ
2
= 2.80, p= 0.094). We found a similar pattern for patterned‐
sequence II in that participants were more likely to prefer the patterned
sequence when the COVID‐19 threat was high (M= 23.8% [=20/84])
than when it was absent (M= 11.0% [=10/91], χ
2
= 5.06, p= 0.025).
7
Finally, the overall enjoyment across two conditions was not different
from each other (M
high threat
=5.08, SD =1.57 vs. M
control
=5.09, SD =
1.45, F(1, 173) = 0.01, p= 0.984, η
2
< 0.001).
7|STUDY 4: PROVIDING EMPIRICAL
EVIDENCE OF GENERALIZABILITY
In our previous studies, stimuli were different in valence in that some
options were positive (e.g., favorite flavor) and others were negative
(e.g., not favorite flavor). In this study, we test pattern‐seeking in equally
valanced options (i.e., all favored options). In addition, we compare the
effects of the COVID‐19 threat and the threat of a general health risk.
We expect that pattern‐seeking will be higher for the COVID‐19 (vs.
general health risk) threat condition. Finally, we test some alternative
explanations to provide further support for our proposed mechanism.
7.1 |Method: Participants, design, and procedure
We recruited 196 participants (52.0% female; M
age
= 42.06, SD =
13.06) from MTurk for a nominal payment. We manipulated the le-
vels of COVID‐19 threat as in Study 2. However, we provided one of
two choices that differ in the degree of pattern and measured sa-
tisfaction with the given choice. Thus, we used a 2 (COVID‐19 threat:
high vs. control) × 2 (consumption sequence: high pattern vs. low
pattern) between‐subjects design.
We informed participants that this study involved several un-
related tasks. We first asked participants to read a newspaper article
as in Study 2. Participants in the high‐threat condition read the same
article regarding COVID‐19 used in Study 2, whereas participants in
the control condition read an article about health risk titled “Heart
Attacks versus Cardiac Arrest”(Appendix B). Previous research in-
dicates that a threat may cause cognitive depletion and emotional
exhaustion (Palmwood & McBride, 2019). To rule out these alter-
native explanations, we measured cognitive depletion and emotional
nervousness. Following prior work (Brengman et al., 2012), we asked
4
Additional 26 participants were excluded from further analysis as they did not finish
evaluating all four remaining pictures.
5
“P”represents a positive image, whereas “N”represents a negative image.
6
In this coding, we relax the assumption of a pattern in that the first two choices used two
different images. By doing so, NPNP could be coded as a patterned sequence.
7
The overall pattern‐seeking rate was relatively low for this study. We suspect that the
unique characteristic of the decision task (i.e., mixed with the choice tasks and actual
evaluation tasks) of this study generated lower pattern‐seeking since participants could not
choose all options simultaneously.
PARK ET AL.
|
9

participants to report the extent of cognitive depletion (i.e., “At this
moment, I feel tired/depleted,”Cronbach's α= 0.867) and their
emotional nervousness (i.e., “At this moment, I am feeling nervous/
irritated,”Cronbach's α= 0.814) on a 7‐point scale (1 = not at all,
7 = very much).
Finally, participants were asked to imagine that they were eating
eight jellybeans from two different but equally preferred flavors. Partici-
pants were presented with one of two different sequences (i.e., high
pattern [i.e., XXYYXXYY] or low pattern [XXYXXYYY]),
8
as shown in
Appendix E, and evaluated the given sequence on 7‐point scales (1 = not
at all satisfied/pleased, 7 = very satisfied/pleased, Cronbach's α= 0.972).
7.2 |Results and implications
We expected that the evaluation of sequence would be higher for the
high pattern than the low pattern choice when the COVID‐19 threat
was high. We also expected that there would be no difference in the
evaluation of the sequences when the COVID‐19 threat was absent.
Atwo‐way ANOVA yielded a significant main effect of consumption
sequence, F(1, 192) = 13.61, p< 0.001, η
2
= 0.066. Participants evaluated
the high pattern sequence more favorably than the low pattern sequence
(M_
high pattern
=5.59, SD =1.42 vs. M_
low pattern
= 4.81, SD =1.67). The
main effect of COVID‐19 threat was not significant, F(1, 192) = 0.81,
p=0.369, η
2
= 0.004. More importantly, as we predicted, the two‐way
interaction effect was significant, F(1, 192) = 4.11, p=0.044, η
2
= 0.021.
Planned contrast showed that participants in the high COVID‐19 threat
condition more favorably evaluated the high pattern than low pattern
sequence (M_
high pattern
=5.71, SD =1.31 vs. M_
low pattern
=4.44, SD =
1.76; F(1, 192) = 15.72, p<0.001, η
2
= 0.076). For the control condition,
there was no difference in participants' evaluations of the high and low
pattern sequences (M_
high pattern
=5.46, SD =1.54 vs. M_
low pattern
=5.09,
SD =1.55; F(1, 192) = 1.44, p=0.232, η
2
= 0.007).
Additionally, we repeated the same analysis for cognitive depletion
and emotional nervousness, but we did not find any significant results
includingthemaineffectofCOVID‐19 threat (for cognitive depletion, F
(1, 192) = 0.21, p=0.651, η
2
= 0.001 and for others ps > 0.333; for emo-
tional nervousness, F(1, 192) = 0.24, p= 0.622, η
2
=0.001 andfor others
ps > 0.454). Therefore, these factors could not explain our results.
8|STUDY 5: PROVIDING THE
MODERATING ROLE OF CHILDHOOD SES
The primary purpose of Study 5 is to provide further evidence for the
underlying mechanism, reducing uncertainty through patterned
choices. Individuals differently perceive and seek control over
threatening environments depending on resource abundance or
scarcity in the environment where they grew up (Infurna et al., 2011;
Kraus et al., 2012). Therefore, we test the moderating role of child-
hood SES in the relationship between the perceived threat of
COVID‐19 and pattern‐seeking in a nonfood consumption domain in
the study.
