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All the cues we cannot see: How reward-driven distractors render
consumers insensitive to assortment complexity
Sebastian Sadowski
a,*
, Bob M. Fennis
b
, Koert van Ittersum
b
a
Department of Language and Communication, Centre for Language Studies, Radboud University Nijmegen, Erasmusplein 1 6525 HT Nijmegen, the Netherlands
b
Department of Marketing, University of Groningen, Zernike Campus, Duisenberg Building, Nettelbosje 2 9747 AE Groningen, the Netherlands
ARTICLE INFO
Keywords:
Reward-driven distractors
Assortment complexity
Attention
Motivation
ABSTRACT
Consumers face assortments in the retail environment that are more and more complex. This research extends the
current literature on location-based choice behavior by demonstrating how varying assortment complexity im-
pacts consumer choice behavior while shopping and how the presence or absence of reward-driven distractors
(cues that promise a reward yet are unrelated to the choice task) modulate that choice process. We nd that
consumers tend to choose products closer to the center of an assortment when facing non-complex assortments.
At the same time, they shift their choice towards the edge when selecting products from complex assortments.
However, we only observe these effects in the absence of reward-driven distractors. When present, assortment
complexity fails to steer consumers into diverging product locations. We discuss how our ndings might inform
retail practice.
1. Introduction
The present retail environment is one where consumers face abun-
dant possibilities and choices (Chernev et al., 2015). Just a glance at the
list of the world’s largest malls leads to the striking observation that only
one mall from the global top 10 has fewer than 1000 shops, while the
ones that belong to the top 3 house more than 2000 stores. The strategy
of giving consumers a broad scope of options to choose from does not
only pertain to shopping malls and retail chains; niche actors also try to
excel in the market by offering large assortments differing in complexity
(dened here as the number of identiable attributes in any product,
assessable before consumption; Greifeneder et al., 2010). For instance,
in Powell’s Book Store in Portland, Oregon, consumers can browse more
than 1 million books from different publishers and authors, with
different types of binding, covers, illustrations, or lack thereof. This
abundance of choices strains consumers and their limited attentional
and motivational resources (Chan, 2015; Messner & W¨
anke, 2011),
making it increasingly difcult for them to nd their way through such
vast and complex assortments.
Research on the impact of assortment complexity on consumer
decision-making (Chernev et al., 2015) demonstrates that the
complexity of assortments can have detrimental effects, such as choice
deferral (Huffman & Kahn, 1998), lower condence in selected options
(Chernev, 2006), or decreased satisfaction (Diehl & Poynor, 2010;
Messner & W¨
anke, 2011). Nonetheless, the impact of complexity is not
always unequivocal. Research has also identied benets of more
complex assortments for consumers, such as the greater likelihood of
nding the most desired options (Oppewal & Koelemeijer, 2005) or
yielding more stimulating choice sets (Boyd & Bahn, 2009). Interest-
ingly, scant research has explored the consequences of assortment
complexity for consumer choice strategies (for notable exceptions, see
Boyd & Bahn, 2009; Chernev, 2003; Messner & W¨
anke, 2011). Our
research builds on previous work by showing how varying assortment
complexity induces divergent processing modes while shopping (e.g.,
McElroy & Mascari, 2007). These different processing modes may affect
consumers’ choice strategies: holistic processing (scanning the assort-
ment as a whole, in parallel; Pellicano & Rhodes, 2003) in case of non-
complex assortments and analytic processing (considering alternatives
one at a time, sequentially; McElroy & Mascari, 2007) in case of complex
assortments. We demonstrate the downstream consequences of these
processing modes for choice strategies: holistic processing should be
reected in choosing products located more centrally in the assortment.
In contrast, analytic processing translates into choices of less centrally
located products. We propose this as the default. However, we addi-
tionally posit and demonstrate that this pattern is observed only when
shoppers are not distracted by rewards they can obtain (Bijleveld et al.,
* Corresponding author at: Centre for Language Studies, Radboud University, The Netherlands.
E-mail address: sebastian.sadowski@ru.nl (S. Sadowski).
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
https://doi.org/10.1016/j.jbusres.2025.115227
Received 11 November 2022; Received in revised form 3 January 2025; Accepted 28 January 2025
Journal of Business Research 190 (2025) 115227
Available online 11 February 2025
0148-2963/© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
2012) and which preoccupy their minds while making choices from
given assortments. Hence, the effect of assortment complexity on choice
strategies is attenuated when reward-driven distractors are present
rather than absent.
Our main contributions are twofold. First, we contribute to the
literature on location-based choice behavior, exploring which products
consumers tend to choose from a given set of options—the central
(Christenfeld, 1995; Valenzuela & Raghubir, 2009) or less central op-
tions (Ert & Fleischer, 2016). We explore patterns in location-based
choice behavior when consumers face assortments of varying
complexity, investigating how the complexity of the presented set of
options makes them select more or less central options within a given
assortment. Moreover, we also demonstrate that varying levels of
complexity trigger divergent information processing modes: consumers
tend to process assortments holistically when the assortments are non-
complex. At the same time, they engage in analytic processing when
the assortments are complex.
Second, we demonstrate that these effects are contingent on the
presence or absence of reward-driven distractors (Rusz et al., 2020)
unrelated to the choice task at hand. Rewards could be present within
the shopping context, for instance, through the possibility of obtaining
extra points in a loyalty program, a pop-up ad for an online casino in a
webshop, or the constant notications from various social media plat-
forms on one’s smartphone. They can also be present outside of the
shopping context, for instance, in the form of the last episode of a hit TV
series waiting to be watched back at home or the thought of a possibly
successful rst date later in the day (Rusz et al., 2020). We extend
previous work showing that pursuing a choice strategy when faced with
assortments of varying levels of complexity requires attentional re-
sources and working memory capacity that is taken up by responding to
the reward cue when present. As such, we demonstrate how being
preoccupied with reward-driven distractors and related thoughts at-
tenuates the impact of assortment complexity on consumer choice
behavior. A recent meta-analysis (Rusz et al., 2020) demonstrates how
attending to reward cues unrelated to the currently activated goal (i.e.,
choosing a product from a given assortment in our article) impairs
cognitive performance and consumes attentional resources. As a result,
we expect that the cognitive consequence of being distracted by present
rewards while searching for the most preferred product in an assortment
is that the consumer will not adapt their choice strategy to the level of
complexity that the assortment represents (holistic or parallel when
complexity is low vs. sequential or analytical when complexity is high).
We will also argue why this is critical for retailers to understand.
2. Assortment effects in the marketplace
2.1. Assortment characteristics and Location-Based choice behavior
To date the assortment literature has focused predominantly on such
topics as choice overload (Messner & W¨
anke, 2011), assortment size
(Aurier & Mejía, 2020; Chernev, 2003), perceived variety (van Herpen &
Pieters, 2002), assortment organization (Lamberton & Diehl, 2013;
Langner & Krengel, 2013) or assortment presentation (Kahn, 2017;
Townsend & Kahn, 2014). One underlying driver ties all these seemingly
divergent factors together –the perceived complexity of the assortment.
Nonetheless, scant research has been dedicated to exploring the
connection between the complexity of assortments and choices of
products from specic locations within an assortment.
Prior literature has seen location-based choices predominantly as a
meaningful cue (Valenzuela & Raghubir, 2015) that signals specic
properties of a product purely based on its location within an assort-
ment. The central location of the product conveys, for instance, its
popularity (Valenzuela & Raghubir, 2009), which further boosts product
evaluations and increases the probability of choosing products located
more centrally. Inman et al. (1990) showed that products placed at an
end-of-aisle display are perceived as discounted. Moreover, consumers
judge the products located at the bottom (vs. top) and on the left-hand
(vs. middle and right-hand) side of a display to be cheaper and of
lower quality (Valenzuela & Raghubir, 2015). Despite identifying the
inferences consumers make while choosing from specic assortments,
the literature on location-based choices has not yet explored how
assortment complexity affects how consumers make choices as a func-
tion of such inferences.
The impact of assortment complexity on consumer decision-making
has been primarily investigated through the lens of assortment size,
often equating larger assortments with higher complexity (e.g., Oppewal
& Koelemeijer, 2005). Consumers generally prefer larger assortments
due to a higher likelihood of nding more desired options (Borle et al.,
2005). Ma (2016) demonstrates that this preference can translate into
increased revenues, particularly for online retailers, due to consumers’
increased efciency in processing large online assortments. Research
offers more qualifying factors, allowing us to grasp better and contex-
tualize this preference for bigger assortments, going beyond the multi-
channel decisions of the retailers. Consumers were shown to prefer
larger assortments when asked to make choices in the present moment,
while this preference disappeared for choices made later in the future
(Goodman & Malkoc, 2012). Moreover, Whitley et al. (2018) demon-
strated that consumers value large assortments more, particularly when
driven by hedonic (vs. utilitarian) purchase motivation. Under this
condition, consumers judge their preferences as more unique and
anticipate that making a choice will be more difcult. The possibility of
selecting from a broader scope of products is expected to increase the
probability of nding a preference-matching option within the assort-
ment. Aydinli et al. (2017) explained this consumer preference from a
novel, affect-based perspective. They posited that consumers derive
greater experience utility from reviewing larger assortments—choosing
products from bigger assortments is more pleasing.
Apart from focusing on perceptions of large versus small assortments
(e.g., Chernev, 2006), research also demonstrates which specic options
consumers choose when selecting products from larger assortments. The
broad scope of ndings shows that choosing more complex, larger as-
sortments is qualitatively different from selecting products from a
smaller set of available options. For instance, consumers tend to select
more easily justiable options while choosing products from large as-
sortments (Sela et al., 2009), leaning more towards the virtue option
than vice options. Moreover, selecting products from larger assortments
might result in lower satisfaction with the selected product due to dis-
conrmation of overly positive prior expectations (Diehl & Poynor,
2010). The lower satisfaction following the choice from a larger
assortment dissipates nonetheless when consumers do not deliberate
(Messner & W¨
anke, 2011) or when they are characterized by higher
assessment motivation (i.e., innate motivation to assess available
choices to improve decision-making; Mathmann et al., 2017).
As the abovementioned literature suggests, research so far has
focused either on the attributes of products (e.g., Sela et al., 2009),
motivational states (Mathmann et al., 2017), or information processing
(e.g., Messner & W¨
anke, 2011) to understand the decision-making of
consumers while facing complex assortments. Less attention appears to
have been dedicated to choice strategy as a function of assortment
complexity. Moreover, the impact of the visual complexity of assort-
ments while keeping the assortment size stable on choice strategy has
been largely overlooked. The question then arises as to which patterns in
location-based choice could be expected when consumers select prod-
ucts from complex versus less complex assortments.
2.2. Assortment Complexity, Location-Based choice behavior and rewards
Prior literature has claried the connection between how consumers
process assortments and location-based choice behavior through the
concept of reachability (Bar-Hillel, 2015)—the object that is the easiest
to reach is supposed to be chosen. Notably, reachability can operate on
both physical and mental levels. While processing non-complex
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
2
assortments, consumers are proposed to choose products closer to the
center of assortments because the item located in the middle or close to
the middle is physically the closest to them. Moreover, the choice task
does not require a lot of mental (processing) effort from the chooser.
They can process the assortment holistically, attending to more products
at once. Other expectations could be derived for complex assortments. In
the case of complex assortments, consumers tend to process the avail-
able options analytically, sequentially, starting from the edge (e.g., top
left) of the assortment (Treisman, 1998). Consequently, when they select
products from complex assortments, less central choices located more
towards the edge are most likely since the edge options are the ones that
are the most "reachable" mentally. Consumers start processing available
options from the edge and continue, sometimes engaging with pairwise
comparisons, until they nd the most preferred offering. In this way,
they process the assortment analytically, carefully scrutinizing available
options one by one.
Depending on assortment complexity, these differential processing
strategies might be further inuenced by any reward-driven distractors
present in consumer minds while searching for the most desirable option
in a given assortment. Reward cues and the act of pursuing rewards are
ubiquitous in the marketplace (e.g., Simmank et al., 2015; Wadhwa
et al., 2008). Consumers are often preoccupied while shopping with
collecting extra points in the loyalty programs (Stourm et al., 2015),
attending to the numerous possibilities of getting cashback or rebates
(Vana et al., 2018), or are motivated to obtain early access to special
promotions and personalized discounts (Pizzi et al., 2022). Moreover,
gamied apps and social media contests give them even more oppor-
tunities to obtain rewards and attend to miscellaneous reward cues
during shopping trips (Tobon et al., 2020). Additionally, outside of the
shopping context, reward cues can further distract consumers from
selecting the most preferred products from given assortments. Even such
subtle cues as the ‘Tudum’ sound of Netix or the smell of freshly baked
croissants accidentally encountered on the way to the supermarket can
activate reward-related thoughts and further interfere with making
choices from various assortments (Rusz et al., 2020). Similar expecta-
tions have been conrmed by Kim et al. (2022), who have shown that
consumers experiencing time pressure do not engage in elaborate pro-
cessing and are more likely to choose products positioned in the center.