8.1 |Method: Participants, design, and procedure
We recruited 255 participants (52.9% female; M
age
= 40.35, SD =
14.10) from MTurk. We first measured the perceived threat of
COVID‐19 using two items adapted from J. Kim (2020) (e.g., “In your
opinion, is coronavirus (COVID‐19) a serious threat?”)ona7‐point
scale (1 = not at all serious, 7 = very serious, r= 0.781, p< 0.001).
Next, we asked participants to imagine that they were about to
listen to six different songs, including three favorite and three not‐so‐
favorite songs as in Study 1. Participants then selected the sequence
of six choices,
9
as shown in Appendix A. Following this task, Parti-
cipants reported their childhood SES (Cronbach's α= 0.878) using
three items (e.g., I grew up in a relatively wealthy neighborhood,
based on Griskevicius et al., 2011)ona7‐point scale (1 = strongly
disagree, 7 = very agree). Participants also provided their current SES
(e.g., “I don't think I'll have to worry about money too much in the
future,”Cronbach's α= 0.906) using three items. Childhood and
current SES scores were positively correlated (r= 0.272, p< 0.001).
The detailed scale information is provided in Appendix C. Finally, all
participants reported their general attitude toward listening to music
on a 7‐point scale (1 = not much, 7 = very much).
8.2 |Results and implications
As in Study 1, we coded four‐choice outcomes as patterned se-
quential choices (i.e., FFFNNN, NNNFFF, FNFNFN, and NFNFNF)
and 16 others as nonpatterned ones. We used Hayes (2017) macros
process (2017, model #1 with 5000 bootstrapping) to test the
moderating role of childhood SES. The independent variable was the
perceived threat measured. The moderator was the childhood SES
measured, and the dependent variable was whether the choice out-
come was a patterned or nonpatterned sequence.
The interaction effect between the independent variable and the
moderator was significant (β=−0.12, SE = 0.06, t=−2.00, p= 0.045,
95% CI: [−0.237, −0.003]).
10
Specifically, when participants' child-
hood SES was relatively low (i.e., one SD below the average; −1SD),
8
To verify thepattern manipulation,we conducted a pretest(n= 80, 52.5% women).Participants
were assigned to one of high‐and low‐pattern experimental conditions and rated their
perception regarding a given sequence on 7‐point scales (1= not patterned/not atall regular/not
at all predictable, 7 = highly patterned/very regular/very predictable, Cronbach's α= 0.918). The
results showed a significant difference across two conditions (M
_high pattern
= 6.32, SD =1.01 vs.
M
_low pattern
=3.45, SD =1.54,F(1, 78) = 96.65, p<0.001,η
2
= 0.55).
9
Additional 13 participants were excluded from our analysis since their choice outcome was
not three choices for each favorite and not‐so‐favorite category. When we recorded them as
nonpattern choices, the results are similar (−2 Log‐Likelihood = 246.80, β= 0.199, SE = 0.10,
Wald = 3.72, p= 0.054).
10
We conducted bilogistic analysis to test the impact of the perceived threat on choice
patterns. We found a nonsignificant result (−2 Log Likelihood = 296.54, β= 0.084, SE = 0.09,
Wald = 0.79, p= 0.376), which is not parallel with that of Study 1. These inconsistent results
from the two studies may be driven by the different types of choice tasks (i.e., jellybeans vs.
music).
10
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PARK ET AL.

pattern‐seeking was higher as the perceived threat was also high
(β= 0.27, SE = 0.14, z = 2.01, p= 0.044, 95% CI: [0.007, 0.539]). In
detail, the choice pattern was estimated high when their perceived
threat was relatively high (estimated M
_+1SD
= 78.7%) compared to
when the perceived threat was relatively low (M
_−1SD
= 62.6%). In
contrast, we found different results for participants with high child-
hood SES (i.e., one SD above the average; +1 SD). Specifically,
pattern‐seeking was similar regardless of their perceived threat levels
(estimated M
_+1SD
= 50.6% vs. M
_−1SD
= 58.3%, β=−0.11, SE = 0.14,
z=−0.78, p= 0.433, 95% CI: [−0.371, 0.159]), as shown in Figure 2.
An additional Johnson–Neyman test indicated that the significant
point for childhood SES was −1.40 SD (around 19.11% of all partici-
pants) at the 95% threshold.
To control for individual differences in pre‐existing attitudes
toward listening to music, we included participants' attitudes toward
listening to music as a covariate. The interaction effect remained
significant (β=−0.12, SE = 0.06, t=−1.99, p= 0.047, 95% CI: [−0.237,
−0.002]), whereas the covariate was not (β= 0.02, SE = 0.13, t= 0.18,
p= 0.856, 95% CI: [−0.222, 0.267]). Finally, when we repeated our
analysis with current SES, the interaction effect between the per-
ceived threat and the current SES was not significant (β= 0.04, SE =
0.06, z = 0.72, p= 0.473, 95% CI: [−0.075, 0.162]). This finding sug-
gests that the wealth effect could not simply explain the above in-
teraction effect.
9|GENERAL DISCUSSION
The pandemic outbreak represents one of the most significant
threats. To mitigate such a threat associated with infectious disease,
people engage in various adaptive behaviors. These behaviors include
stockpiling and hoarding (Prentice et al., 2021), online searches for
health information (Du et al., 2020), and risk‐averse choices (Rettie &
Daniels, 2021). Along this line of research, we show novel evidence
that people engage in pattern‐seeking in their choices as an adaptive
response when faced with the COVID‐19 threat. People frequently
make repeated choices with a fixed set of items (Wang et al., 2013).