Notably, we focus on the differential information processing induced
by assortment complexity and investigate whether and to what extent
reward-driven distractors interfere with such differential processing
modes (Rusz et al., 2020). While making decisions in the retail setting,
consumers need rst to make sense of the environment and available
options. Such sense-making entails not only seeing specic contextual
cues furnished in the retail environment but also sufciently processing
them so that they become inuential for subsequent decision-making,
specically when they are complex (Greifeneder et al., 2010). Two
critical factors determining whether diverging processing modes will be
triggered by assortment complexity are the supply of attentional re-
sources consumers have at their disposal and/or the motivational drive
to process the complexity (Shah & Kruglanski, 2002). While processing
non-complex assortments holistically is relatively effortless (Pellicano &
Rhodes, 2003), analytic processing requires more in-depth processing of
the attributes for each product. Additionally, it frequently involves
pairwise comparisons of the most salient attributes between products
(McElroy & Mascari, 2007). The activation of reward-related thoughts
has been broadly shown in previous research to impair cognitive re-
sources (Hickey et al., 2010; Libera et al., 2011). People have problems
ignoring reward cues in the environment even when explicitly instruc-
ted to do so (Anderson et al., 2011). Additionally, reward-related cog-
nitions are highly potent in consuming attentional resources, which has
been further corroborated by a recent large-scale meta-analysis (Rusz
et al., 2020). They also redirect consumer motivation towards instant
reward acquisition (Ryan & Deci, 2000). Therefore, due to this pull of
resources towards such motivational distractors, we expect that the
impact of assortment complexity on consumer choice behavior will be
attenuated when consumers simultaneously pursue any potential re-
wards, having a chance to obtain them shortly. When their minds
become ‘hijacked’ by reward-driven distractors, we expect varying
complexity levels will not steer consumers towards different locations (i.
e., more central for non-complex assortments and less central for com-
plex assortments). While distracted by reward-related thoughts, con-
sumers are more likely to fall back on the inferences they make during
the decision process (e.g., going for the central option appears to be the
most popular; Valenzuela & Raghubir, 2009). Moreover, the central
options are also the ones to capture their attention rst, which has been
conrmed by a plethora of eye-tracking studies (Atalay et al., 2012;
Chandon et al., 2009; Motoki et al., 2021; Orquin & Mueller Loose,
2013). Contrastingly, the effects of complexity on the location of choice
are likely to occur when consumers are liberated from reward-related
cognitions, after attaining the rewards, both able and motivated to
process more complex assortments in place of merely seeing them. More
formally,
H
1
: The presence of reward-driven distractors will moderate the
impact of assortment complexity on the consumer location-based
choice behavior:
H
1A
: The presence of reward-driven distractors will attenuate the
impact of complexity on the location of the most preferred product
H
1B
: The absence of reward-driven distractors will make consumers
more sensitive to changes in assortment complexity so that non-
complex assortments will induce product choices that are more
centrally located. In contrast, complex assortments will shift con-
sumer choices further from the center.
3. Outline of experiments
Across four experiments, we demonstrate how assortment
complexity inuences consumer choice behavior. Moreover, we show
how this effect of assortment complexity depends on the presence of
reward-based distractors. Across all experiments, we vary assortment
complexity following Greifeneder et al. (2010) by increasing multi-
attribute differences between options. To vary the presence of reward-
driven distractors, we asked participants to select their most preferred
products from a given assortment before or after they had obtained a
reward.
Experiment 1 implements an externally valid operationalization of
the presence of rewards daily encountered by consumers that helps us to
generalize our results to real-life settings—we invited to participate in
our experiment people who were either feeling hungry, heading to a
restaurant to eat, or people who had already eaten. Hunger constitutes
an intense, often even overpowering, motivational state that activates
the general reward system in human beings, guiding their attention and
actions towards immediate satisfaction of this state (Wadhwa et al.,
2008). In this eld experiment, we show that the presence of reward-
driven distractors (i.e., going to the restaurant to eat) attenuates the
impact of assortment complexity on choice behavior—diverging levels
of assortment complexity do not steer consumers towards different
product locations when consumers are distracted by reward-related
cognitions. Additionally, consumers act upon the complexity of assort-
ments while making choices when reward-driven thoughts are no longer
present (i.e., food has been eaten). Under these conditions, exposure to
non-complex assortments steers consumer choices toward the center of
the assortment. In contrast, consumers tend to select products located
further from the center of the assortment in case of complex assortments.
Experiments 2A and 2B further strengthen our empirical investigation
while taking care of confounds and making choices consequential.
Experiment 3 provides evidence for the process behind the joint effect of
assortment complexity and reward-driven distractors on the location of
most preferred choices. Across the four experiments, we demonstrate
our ndings using different product categories. Moreover, we demon-
strate our effects for two different types of rewarding stimuli—food
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
3
when hunger is experienced (Experiment 1) or monetary rewards
(Experiment 2A-3). Additionally, we rule out mood across all experi-
ments as a possible alternative explanation for our ndings.
Across all experiments, we report all data exclusions and all condi-
tions. We exclude all participants with missing data. Moreover, in Ex-
periments 2A-3, we implemented an attention check (e.g., in Experiment
2B: Please evaluate the quality of this survey. This is an attention check,
please select the response ’Moderately Good’.; Oppenheimer et al., 2009).
We excluded respondents who did not provide the correct answer to this
question. This approach resulted in excluding 36 participants for
Experiment 2A, 23 participants for Experiment 2B and 94 participants
for Experiment 3. These exclusions did not signicantly impact the
patterns of the core interactions we present across all studies.
4. Experiment 1
In the rst experiment, we focus on realistic instances of rewards
preoccupying consumer minds daily (Xu et al., 2015). To this end, we
approached consumers at a fast-food restaurant, asking them to partic-
ipate in a small study involving product choices either before they or-
dered food (i.e. when reward-driven distractors were present) or after
they had ordered and consumed their food (i.e. when reward-driven
distractors were absent). In line with our notions, we expected that
responsiveness to assortment complexity was attenuated when the goal
of satisfying one’s hunger was still active. However, when the rewards
have already been obtained, the effect of complexity should resurface,
even when assessed in the ‘noisy, real-world context’ outside the lab.
For our rst experiment, we used a mixed design. We assumed a
small to medium effect size based on previous literature exploring
assortment effects in the marketplace (Chernev et al., 2015) to deter-
mine our sample size. Thus, we submitted an effect size between small
and medium (
η
2
=0.035) to an a priori power analysis for within-
between interactions in repeated measures designs (Faul et al., 2009),
setting power to 0.80 at
α
=0.05. As a result, we obtained a minimum
required sample size of 58 participants for the target 2-way interaction.
Therefore, we concluded that a more modest sample size is still adequate
due to the mixed design of our experiments and the expected small to
medium effect size. We decided to use this sample size as a required
minimum. We continued data collection after reaching this minimum for
as long as our resources in terms of budget and time allowed.
4.1. Participants and design
Ninety-one participants (59.4 % male; M
age
=26.00, SD =10.27)
included in the nal sample were invited to provide their responses in a
2 (assortment complexity: non-complex vs. complex) ×2 (reward-
driven distractor: present vs. absent) mixed design, in which we
manipulated the presence of distractors as a between-subjects and
assortment complexity as a within-subjects factor.
1
4.2. Procedure
Participants were approached with a survey either before they
consumed a meal at a fast-food restaurant (distractor present) or after
nishing their meal (distractor absent). Participants were presented
with two assortments: non-complex (donuts) and complex (bread
spreads; see below for more details). Each assortment contained 99
options, arranged in a 9 ×11 grid, with 4 or 3 repetitions of the same
item (see Appendix). The assortments were displayed sequentially, and
we counterbalanced the order of the presentation of both assortments.
Moreover, we designed ve different versions of each assortment to
randomize each product’s location. Participants were asked to choose
one item from one version of each assortment (non-complex and com-
plex). The assortments and the questions that followed were presented
on paper.
4.3. Assortment complexity
We varied the assortment complexity following previous literature
(Greifeneder et al., 2010). Hence, the non-complex assortment con-
tained donuts, varying only on one attribute – coating. In contrast, the
complex assortment (bread spreads) varied products on more attributes
– brand, color, shape, font, and avor. We selected foreign brands that
would be deemed unfamiliar to our participants, which we ascertained
via our funneled debrieng.
4.4. Centrality scores
Our core dependent variable was the centrality of choice. We used a
measure of distance from the center that considers both vertical and
horizontal centrality. Since the assortments were grids composed of
smaller quadrants, and each quadrant constituted a separate product, we
coded in our data how many quadrants from the center a chosen product
is located. Locations more horizontally or vertically proximal to the
center received higher centrality scores than product locations located
diagonally from the center. Scores varied between 1 and 10 (1 =edge
option preferred, 10 =central option preferred, M =6.36, SD =2.23).
4.5. Manipulation checks
We measured perceived complexity by asking, How complex was the
assortment of donuts/bread spreads that you just saw? (nine-point scale; 1
=Not complex at all, 9 =Very complex; M =4.53, SD =2.15). In
addition, to validate our manipulation of reward-driven distractors, we
asked participants how hungry they felt at that particular moment in
time (nine-point scale; 1 =Not hungry at all, 9 =Extremely hungry, M =
4.84, SD =2.82).
4.6. Additional measures
Because positive mood is associated with broadened attentional
scope (Paul et al., 2021) and enhanced attention to rewarding stimuli
(Tamir & Robinson, 2007), we measured mood (nine-point scales, with
bad/good, sad/happy, and displeased/pleased items;
α
=0.88; Aarts &
Dijksterhuis, 2003; M =6.92, SD =1.45), to rule out this alternative
explanation.
5. Results
5.1. Manipulation checks
Corroborating our manipulations, participants perceived the com-
plex assortments as more complex (M =5.03, SD =2.11) than the non-
complex ones (M =4.02, SD =2.09, t(90) =3.38, p <0.01, d =0.35).
Next, participants reported that they felt more hungry before they ate at
a fast food restaurant (M =6.80, SD =1.49) than after having eaten
there (M =2.91, SD =2.47; t(74.17) =9.13, p <0.01, d =1.92).
5.2. Main analyses
A mixed-model ANOVA with the reward-driven distractor as a
between-subjects factor and assortment complexity as a within-subject
factor revealed a main effect of assortment complexity on product cen-
trality score (F(1, 89) =5.49, p =0.02,
η
2
=0.06). Participants selected
products located closer to the center of the assortment when they chose
products from non-complex (M =6.65, SD =2.13) than complex as-
sortments (M =6.07, SD =2.31). Furthermore, we identied a
1
Randomization checks showed that age and gender were equally distrib-
uted across the cells: Age: t(1, 79.045) =1.44, p =0.15; Gender:
χ
2
(1, 91) =
1.98, p =0.20.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
4
signicant interaction between the assortment complexity and the
presence of distractors (F(1, 89) =5.07, p =0.03,
η
2
=0.05). Additional
simple-effects analysis showed that the interaction was driven by par-
ticipants assigned to the distractor-absent condition who had already
satised their hunger. Participants who had already eaten and were
selecting products from a non-complex assortment were more likely to
choose products from the center of the assortment (M =6.54, SD =2.12)
than those who had already eaten and were choosing products from a
complex assortment (M =5.41, SD =2.32; F(1, 89) =10.67, p <0.01,
η
2
=0.11), conrming H1B. When the reward-related cognitions were still
active, and participants had not yet ordered their meal, the complexity
of the assortment did not lead participants to choose products from
different locations when hunger was still not satised (M
non-complex
=
6.76, SD =2.15; M
complex
=6.73, SD =2.11; F <1, see Fig. 1), in line
with H1A. Moreover, our manipulation of reward-driven distractors did
not inuence mood (F <1).Fig. 2.Fig. 3..
6. Discussion
The rst experiment provided preliminary evidence supporting our
hypotheses. We demonstrated that consumers become responsive to
assortment complexity, particularly when no motivational distractors
are present. Under these conditions, consumers were more likely to
choose products closer to the center of the assortment for non-complex
assortments. At the same time, they selected products closer to the edge
of the assortment while choosing from a complex assortment.
7. Experiment 2A
In Experiment 2A, we strengthened the evidence for the hypothe-
sized effects by focusing on ruling out possible alternative explanations.
One possible confounding variable that could lead to the presented ef-
fects besides the complexity of the assortments is the presence of textual
information in our earlier manipulations of complexity and the absence
thereof for non-complex assortments. The presence of textual informa-
tion was shown in previous research to induce analytic processing
(Townsend & Kahn, 2014). Additionally, we made the choices conse-
quential in subsequent experiments, increasing our ndings’ external
validity (e.g., Lamberton & Diehl, 2013). For the following study, we
developed two assortments following the literature on assortment
complexity (Broniarczyk & Hoyer, 2010). Both assortments consisted of
different sweets (donuts, cupcakes, mini cakes) varying in color, shape,
coating, and assumed avor (see Appendix for examples of both as-
sortments). We varied the organization of options to manipulate com-
plexity—in the non-complex assortment, we organized the sweets, while
in the complex assortment, the sweets were randomly presented.
Further, in Experiment 2A, we changed the design to tackle potential
issues that might be identied in Experiment 1. First, we employed a
between-subjects design to minimize the chance of participants guessing
the purpose of our investigation. Further, we followed the procedure of
Gu and Wu (2023) to make the choices consequential. We informed
participants that each sweet had the same price tag in the supermarket
and cost
€
0.85. We asked them to pick a product that appeals to them the
most and which they would like to purchase if they saw it in a super-
market. We subsequently informed them that 25 % of all participants
would obtain the sweets they selected from the assortment. In this way,
we wanted to increase the chances of participants selecting the products
that they would like to purchase if they had a chance. We debriefed
participants at the end and offered 25 % of randomly selected partici-
pants
€
0.85 as a bonus payment on top of their participation fee.
To determine the sample size, we used similar calculations as before,
assuming small to medium effect sizes based on previous literature
exploring assortment effects in the marketplace (Chernev et al., 2015).
We submitted an effect size between small and medium (
η
2
=0.035) to
an a priori power analysis for between-subjects designs, setting power to
0.95 at
α
=0.05. As a result, we obtained a minimum required sample
size of 361 participants for the target 2-way interaction. We aimed to
exceed this number of respondents within the possibilities of our budget
to also allow for possible exclusions of participants according to our
exclusion criteria presented above.