Our research presents the possibility that consumers seek patterns in
sequential, repeated choices amid COVID‐19. Since patterns are
characterized as predictable, certain, and orderly, we contend that
pattern‐seeking is one of the adaptive responses to make sense out
of the crisis.
Across five studies, consumers exhibit sequential patterns in
choice under the perceived threat of COVID‐19. Specifically,
consumershigh(vs.low)inperceived threat displayed specific
sequential patterns in the repeated choices of jellybeans (Study 1).
Similarly, consumers who were experimentally primed with the
threat of COVID‐19 (vs. those in the control group) preferred a
sequentially patterned option in the jellybean choice over a non-
patterned option even when the nonpatterned option was objec-
tively better the patterned option (Study 2). As an underlying
process, the perceived controllability accounts for the obtained
effect (Study 2). Such a tendency to seek a pattern in choice
emerged when consumers evaluated a set of positive and negative
pictures (Study 3), when they evaluated equally valenced options
(Study 4), and when they made a sequential choice for favorite and
nonfavorite songs (Study 5). Notably, the effect of the perceived
threat of COVID‐19 on sequential choices with a set of fixed
options was stronger for consumers with lower (vs. higher) SES
childhoods (Study 5). However, this effect was independent of the
current SES.
9.1 |Theoretical and practical contributions
Our research makes several contributions. First, the current re-
search contributes to the growing literature across multiple research
disciplines on how COVID‐19 influences consumer behavior (see
Table 2). Recent research on cues of infectious diseases demon-
strates that consumers increase preferences for atypical products
relative to typical products (Huang & Sengupta, 2020). Consumers
under infectious disease threats also experience fear and disgust
(Galoni et al., 2020) and express negative responses to unfair price
practices (K. Zhang et al., 2020). While these findings offer im-
portant insights into how consumers respond to infectious disease
cues in general, the novelty of the phenomenon limits the empirical
research on COVID‐19 and decision‐making in marketing. Our re-
search is the first to demonstrate consumers' pattern‐seeking ten-
dencies in choice in a pandemic situation. People may have inherent
preferences for patterns. For example, people express positive at-
titudes toward ordered (vs. chaotic) compositions of flowers
(Todorova et al., 2004). People provide positive evaluations toward
a product advertised in a regular (vs. irregular) visual pattern (Farace
et al., 2020). We contend that the perceived threat or the salience
of the COVID‐19 threat may amplify such a tendency to seek pat-
terns in sequential choices, suggesting a straightforward impact to
several stakeholders such as consumers or marketers under the
pandemic (Viglia, 2021).
FIGURE 2 Results of Study 5
PARK ET AL.
|
11

We further identify the boundary condition of childhood SES for
the effect on pattern‐seeking. Pattern‐seeking was more pronounced
for people with lower‐class childhoods than their higher‐class coun-
terparts. Social class is considered an essential dimension for market
segmentation (Aljukhadar et al., 2021; Kamakura & Mazzon, 2013)
and determines differences in information processing (J. Lee, 2018).
These cognitive differences are manifested strongly under stressful
circumstances as a function of childhood SES (Griskevicius et al.,
2013; Oi & Haas, 2019). People with lower SES childhoods, char-
acterized by a higher level of unpredictability and a lower sense of
control, feel more vulnerable to threatening situations than those
with higher SES childhoods (Mittal & Griskevicius, 2014). Our re-
search suggests that childhood SES may serve as a proxy for why
certain individuals (e.g., those with a lower sense of control) increase
their tendency to seek sequential patterns in choice in times of a
pandemic crisis.
From a managerial perspective, marketers may benefit from our
findings in the context of product bundling. A product bundle com-
prises multiple units of the product in a single package (Simonson,
1999; Wang et al., 2013). For example, a product bundle with variety
(e.g., a package of yogurt with six different flavors) provides con-
sumers with an opportunity to satiate their need for variety‐seeking
and thus would be more attractive, relative to a bundle with non-
variety (e.g., a package of yogurt with one flavor) in times of
COVID‐19. Additionally, marketing practitioners may utilize
consumers' purchase data to identify favorite and nonfavorite
products/services/attributes and incorporate the information when
designing how to bundle them (Rao et al., 2018).
Finally, prior research on repeated choice has focused on the
factors deriving different choice outcomes. However, researchers
paid relatively little attention to the overall sequence of a fixed set of
options consumers use. This study reveals that consumers seek
patterns in their sequential consumption or experience even when a
patterned option is objectively inferior to a nonpatterned option
mainly when disease threat is salient.
9.2 |Future directions
Our research provides avenues for future research. For instance, a recent
study applies implicit theory beliefs (Muncy & Iyer, 2020)anddemon-
strates that consumers who hold an entity theory feel more vulnerable to
COVID‐19 than those who endorse an incremental theory (Y. Zhang
et al., 2021). People with entity beliefs view situations as fixed and in-
evitable, whereas those with incremental beliefs view them as malleable
and dynamic. Further, people with entity (vs. incremental) orientations
exhibit high levels of consistency in behavior (Dweck & Leggett, 1988). If
so, consumers high in entity (vs. incremental) beliefs likely engage in
pattern‐seeking behavior. Thus, additional research may investigate this
possibility in the repeated choice context.
Second, we showed that the effect on pattern‐seeking is mag-
nified for some people (i.e., those with lower childhood SES)
TABLE 2 Summary of COVID‐19 relevant research on consumer behavior and related research disciplines
Articles Research question
Huang & Sengupta (2020) This article examines how exposure to disease‐related cues influences consumers' preference for typical (vs. atypical)
product options.
S. Li, Zhang, et al. (2021) The research finds that a closer (vs. farther) distance to the epicenter associates with lower (vs. higher) perceived risk
of the pandemic, leading to less (vs. more) irrational consumption behaviors.
Park et al. (2021) This study investigates how the COVID‐19 threat increased consumer evaluation of a product with authenticity
appeals in advertisements.