7.1. Participants and design
Four hundred seventeen participants (58.8 % male, M
age
=31.41, SD
=10.16) included in the nal sample were recruited via the Prolic
platform to provide their responses in a 2 (assortment complexity: non-
Fig. 1. Choice Centrality (Non-Complex vs. Complex Assortments). Note. Error
bars denote one standard error around the mean.
Fig. 2. Choice Centrality (Non-Complex vs. Complex Assortments). Note. Error
bars denote one standard error around the mean.
Fig. 3. Choice Centrality (Non-Complex vs. Complex Assortments). Note. Error
bars denote one standard error around the mean.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
5
complex vs. complex) ×2 (reward-driven distractor: present vs. absent)
between-subjects design experiment.
2
7.2. Procedure
While designing Experiment 2A, we followed the Monetary Incentive
Delay (MID) task paradigm to experimentally induce concurrent reward
pursuit (e.g., Gable & Harmon-Jones, 2010; Sadowski et al., 2020). In
this paradigm, reward-driven distractors are operationalized by partic-
ipants either anticipating (i.e., distractor present) or attaining (i.e.,
distractor absent) a monetary reward as a function of task performance.
Participants were informed that they would participate in an
experiment composed of two core parts: one testing their numerical
skills and the other allowing them to select a product from a given
assortment. We used the numerical skills task to implement reward-
driven distractors. During the numerical skills tasks, participants were
asked to complete seven trials of the modied anker task (Huntsinger,
2012), indicating a specic number (e.g., 7th, 11th) in long sequences of
numbers (for instance, composed of 25 elements). Participants were told
they would have a chance to earn an extra
€
1 on top of their partici-
pation fee in case of satisfactory performance in the anker task. The
satisfactory performance entailed sufcient accurate and quick re-
sponses across different trials. Ultimately, all of the participants ob-
tained the bonus payment.
Respondents in the distractor-present condition heard about the
possibility of obtaining the bonus payment depending on their perfor-
mance were requested to select the most preferred product from a given
assortment, and only afterward completed seven trials of the anker
task. Participants assigned to the distractor-absent condition rst
completed the trial of the anker task. They obtained feedback about
their performance (their performance was satisfactory, and they will
obtain
€
1 on top of their participation fee), and only afterward were they
asked to select a product. We again exposed participants to one of two
different assortments: non-complex (organized assortment of sweets)
and complex (disorganized assortment of sweets). Each assortment was
composed of 96 options, arranged in an 8 ×12 grid, with 4 repetitions of
each item (see Appendix). We used four different versions of each
assortment to randomize product locations that different participants
see. Participants were requested to choose a product from a given
assortment, either complex or non-complex.
7.3. Assortment complexity
We manipulated assortment complexity based on previous research,
exposing participants in the non-complex condition to one of the four
organized assortments. At the same time, respondents assigned to the
complex condition chose their most preferred option from a disorga-
nized assortment (Broniarczyk & Hoyer, 2010).
7.4. Centrality scores
Our core dependent variable, also this time, was the centrality of
choice. The scores for centrality were coded based on the distance
measure used in the previous experiment as well, with scores varying
between 1 and 13 (1 =edge option preferred, 13 =central option
preferred, M =8.00, SD =3.29; see Appendix for an illustration of
implemented centrality coding).
7.5. Manipulation checks
We measured the assortments’ perceived complexity by asking, How
complex do you perceive the assortment of sweets to be? (Scheibehenne
et al., 2009; 0 =Not at all complex, 100 =Extremely complex; M =
40.35, SD =24.82).
7.6. Additional measures
We measured mood in line with the previous experiment (nine-point
scales, with bad/good, sad/happy, and displeased/pleased items;
α
=
0.83; Aarts & Dijksterhuis, 2003; M =5.70, SD =1.34). Furthermore, we
also collected responses to the attention check (Oppenheimer et al.,
2009) after the assortment task, embedded among other scales.
8. Results
8.1. Manipulation checks
The analysis of manipulation checks indicated that the complex as-
sortments were seen as more complex (M =43.84, SD =25.08) than the
non-complex assortments (M =36.91, SD =24.13; t(415) =-2.87, p <
0.01, d =-0.28).
8.2. Main analyses
We run a between-subjects ANOVA with the presence of reward-
driven distractors and assortment complexity as two independent vari-
ables and the location of the most preferred product as the dependent
variable. This analysis resulted in a main effect of the assortment
complexity on the centrality of the selected product; F(1, 413) =18.36,
p <0.01,
η
2
=0.04. Participants selected products that were located
closer to the center when they made choices from non-complex assort-
ments (M =8.68, SD =2.85) in comparison to participants selecting
products from complex assortments (M =7.32, SD =3.57). Moreover,
the two-way interaction of interest between the assortment complexity
and the presence of reward cues was signicant, F(1, 413) =4.74, p =
0.03,
η
2
=0.01). We further explored which differences in means be-
tween the conditions contribute to the signicance of this interaction
effect. The results suggest that participants’ behavior in the distractor-
absent condition drove the observed interaction effect. Participants
who have already obtained rewards moved further away from the center
of the assortment while selecting the most preferred product from the
complex assortment (M =6.76, SD =3.69) in comparison with those
who were choosing from the non-complex assortment (M =8.79, SD =
2.91; F(1, 413) =21.34, p <0.01,
η
2
=0.05), conrming H1B. This
effect was attenuated in the in the distractor-present condition (F (1,
413) =2.17, p =.14).
We have run an additional between-subjects ANOVA analysis with
mood as a control variable. This covariate exerted a signicant effect on
our dependent variable (F(1, 412) =5.79, p =0.02,
η
2
=0.01). Yet, the
interaction between the presence of reward-driven distractors and the
complexity of the assortment remained signicant (F(1, 412) =5.90, p
=0.02,
η
2
=0.01).
9. Discussion
Experiment 2A replicated our ndings from Experiment 1, further
addressing potential confounds by changing the design to between-
subjects, making choices consequential, and rening our manipula-
tions of assortment complexity through, for instance, removing textual
information. In Experiment 2B, we aimed to replicate the ndings of
Experiment 2A with a slightly adjusted manipulation of assortment
complexity.
10. Experiment 2B
In the next study, we further focused on a different manipulation of
complexity, moving the complex and non-complex assortment even
further apart. Research has found that color can contribute to visual
2
Randomization checks showed that age and gender were equally distrib-
uted across the cells: Age: F(3, 413) <1, Gender:
χ
2
(3, 410) =3.54, p =0.32.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
6
richness (i.e., Corchs et al., 2016; Creusen et al., 2010; Orth & Crouch,
2014) and, as such, to a sense of complexity. Therefore, for the following
two studies, we developed assortments using color only for the complex
but not non-complex conditions (for which we used stimuli presented in
grayscale). Thus, for this study, we used the same assortments as in
Experiment 2A (various sweets—donuts, cupcakes, mini cakes—varying
in color, shape, coating, and assumed avor), using the version with
varying color for the complex condition, while transforming the
assortment into grayscale for the non-complex condition. Furthermore,
we organized the options to decrease the perceptions of complexity
further (see Appendix for examples of both assortments).