Sarial‐Abi et al. (2021) The research showed that individuals who experience temporary (permanent) restrictions adopt more concrete
(abstract) levels of construal, which results in their preference for products that communicate brand (category)
attributes and shelves that contain only restriction‐related (mixture of restriction‐and no restriction‐related)
products.
Xia et al. (2021) This study investigates the motivational effect of nostalgia induced by aversive and threatening situations (e.g.,
COVID‐19) on new product purchase intentions.
Azer et al. (2021) This study uses netnography and in‐depth interviews to explore social media users’behavioral manifestations toward
the COVID‐19 crisis.
Islam et al. (2021) The research investigates how in the panic situation created by the pandemic, external scarcity stimuli affect the
emotional arousal among people, which in turn influences consumers' impulsive and obsessive buying behaviors.
J. Kim, Giroux, et al. (2020) The current research offers a novel and timely view by examining how communication messages in public service
advertisements can alter the perception of threat under uncertain situations such as the SARS‐CoV‐2 coronavirus
pandemic.
Sembada & Kalantari (2020) The research identifies that low perceived control explains why some tourists still chose to travel despite a pandemic.
J. Kim, Park, et al. (2021) The research examined how and why the perceived threat of COVID‐19 affects consumers to select compromise
options.
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PARK ET AL.

presumably because of their lower sense of control. Although this
finding provides support for our theorizing, future research may re-
plicate the moderating effect of childhood SES by directly measuring
individual differences in personal control across social classes. Re-
latedly, people have often reported their experiences of social iso-
lation during COVID‐19 (Jargon, 2020). In such a situation, people
may differ in their sensitivity to social isolation (e.g., rejection sensi-
tivity; Downey & Feldman, 1996). That is, relatively high (vs. low)
levels of sensitivity to social isolation or threats are likely to lead
people to feel more isolated and threatened and thus increase their
tendency to seek patterns in repeated choices. In addition, future
research may assess SES both objectively and subjectively, thereby
providing managers the opportunities to predict and guide consumer
behavior at a shopping site (Aljukhadar et al., 2021).
Third, future research may consider extending our findings to the
presentation strategy of price information. Research suggests that
certain digits' presentation as price endings affects consumer deci-
sions (J. Kim, Giroux, et al., 2021; Suri et al., 2004). For example,
retailers often use nine‐ending pricing (e.g., $35.39) strategies
(Gaston‐Breton & Duque, 2015; Schindler & Kibarian, 2001). How-
ever, there is the possibility that consumers may prefer ordered or
patterned pricing (e.g., $35.35) when faced with cues of infectious
diseases. Thus, it will be interesting to examine an application of
pricing strategy to consumers' preferences for pattern‐seeking.
Fourth, it may be worthwhile to investigate how cultural or-
ientations influence the effect observed in our studies. We re-
cognizethatsomecountriesimposedmorestrictrulesatthe
outset of the pandemic outbreak than other countries. Thus, there
may be cultural differences in how people perceive infectious
diseases. Recent research shows that cultures with high (vs. low)
levels of uncertainty avoidance (Hofstede, 2003)tendtohave
lower levels of social gatherings in public (Huynh, 2020). Put dif-
ferently, consumers high (vs. low) in uncertainty avoidance may
perceive the pandemic threat to be higher, thus displaying stronger
pattern‐seeking behavior.
Fifth, some results of the empirical studies have some limitations.
For example, the results of Study 2 could be driven by the preference
for the simple repetition rather than the preference for the pattern or
by the preference for the first option under the higher COVID‐19
threat. Future research needs to test these alternative mechanisms.
Furthermore, the percentage of participants who showed pattern‐
seeking was low in Study 3. This result implies that the pattern‐
seeking tendency is generally influenced by the situational influence
of the pandemic as well as by the way choice options are presented.
(e.g., whether choice options are presented simultaneously or one at
a time). Future research may need to investigate this further. Ad-
ditionally, participants in Study 4 read an article about either a heart
attack (in the control condition) or brain damage by COVID‐19 (in the
experimental condition). One may argue that a heart attack may not
be perceived as threatening as brain damage by Covid‐19 because a
heart attack is more relevant to the older populations than the
younger ones. While we described a heart attack as “the largest cause
of natural death in the U.S.”in the control condition, future research
may employ a different manipulation to discern the unique aspects of
COVID‐19 threats from general health threats.
Last, infectious diseases such as COVID‐19 pose economic, so-
cial, and health threats (Campbell et al., 2020). Such threats may
affect various fundamental motives such as personal control, self‐
esteem, belongingness, and meaningful existence (Vignoles et al.,
2006). Our research implies that a loss of control accounts for
pattern‐seeking in multiple choices. However, there may be some
other situations in which people feel a threat to personal control.
Examples include some types of social exclusion (J. Lee et al., 2017),
disorganized environments (Chae & Zhu, 2014), duration of restric-
tions (Sarial‐Abi et al., 2021), and stressful situations (Folkman, 1984).
Additionally, given that COVID‐19 is unique in scale (e.g., transmis-
sion speed, duration, and infection numbers), it is likely that it triggers
various psychological threats including loneliness (Dahlberg, 2021),
resource scarcity (Hamilton, 2021), uncertainty (Stewart, 2021),
construal levels (Sarial‐Abi et al., 2021), and mortality salience (Y. Liu
et al., 2021). Thus, how other relevant situations and psychological
threats affect control and pattern‐seeking would be an interesting
empirical question.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
ORCID
Jooyoung Park http://orcid.org/0000-0001-9626-7356
Jungkeun Kim http://orcid.org/0000-0003-2104-833X
Jihoon Jhang http://orcid.org/0000-0001-9570-4024
Jacob C. Lee https://orcid.org/0000-0002-1410-4711
Jaehoon Lee http://orcid.org/0000-0002-3514-8000
REFERENCES
Aaker, J., & Schmitt, B. (2001). Culture‐dependent assimilation and
differentiation of the self: Preferences for consumption symbols in
the United States and China. Journal of Cross‐Cultural Psychology,
32(5), 561–576.