We conducted a pre-test to conrm that this manipulation success-
fully impacted perceived complexity. We recruited 200 respondents (62
% male, M
age
=38.13, SD =9.18) on the Amazon MTurk platform. We
measured complexity by asking participants, How complex do you
perceive the assortment of sweets to be? (0 =Not at all complex, 100 =
Extremely complex; M =70.32, SD =20.92). The complex assortments
was perceived as more complex (M =72.64, SD =20.31) than the non-
complex one (M =67.95, SD =21.37; t(198) =-1.59, p =0.06, d =
-0.23). The difference was not statistically signicant, but a statistical
trend was in the desired direction.
Experiment 2B mirrored the design of Experiment 2A to a large
extent, with the crucial difference of assortments in the non-complex
condition being presented in grayscale rather than in color (see
Appendix).
10.1. Participants and design
Four hundred twenty-eight participants (56.3 % male, M
age
=30.23,
SD =10.15) included in the nal sample were recruited via the Prolic
platform to provide their responses in a 2 (assortment complexity: non-
complex vs. complex) ×2 (reward-driven distractor: present vs. absent)
between-subjects design experiment.
3
10.2. Assortment complexity
We followed our previously pre-tested approach to vary the assort-
ment complexity. The complex assortment was presented in color and
disorganized, while the non-complex assortment was in grayscale and
organized.
10.3. Reward-driven distractors
We used the same paradigm as in Experiment 2A. We varied the
presence versus absence of reward-driven distractors by inviting par-
ticipants to seven rounds of the modied ankers task (Huntsinger,
2012). Respondents either made choices from the assortments after
completing all these trials and obtaining feedback related to their per-
formance (distractor absent), or before they started the ankers trials
(distractor present).
10.4. Centrality scores
Our core dependent variable, also this time, was the centrality of
choice. The scores for centrality were coded based the distance measure
employed already in the previous experiments, with scores varying be-
tween 1 and 13 (1 =edge option preferred, 13 =central option
preferred, M =8.39, SD =3.20).
10.5. Manipulation checks
We measured the perceived complexity of the assortments by asking
the question, How complex do you perceive the assortment of sweets to be?
(0 =Not at all complex, 100 =Extremely complex; M =49.87, SD =
22.64).
10.6. Additional measures
We measured mood in line with the previous experiment (nine-point
scales, with bad/good, sad/happy, and displeased/pleased items;
α
=
0.87; M =6.86, SD =1.35). Furthermore, we also collected responses to
the attention check (Oppenheimer et al., 2009) after the assortment task,
embedded among other scales.
11. Results
11.1. Manipulation checks
The analysis of manipulation checks indicated that the complex
assortment was seen as more complex (M =51.94, SD =22.53) than the
non-complex assortment (M =47.90, SD =22.63; t(426) =-1.85, p =
0.03, d =-0.18).
11.2. Main analyses
We conducted a between-subjects ANOVA with the presence of
reward-driven distractors and assortment complexity as independent
variables and the location of the most preferred product as the depen-
dent variable. This analysis identied a main effect of assortment
complexity on the centrality of the selected product; F(1, 424) =7.25, p
<0.01,
η
2
=0.02. Respondents chose products located closer to the
center when they made choices from non-complex assortments (M =
8.79, SD =2.82) in comparison to participants selecting products from
complex assortments (M =7.97, SD =3.51). Moreover, we observed the
two-way interaction of interest between assortment complexity and the
presence of reward cues, F(1, 424) =4.29, p =0.04,
η
2
=0.01). Follow-
up simple-effects analysis conrmed that the choices of participants in
the absent-distractor condition predominantly drove the interaction.
Participants who have already obtained rewards moved closer to the
center of the assortment while selecting the most appealing product
from the non-complex assortment (M =9.22, SD =2.83) in comparison
with those who were choosing from the complex assortment (M =7.76,
SD =3.76; F(1, 424) =11.35, p <0.01,
η
2
=0.03), corroborating H1B.
Conversely, participants who have not yet obtained rewards did not
differ in the locations of their choices from complex (M =8.17, SD =
3.25) versus non-complex assortments (M =8.36, SD =2.76; F(1, 424)
<1, p =0.66), in line with H1A.
We have run an additional between-subjects ANOVA analysis with
mood as a control variable. This covariate did not signicantly affect our
dependent variable (F <1). The interaction between the presence of
reward-driven distractors and the complexity of the assortment
remained stable; F(1, 423) =4.16, p =0.04,
η
2
=0.01).
12. Discussion
Experiment 2B replicated the ndings from Experiment 2A using a
different manipulation of assortment complexity by employing gray-
scale for non-complex organized assortments. The fact that the core two-
way interaction between the presence of reward-driven distractors and
the assortment complexity was also demonstrated for this adjusted
manipulation of complexity proves that conrming our expectations,
consumers become responsive to assortments complexity and start
making choices from different locations within the assortment as a
function of complexity only when they are not preoccupied with any
concurrent reward cues.
Since color could also be seen as a factor that might inuence either
the involvement with a particular assortment (Ha & Lennon, 2010) or
the motivation to process (Frey et al., 2008), we conducted an additional
3
Randomization checks showed that age and gender were equally distrib-
uted across the cells: Age: F(3, 424) <1, Gender:
χ
2
(3, 428) =2.26, p =0.52.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
7
post-test to investigate the possible inuence of color on these con-
structs. The post-test aimed to rule out the possibility of introducing an
unwanted confound in Experiment 2B, but also in Experiment 3 in which
we kept the same manipulation of complexity. We conducted the post-
test on Prolic, collecting responses from 99 participants and present-
ing them either with one of the four randomized versions of the complex
disorganized assortment in color or a non-complex organized assortment
in grayscale. For each of the assortments, we administered 3 different
scales measuring product involvement (e.g., For me, the products depicted
in the assortment would be important/would be unimportant, Zaichkowsky,
1985;
α
=0.86, M =3.77, SD =1.31), purchase decision involvement (e.
g., In making your selection of this product, how concerned would you be
about the outcome of your choice? (1) Not at all concerned, (7) Very much
concerned, Mittal, 1995;
α
=0.86, M =4.16, SD =1.61), and motivation
to process (e.g., The assortment created a strong desire in me to examine it,
Shukla et al., 2022;
α
=0.89, M =4.14, SD =1.34). The results of the
independent-sample t-tests conrmed that there was no difference
across any of these measures between the non-complex assortment in
grayscale and the complex assortment presented in color (product
involvement: M
non-complex
=3.87, SD =1.36; M
complex
=3.66, SD =1.25; t
(97) =0.81, p =0.42, n.s.; purchase decision involvement: M
non-complex
=4.19, SD =1.28; M
complex
=4.13, SD =1.54; t(97) =0.22, p =0.83, n.
s.; motivation to process: M
non-complex
=4.08, SD =1.53; M
complex
=4.19,
SD =1.12; t(89.76) =-0.42, p =0.67, n.s.). As a result, we concluded
that the validity of Experiment 2B and Experiment 3 is not threatened by
introducing color as a confound in terms of involvement or motivation
to process.