Aljukhadar, M., Boeuf, B., & Senecal, S. (2021). Does consumer
ethnocentrism impact international shopping? A theory of social
class divide. Psychology & Marketing,38(5), 735–744.
Amir, D., Jordan, M. R., & Rand, D. G. (2018). An uncertainty management
perspective on long‐run impacts of adversity: The influence of
childhood socioeconomic status on risk, time, and social preferences.
Journal of Experimental Social Psychology,79, 217–226.
Azer, J., Blasco‐Arcas, L., & Harrigan, P. (2021). COVID‐19: Forms and
drivers of social media users' engagement behavior toward a global
crisis. Journal of Business Research,135,99–111.
Brengman, M., Willems, K., & Joye, Y. (2012). The impact of in‐store
greenery on customers. Psychology & Marketing,29(11),
807–821.
Campbell, M. C., Inman, J. J., Kirmani, A., & Price, L. L. (2020). In times of
trouble: A framework for understanding consumers' responses to
threats. Journal of Consumer Research,47(3), 311–326.
PARK ET AL.
|
13

Chae, B., & Zhu, R. (2014). Environmental disorder leads to self‐regulatory
failure. Journal of Consumer Research,40(6), 1203–1218.
Clee, M. A., & Wicklund, R. A. (1980). Consumer behavior and psychological
reactance. Journal of Consumer Research,6(4), 389–405.
Dahlberg, L. (2021). Loneliness during the COVID‐19 pandemic. Aging &
Mental Health,25(7), 1161–1164.
Deutsch, D., & Feroe, J. (1981). The internal representation of pitch
sequences in tonal music. Psychological Review,88(6), 503–522.
Douglas, K. M., Uscinski, J. E., Sutton, R. M., Cichocka, A., Nefes, T.,
Ang, C. S., & Deravi, F. (2019). Understanding conspiracy theories.
Political Psychology,40,3–35.
Downey, G., & Feldman, S. I. (1996). Implications of rejection sensitivity
for intimate relationships. Journal of Personality and Social
Psychology,70(6), 1327–1343.
Du, H., Yang, J., King, R. B., Yang, L., & Chi, P. (2020). COVID‐19 increases
online searches for emotional and health‐related terms. Applied
Psychology: Health and Well‐Being,12(4), 1039–1053.
Dweck, C. S., & Leggett, E. L. (1988). A social‐cognitive approach to
motivation and personality. Psychological Review,95(2), 256–273.
Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009).
Fundamental dimensions of environmental risk. Human Nature,
20(2), 204–268.
Enquist, M., & Arak, A. (1994). Symmetry, beauty and evolution. Nature,
372(6502), 169–172.
Farace, S., Roggeveen, A., Villarroel Ordenes, F., De Ruyter, K.,
Wetzels, M., & Grewal, D. (2020). Patterns in motion: How visual
patterns in ads affect product evaluations. Journal of Advertising,
49(1), 3–17.
Fiorillo, A., & Gorwood, P. (2020). The consequences of the COVID‐19
pandemic on mental health and implications for clinical practice.
European Psychiatry,63(1), e32.
Folkman, S. (1984). Personal control and stress and coping processes: A
theoretical analysis. Journal of Personality and Social Psychology,
46(4), 839–852.
Frederick, S., & Loewenstein, G. (2008). Conflicting motives in evaluations
of sequences. Journal of Risk and Uncertainty,37(2), 221–235.
Galoni, C., Carpenter, G. S., & Rao, H. (2020). Disgusted and afraid:
Consumer choices under the threat of contagious disease. Journal of
Consumer Research,47(3), 373–392.
Gaston‐Breton, C., & Duque, L. C. (2015). Utilitarian and hedonic
promotional appeals of 99‐ending prices. European Journal of
Marketing,49(1/2), 212–237.
Griskevicius, V., Ackerman, J. M., Cantú, S. M., Delton, A. W.,
Robertson, T. E., Simpson, J. A., Thompson, M. E., & Tybur, J. M.
(2013). When the economy falters, do people spend or save?
Responses to resource scarcity depend on childhood environments.
Psychological Science,24(2), 197–205.
Griskevicius, V., & Kenrick, D. T. (2013). Fundamental motives: How
evolutionary needs influence consumer behavior. Journal of
Consumer Psychology,23(3), 372–386.
Griskevicius, V., Tybur, J. M., Delton, A. W., & Robertson, T. E. (2011). The
influence of mortality and socioeconomic status on risk and delayed
rewards: A life history theory approach. Journal of Personality and
Social Psychology,100(6), 1015–1026.
Gupta, S., & Gentry, J. W. (2019). ‘Should I Buy, Hoard, or
Hide?’‐Consumers' responses to perceived scarcity.
International Review of Retail, Distribution and Consumer
Research,29(2), 178–197.
Hamilton, R. (2021). Scarcity and coronavirus. Journal of Public Policy &
Marketing,40(1), 99–100.
Hastie, R. (1984). Causes and effects of causal attribution. Journal of
Personality and Social Psychology,46(1), 44–56.
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional
process analysis: A regression‐based approach. Guilford Press
Hill, S. E., Prokosch, M. L., DelPriore, D. J., Griskevicius, V., & Kramer, A.
(2016). Low childhood socioeconomic status promotes eating in the
absence of energy need. Psychological Science,27(3), 354–364.
Hinsch, C., Tang, Y., & Lund, D. J. (2021). Compulsion and reactance: Why
do some green consumers fail to follow through with planned
environmental behaviors? Psychology & Marketing (forthcoming).