In our last experiment, we test the process behind the focal two-way
interaction.
13. Experiment 3
In Experiment 3, we follow a procedure similar to the previous
experiment. Additionally, we provide process evidence to understand
better what is driving the effects presented throughout Experiments 1-
2B. We test the process via moderation (Pirlott & MacKinnon, 2016),
inducing a particular information processing mode before the product
choice task (analytic vs. holistic vs. control; Weaver et al., 2012).
We expect to replicate the ndings we presented across Experiments
1-2B in the control condition. Moreover, we expect to demonstrate
similar effects in the holistic-processing condition. For this, we build on
the global precedence effect —the greater tendency to perceive global
than local stimulus features under default conditions (Kimchi, 1992;
Navon, 1981). Hence, it is plausible to assume that consumers will, by
default, tend to process any assortment using a holistic (global, parallel)
rather than analytic (local, sequential) strategy unless choice-task de-
mands (e.g., complexity) require otherwise. This would imply similar
effects in the control and holistic processing conditions. In contrast, we
expect that instructing participants to use analytical processing while
viewing the assortments will maintain the analytical style in the com-
plex assortment condition but will shift the holistic processing style in
the non-complex condition to a more analytic processing style, thus
attenuating any effect of assortment complexity (Spencer et al., 2005).
We used the same effect size (between small and medium;
η
2
=
0.035) as in previous experiments to conduct power analysis in an a
priori power analysis for between-subjects designs, setting power to 0.95
at
α
=0.05. Consequently, the minimum sample size required for
Experiment 3 was 429 participants for the target 3-way interaction. We
planned to reach this minimum number of respondents and exceed it
within the possibilities of our budget to account for possible exclusions
of participants in line with our predened criteria.
13.1. Participants and design
Five hundred fty-six participants (44.2 % male, M
age
=32.16, SD =
11.75) included in the nal sample were recruited via the Prolic
platform to provide their responses in a 2 (assortment complexity: non-
complex vs. complex) ×2 (reward-driven distractor: present vs. absent)
×3 (information processing mode: analytic vs. holistic vs. control)
between-subjects design experiment.
4
13.2. Procedure
We followed a similar procedure to Experiment 2B. Compared to
Experiment 2B, the core difference being that before participants
engaged in the product choice task, we manipulated their information
processing mode, following Weaver et al. (2012). Participants assigned
to the analytic processing condition were given the following in-
structions: ‘While picking up a sweet from the assortment of sweets
presented to you, your goal is to memorize the individual options within
the assortment carefully because you will be asked to recall them later.’
Participants in the holistic-processing condition read the following in-
structions: “While picking up a sweet from the assortment of sweets
presented to you, your goal is to form a general impression of the
assortment presented to you.’ Participants in the control condition were
informed that on the next page, they could choose from the depicted
assortment. Afterward, participants made their choice from one of two
different assortments: non-complex (organized assortment of sweets
without color) and complex (disorganized assortment of sweets with
color). Similar to Experiment 2B, each assortment was composed of 96
options, arranged in an 8 ×12 grid, with four repetitions of each item
(see Appendix). We created four different versions of each assortment,
which allowed us to randomize product locations for different partici-
pants. Participants made their choice either before (distractor present)
OR after seven trials of the modied anker task (distractor absent,
Huntsinger, 2012) indicating a specic number (e.g., 7th, 11th) in long
sequences of numbers (for instance, composed of 25 elements).
13.3. Assortment complexity and centrality score
We manipulated assortment complexity as in Experiment 2B. We also
measured the centrality of the choices within a given assortment, in line
with previous experiments (M =8.02, SD =3.19).
13.4. Manipulation checks
As in previous experiments, we measured the perceived complexity
of the assortments by asking, How complex do you perceive the assortment
of sweets to be? (0 =Not at all complex, 100 =Extremely complex; M =
47.25, SD =25.30).
Additionally, we measured the time it took participants to make a
choice from the assortments as a manipulation check for assortment
complexity (Paquette & Kida, 1988). Moreover, we also used time as a
manipulation check for information processing mode, expecting that
participants who process the assortments analytically will take longer to
select their most preferred products (Wong et al., 2021).
13.5. Additional measures
We measured mood in line with previous experiments (nine-point
scales, with bad/good, sad/happy, and displeased/pleased items;
α
=
0.87; Aarts & Dijksterhuis, 2003; M =6.51, SD =1.49). Additionally, as
in Experiments 2A and 2B, we collected responses to the attention check
(Oppenheimer et al., 2009).
4
Randomization checks showed that age and gender were equally distrib-
uted across the cells: Age: F(11, 544) <1, Gender:
χ
2
(22, 556) =17.45, p =
0.74.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
8
14. Results
14.1. Manipulation checks
Participants indicated that the complex assortment was seen as
indeed more complex (M =49.50, SD =25.29) than the non-complex
assortment (M =45.18, SD =25.18; t(554) =-2.02, p <0.04, d =-0.17).
We also compared participants’ time needed to select their most
preferred products from a given assortment. In line with earlier research
(Richler et al., 2009; Schwarzer et al., 2005), we expected analytical
processing to take longer than holistic processing. Thus, in the control
condition, the time used should be less than in the analytical condition.
Participants were anticipated to predominantly engage in holistic pro-
cessing as their primary mode of information processing, consistent with
prior research on global precedence (Navon, 1981). We ran a one-way
ANOVA to explore how long participants took to process assortments
and select the most preferred products across three conditions with
varying information processing modes. This analysis resulted in signif-
icant differences between the conditions, F(2, 553) =20.51, p <0.01,
η
2
<0.01. Tukey posthoc comparisons conrmed that participants took
longer to select products from the assortments in the analytic-processing
condition than in the control condition (p <0.01) and in the holistic-
processing condition (p <0.01). There was no difference in time taken
to select most preferred products between the control condition and the
holistic-processing condition, corroborating the notion that holistic
processing is indeed the default (p =0.64).
14.2. Main analyses
We ran a between-subjects ANOVA with the assortment complexity,
presence of distractors, and information processing as three independent
variables and the location of the most preferred product as the depen-
dent variable. As a result of this analysis, we identied a main effect of
assortment complexity on choice location; F(1, 544) =8.24, p <0.01,
η
2
=0.02. Participants made choices located closer to the center of the non-
complex assortment (M =8.42, SD =2.99) in comparison to participants
selecting products from complex assortments (M =7.60, SD =3.36).