Hofstede, G. (2003). Culture's consequences: Comparing values, behaviors,
institutions and organizations across nations. Sage Publications
Huang, Y., & Sengupta, J. (2020). The influence of disease cues on
preference for typical versus atypical products. Journal of Consumer
Research,47(3), 393–411.
Huynh, T. L. D. (2020). Does culture matter social distancing under the
COVID‐19 pandemic? Safety Science,130, 104872.
Hwang, E., Kim, J., Lee, J. C., & Kim, S. (2019). To do or to have, now or
later, in travel: Consumption order preference of material and
experiential travel activities. Journal of Travel Research,58(6),
961–976.
Infurna, F. J., Gerstorf, D., Ram, N., Schupp, J., & Wagner, G. G. (2011).
Long‐term antecedents and outcomes of perceived control.
Psychology and Aging,26(3), 559–575.
Islam, T., Pitafi, A. H., Arya, V., Wang, Y., Akhtar, N., Mubarik, S., &
Xiaobei, L. (2021). Panic buying in the COVID‐19 pandemic: A multi‐
country examination. Journal of Retailing and Consumer Services,59,
102357.
Jargon, J. (2020). Lonely girls: How the pandemic has deepened the
isolation of adolescents. Wall Street Journal.https://www.wsj.com/
articles/lonely-girls-how-the-pandemic-has-deepened-the-isolation-
of-adolescents-11595937600
Jones, M. R., & Zamostny, K. P. (1975). Memory and rule structure in the
prediction of serial patterns. Journal of Experimental Psychology:
Human Learning and Memory,1(3), 295–306.
Kahn, B. E., & Isen, A. M. (1993). The influence of positive affect on
variety seeking among safe, enjoyable products. Journal of Consumer
Research,20(2), 257–270.
Kamakura, W. A., & Mazzon, J. A. (2013). Socioeconomic status and
consumption in an emerging economy. International Journal of
Research in Marketing,30(1), 4–18.
Kay, A. C., Whitson, J. A., Gaucher, D., & Galinsky, A. D. (2009).
Compensatory control: Achieving order through the mind, our
institutions, and the heavens. Current Directions in Psychological
Science,18(5), 264–268.
Kc, D. S., Staats, B. R., Kouchaki, M., & Gino, F. (2020). Task selection and
workload: A focus on completing easy tasks hurts performance.
Management Science,66(10), 4397–4416.
Kim, H. S., & Drolet, A. (2003). Choice and self‐expression: A cultural
analysis of variety‐seeking. Journal of Personality and Social
Psychology,85(2), 373–382.
Kim, H. S., & Sherman, D. K. (2007). “Express yourself”: Culture and the
effect of self‐expression on choice. Journal of Personality and Social
Psychology,92(1), 1–11.
Kim, J. (2020). Impact of the perceived threat of COVID‐19 on variety‐
seeking. Australasian Marketing Journal,28(3), 108–116.
Kim, J., Cui, Y. G., Hwang, E., Franklin, D., & Seo, Y. (2020). “012012 or
111000”: Preference for consumption pattern‐seeking. European
Journal of Marketing,54(9), 2171–2194.
Kim, J., Giroux, M., Gonzalez‐Jimenez, H., Jang, S., Kim, S., Park, J.,
Kim, J. E., Lee, J. C., & Choi, Y. K. (2020). Nudging to reduce the
perceived threat of coronavirus and stockpiling intention. Journal of
Advertising,49(5), 633–647.
Kim,J.,Giroux,M.,&Lee,J.C.(2021).WhendoyoutrustAI?Theeffectof
number presentation detail on consumer trust and acceptance of AI
recommendations. Psychology & Marketing, forthcoming,38,1140–1155.
Kim, J., Lee, J., Jhang, J., Park, J., & Lee, J. C. (2021). The impact of the
COVID‐19 threat on the preference for high versus low quality/
14
|
PARK ET AL.

price options. Journal of Hospitality Marketing & Management
(forthcoming).
Kim, J., & Lee, J. C. (2020). Effects of COVID‐19 on preferences for
private dining facilities in restaurants. Journal of Hospitality and
Tourism Management,45,67–70.
Kim, J., Park, J., Lee, J., Kim, S., Gonzalez‐Jimenez, H., Lee, J., &
Marshall, R. (2021). COVID‐19 and Extremeness Aversion: The Role
of Safety Seeking in Travel Decision Making. Journal of Travel
Research (forthcoming).
Kirk, C. P., & Rifkin, L. S. (2020). I'll trade you diamonds for toilet
paper: Consumer reacting, coping and adapting behaviors
in the COVID‐19 pandemic. Journal of Business Research,117,
124–131.
Kraus, M. W., Piff, P. K., Mendoza‐Denton, R., Rheinschmidt, M. L., &
Keltner, D. (2012). Social class, solipsism, and contextualism: How
the rich are different from the poor. Psychological Review,119(3),
546–572.
Kwon, M., Manikas, A. S., & Barone, M. J. (2021). (Not) Near and dear:
COVID‐19 concerns increase consumer preference for products that
are not “near me”. Journal of Association of Consumer Research
(forthcoming).
Lee, J. (2018). Can a rude waiter make your food less tasty? Social class
differences in thinking style and carryover in consumer judgments.
Journal of Consumer Psychology,28(3), 450–465.
Lee, J., Shrum, L. J., & Yi, Y. (2017). The role of cultural communication
norms in social exclusion effects. Journal of Consumer Psychology,
27(1), 108–116.
Lee, J. C., Hall, D. L., & Wood, W. (2018). Experiential or material
purchases? Social class determines purchase happiness.
Psychological Science,29(7), 1031–1039.
Leeuwenberg, E. L. J. (1971). A perceptual coding language for visual
and auditory patterns. American Journal of Psychology,84(3),
307–349.
Legare, C. H., & Souza, A. L. (2014). Searching for control: Priming
randomness increases the evaluation of ritual efficacy. Cognitive
science,38(1), 152–161.
Levav, J., & Zhu, R. (2009). Seeking freedom through variety. Journal of
Consumer Research,36(4), 600–610.
Li, H., Song, Y., & Xie, X. (2020). Altruistic or selfish? responses when
safety is threatened depend on childhood socioeconomic status.
European Journal of Social Psychology,50(5), 1001–1016.
Li, S., Zhang, Z., Liu, Y., & Ng, S. (2021). The closer I am, the safer I feel:
The “distance proximity effect”of COVID‐19 pandemic on
individuals' risk assessment and irrational consumption. Psychology
& Marketing (forthcoming).
Liu, Y., Lv, X., & Tang, Z. (2021). The impact of mortality salience on
quantified self behavior during the COVID‐19 pandemic. Personality
and Individual Differences,180(October), 110972.
Mantonakis, A., Rodero, P., Lesschaeve, I., & Hastie, R. (2009). Order in
choice: Effects of serial position on preferences. Psychological
Science,20(11), 1309–1312.
Menon, S., & Kahn, B. E. (1995). The Impact of context on variety
seeking in product choices. Journal of Consumer Research,22(3),
285–295.
Miller, S. L., & Maner, J. K. (2012). Overperceiving disease cues: The basic
cognition of the behavioral immune system. Journal of Personality
and Social Psychology,102(6), 1198–1213.
Mittal, C., & Griskevicius, V. (2014). Sense of control under uncertainty
depends on people's childhood environment: A life history theory
approach. Journal of Personality and Social Psychology,107(4),
621–637.
Mittal, C., Griskevicius, V., Simpson, J. A., Sung, S., & Young, E. S. (2015).
Cognitive adaptations to stressful environments: When childhood
adversity enhances adult executive function. Journal of Personality
and Social Psychology,109(4), 604–621.
Morens, D. M., Daszak, P., & Taubenberger, J. K. (2020). Escaping
Pandora's box—Another novel coronavirus. New England Journal of
Medicine,382(14), 1293–1295.
Muncy, J. A., & Iyer, R. (2020). The impact of the implicit theories of social
optimism and social pessimism on macro attitudes towards
consumption. Psychology & Marketing,37(2), 216–231.
Murray, D. R., & Schaller, M. (2016). The behavioral immune system:
Implications for social cognition, social interaction, and social
influence., Advances in Experimental Social Psychology (Vol. 53,
pp. 75–129). Academic Press.
Nisbett, R. E. (2009). Intelligence and how to get it: Why schools and cultures
count. W.W. Norton & Company.
O'Brien, E., & Ellsworth, P. C. (2012). Saving the last for best: A positivity
bias for end experiences. Psychological Science,23(2), 163–165.
Oi, K., & Haas, S. (2019). Cardiometabolic risk and cognitive decline: The
role of socioeconomic status in childhood and adulthood. Journal of
Health and Social Behavior,60(3), 326–343.
Palmwood, E. N., & McBride, C. A. (2019). Challenge vs. threat: The effect
of appraisal type on resource depletion. Current Psychology,38(6),
1522–1529.
Park, J., Kim, J., Lee, D. C., Kim, S. S., Voyer, B. G., Kim, C., Sung, B.,
Gonzalez‐Jimenez, H., Fastoso, F., Choi, Y. K. & Yoon, S. (2021). The
impact of COVID‐19 on consumer evaluation of authentic
advertising messages. Psychology & Marketing (forthcoming).
Pena‐Marin, J., Adaval, R., & Shen, L. (2021). Fear in the stock market:
How COVID‐19 affects preference for high‐and low‐priced stocks.
Journal of Association of Consumer Research (forthcoming).
Povel, D.‐J., & Collard, R. (1982). Structural factors in patterned finger
tapping. Acta Psychologica,52(1‐2), 107–123.
Povel, D.‐J., & Essens, P. (1985). Perception of temporal patterns. Music
Perception,2(4), 411–440.
Prentice, C., Quach, S., & Thaichon, P. (2021). Antecedents and
consequences of panic buying: The case of COVID‐19.
International Journal of Consumer Studies (forthcoming).
Pyszczynski, T. A., & Greenberg, J. (1981). Role of disconfirmed
expectancies in the instigation of attributional processing. Journal
of Personality and Social Psychology,40(1), 31–38.
Rao, V. R., Russell, G. J., Bhargava, H., Cooke, A., Derdenger, T., Kim, H.,
Kumar, N., Levin, I., Ma, Y., & Mehta, N. (2018). Emerging trends in
product bundling: Investigating consumer choice and firm behavior.
Customer Needs and Solutions,5(1), 107–120.
Ratner, R. K., & Kahn, B. E. (2002). The impact of private versus public
consumption on variety‐seeking behavior. Journal of Consumer
Research,29(2), 246–257.
Read, D., Loewenstein, G., & Kalyanaraman, S. (1999). Mixing virtue and
vice: Combining the immediacy effect and the diversification
heuristic. Journal of Behavioral Decision Making,12(4), 257–273.
Restle, F. (1970). Theory of serial pattern learning: Structural trees.
Psychological Review,77(6), 481–495.
Restle, F. (1972). Serial patterns: The role of phrasing. Journal of
Experimental Psychology,92(3), 385–390.
Restle, F., & Brown, E. R. (1970). Serial pattern learning. Journal of
Experimental Psychology,83(1, Pt.1), 120–125.
Rettie, H., & Daniels, J. (2021). Coping and tolerance of uncertainty:
Predictors and mediators of mental health during the COVID‐19
pandemic. American Psychologist,76(3), 427–437.
Sarial‐Abi, G., Ulqinaku, A., & Mokarram‐Dorri, S. (2021). Living with
restrictions: The duration of restrictions influences construal levels.
Psychology & Marketing (forthcoming).
Schindler, R. M., & Kibarian, T. M. (2001). Image communicated by the use
of 99 endings in advertised prices. Journal of Advertising,30(4),
95–99.
Sembada, A. Y., & Kalantari, H. D. (2021). Biting the travel bullet: A
motivated reasoning perspective on traveling during a pandemic.
Annals of Tourism Research,88, 103040.
PARK ET AL.
|
15

Sheth, J. (2020). Impact of Covid‐19 on consumer behavior: Will the old
habits return or die? Journal of Business Research (117, pp. 280–283).
Shiu, E. M. K., Walsh, G., Hassan, L. M., & Shaw, D. (2011). Consumer
uncertainty, revisited. Psychology & Marketing,28(6), 584–607.
Simon, H. A., & Kotovsky, K. (1963). Human acquisition of concepts for
sequential patterns. Psychological Review,70(6), 534–546.
Simonson, I. (1990). The effect of purchase quantity and timing on variety‐
seeking behavior. Journal of Marketing Research,27(2), 150–162.
Simonson, I. (1999). The effect of product assortment on buyer
preferences. Journal of Retailing,75(3), 347–370.
Stewart, D. W. (2021). Uncertainty and risk are multidimensional: Lessons
from the COVID‐19 pandemic. Journal of Public Policy & Marketing,
40(1), 97–98.
Suri, R., Anderson, R. E., & Kotlov, V. (2004). The use of 9‐ending prices:
Contrasting the USA with Poland. European Journal of Marketing,
38(1/2), 56–72.
Thompson, D. V., Banerji, I., & Hamilton, R. W. (2020). Scarcity of choice:
The effects of childhood socioeconomic status on consumers'
responses to substitution. Journal of the Association for Consumer
Research,5(4), 415–426.
Todorova, A., Asakawa, S., & Aikoh, T. (2004). Preferences for and
attitudes towards street flowers and trees in Sapporo, Japan.
Landscape and Urban Planning,69(4), 403–416.
Viglia, G. (2021). What's next? Psychology & Marketing,38(1), 5–6.
Vignoles, V. L., Regalia, C., Manzi, C., Golledge, J., & Scabini, E. (2006). Beyond
self‐esteem: Influence of multiple motives on identity construction.
Journal of Personality and Social Psychology,90(2), 308–333.
Wang, C. S., Whitson, J. A., & Menon, T. (2012). Culture, control, and
illusory pattern perception. Social Psychological and Personality
Science,3(5), 630–638.
Wang, L., You, Y., & Yang, C.‐M. (2020). Restrained by resources: The
effect of scarcity cues and childhood socioeconomic status (SES) on
consumer preference for feasibility. International Journal of Research
in Marketing,37(3), 557–571.
Wang, X., Sun, L., & Keh, H. T. (2013). Consumer responses to variety in
product bundles: The moderating role of evaluation mode.
International Journal of Research in Marketing,30(4), 335–342.
Whelan, J., & Hingston, S. T. (2018). Can everyday brands be threatening?
Responses to brand primes depend on childhood socioeconomic
status. Journal of Consumer Psychology,28(3), 477–486.
Whitson, J. A., & Galinsky, A. D. (2008). Lacking control increases illusory
pattern perception. Science,322(5898), 115–117.
Winterhalder, B. (1986). Diet choice, risk, and food sharing in a stochastic
environment. Journal of Anthropological Archaeology,5(4), 369–392.
Winterhalder, B. (1990). Open field, common pot: Harvest variability and
risk avoidance in agricultural and foraging societies. In E Cashdan
(Ed.), Risk and uncertainty in tribal and peasant economies (pp. 67–87).
Taylor & Francis.
Xia, L., Wang, J. F., & Santana, S. (2021). Nostalgia: Triggers and its role on new
product purchase intentions. Journal of Business Research,135,183–194.
Yoon, S., & Kim, H. C. (2018). Feeling economically stuck: The effect of
perceived economic mobility and socioeconomic status on variety
seeking. Journal of Consumer Research,44(5), 1141–1156.
Zhang, K., Hou, Y., & Li, G. (2020). Threat of infectious disease during an
outbreak: Influence on tourists' emotional responses to disadvantaged
price inequality. Annals of Tourism Research,84, 102993.
Zhang, Y., Mathur, P., & Block, L. (2021). Personality matters during a
pandemic: Implicit theory beliefs influence preparedness and
prevention behaviors. Journal of the Association for Consumer
Research,6(1), 168–177.
Šrol, J., Ballová Mikušková, E., & Čavojová, V. (2021). When we are
worried, what are we thinking? Anxiety, lack of control, and
conspiracy beliefs amidst the COVID‐19 pandemic. Applied
Cognitive Psychology,35(3), 720–729.
How to cite this article: Park, J., Kim, J., Jhang, J., Lee, J. C., &
Lee, J. (2021). The impact of infectious disease threat on
consumers' pattern‐seeking in sequential choices. Psychology
& Marketing,1–20. https://doi.org/10.1002/mar.21602
APPENDIX A
Choice Tasks of Studies 1 and 4
Study 1—Jellybeans Choice
Study 4—Music Choice
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APPENDIX B
Stimuli of Studies 2, 3, and 4—Threat Manipulation
High Covid‐19 threat (Studies 2, 3, and 4) and control conditions
(Study 2)
Control condition for Study 3 and general health risk condition for
Study 4
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APPENDIX C
Stimuli of Study 2—Choice Task
APPENDIX D
Stimuli of Study 3—Choice Task
Initial Information
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First Evaluation Task
Stimuli of Study 3—Choice Task
Second Evaluation Task
Third Choice Task Instruction
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APPENDIX E
Stimuli of Study 4—High‐versus Low‐Pattern Choice
High‐pattern condition
Low‐pattern condition
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