Interestingly, we identied the three-way interaction of interest be-
tween assortment complexity, presence of distractors, and information
processing; F(2, 544) =3.45, p =0.03,
η
2
=0.01. To get a better grasp of
this interaction, we further explored the three two-way interactions
between assortment complexity and reward distractor presence (see
Figs. 4-6). The interaction pattern overlapped for the control condition
and holistic-processing condition (Fig. 4 and Fig. 6). Simple-effects
analysis further helped us identify two crucial signicant differences
in means, aligning with our ndings in Experiments 1-2B. In the control
condition, we again observed the effect of complexity on choice strategy
when reward distractors were absent. Under these conditions, partici-
pants chose products located further from the center when selecting
products from complex assortments (M =7.27, SD =3.06) compared to
non-complex assortments (M =8.92, SD =2.61; F(1, 544) =5.96, p =
0.02,
η
2
=0.01). This effect was attenuated when reward distractors
were present (M
Complex
=8.50, SD =2.80, M
Non-Complex
=8.22, SD =
2.78, F(1, 544) <1, p =0.68). Similarly, in the holistic-processing
condition selected products located further from the center of the
complex assortment after having obtained rewards (M =6.96, SD =
3.90) in comparison to those who were choosing a product from non-
complex assortments (M =8.98, SD =2,78; F(1, 544) =9.57, p <
0.01,
η
2
=0.02). These differences were not observed for respondents
who had not yet had a chance to obtain rewards (M
Complex
=7.58, SD =
3.51, M
Non-Complex
=8.23, SD =3.26, F(1, 544) =1.04, p =0.31). Even
more interestingly, this core difference in means for respondents who
had obtained rewards disappeared after analytic processing had been
manipulated before the product choice task (M
complex
=7.93, SD =3.17,
M
non-complex
=7.57, SD =3.81; F(1, 544) <1, p =0.59). Moreover, this
difference in location of selected products was also not present for
participants in the analytic-processing condition who still had not ob-
tained the rewards (M
complex
=7.53, SD =3.30, M
non-complex
=8.51, SD =
2.60; F(1, 544) =2.25, p =0.13).
We ran an additional between-subjects ANOVA analysis, including
one control variable—mood. This covariate did not signicantly inu-
ence the centrality of the selected product, F <1. The three-way inter-
action of interest remained stable; F(2, 543) =3.45, p =0.03,
η
2
=0.01).
15. General Discussion
15.1. Theoretical implications
Consumers are constantly exposed to various environmental cues
that might profoundly impact their judgment and decision-making (e.g.,
Fig. 4. Choice Centrality (Non-Complex vs. Complex Assortments) – Control
Condition. Note. Error bars denote one standard error around the mean.
Fig. 5. Choice Centrality (Non-Complex vs. Complex Assortments) – Analytic
Processing. Note. Error bars denote one standard error around the mean.
Fig. 6. Choice Centrality (Non-Complex vs. Complex Assortments) – Holistic
Processing. Note. Error bars denote one standard error around the mean.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
9
Roschk et al., 2017). This research shows that consumers need at least a
basic supply of attentional and motivational resources to process com-
plex assortments. As such, we demonstrate that assortments of varying
complexity (complex vs. non-complex) steer consumers’ decision-
making towards either the center (non-complex) or further from the
center (complex assortments), but only when consumers are not
distracted by reward-related cognitions, active next to the shopping
goal. In real-life and online experiments, we show that consumers,
depending on the complexity of assortments, make choices aligning with
different information processing strategies – holistic processing for non-
complex assortments and more ne-grained, analytic processing when
assortments become more complex. These diverging processing modes
have relevant implications for consumer choice behavior, leading con-
sumers to select products closer to the center of an assortment when
options are processed holistically and shifting their choices further from
the center of an assortment when assortments are processed analytically.
These results are robust across four experiments with a total of 1492
respondents. Moreover, we replicated the ndings for both non-complex
assortments presented in color and in grayscale.
Consumer location-based choices have long been a conundrum for
researchers due to contradictory ndings (e.g., Valenzuela & Raghubir,
2009) that proved difcult to reconcile. Bar-Hillel (2015) comprehen-
sive research review suggests that assortment complexity (equivalent vs.
non-equivalent assortments) could be one of the underlying explanatory
factors concerning consumer preferences for product locations. One
might wonder to what extent our ndings are driven by the center-stage
effect (Valenzuela & Raghubir, 2009)—the belief that centrally located
products are the most popular ones, which subsequently inuence
consumer choice behavior. Nonetheless, we postulate that the presented
ndings are more uency-driven (driven by the ease or difculty with
which information can be processed), resulting from the possibility of
expending effort while processing complex assortments. When con-
sumers anticipate obtaining rewards next to searching for the most
preferred products in given assortments, they tend to select options
closer to the center of the assortment, irrespective of their complexity,
since they lack the attentional resources needed for processing complex
information (Rusz et al., 2020). Upon obtaining rewards, complexity
was shown to affect consumer choice behavior, shifting their product
choices further from the center of an assortment. Our ndings are in line
with Bar-Hillel’s (2015) expectations, corroborating her perspective on
consumer product choice from specic locations and complementing
previous ndings with a broader perspective on reward-driven dis-
tractors resulting in the attenuation or facilitation of responsiveness to
assortment effects. Future research might explore how other features of
assortments (size, composition, structure, organization) nudge con-
sumers into selecting products from diverging locations.
Our results are tested on low-involvement products (i.e., food
products); therefore, any possible practical implications should be
derived only for product categories characterized by low consumer
involvement (e.g., impulsive purchases). Literature shows that con-
sumers scrutinize product-related information while purchasing prod-
ucts characterized by high involvement to a larger extent, engaging in
more elaborate processing of given options (Behe et al., 2015; Sengupta
et al., 1997). Therefore, we expect the assortments composed of more
involving options (for instance, mobile phones, laptops) to be processed
analytically, one by one, with clearer customer preferences, which
should be subsequently more inuential for product choice. Thus, we
expect a much-weakened inuence of product location on customer
choice for high-involvement products, irrespective of assortment
complexity. As such, we might observe choices further from the center
for more-involving products.
15.2. Practical implications
With the recent developments in AI-driven retail strategies (Iansiti &
Lakhani, 2020) and the ubiquity of data on consumer journeys, the
practical relevance of our ndings is gradually becoming more and more
relevant. First, retailers should understand what triggers consumer
perception of assortment complexity. Our research, concentrating on the
visual richness of assortments, focuses predominantly on varying the
number of features, color, and organization of the options (i.e., Creusen
et al., 2010; Orth & Crouch, 2014) in order to increase perceived dif-
ferences between the identiable product attributes (Greifeneder et al.,
2010). Retailers could use an approach similar to the one used by us in
Experiments 2A and 2B to decrease the perceived complexity through,
for instance, placing products with less contrasting colors next to each
other. On a related note, retailers might prefer to decrease the
complexity perceptions to steer consumers’ attention more consistently
toward the center of a given assortment. Such subtle manipulations in
assortment complexity might be paired with a better understanding that
some consumers visiting the retail stores will be innately more distracted
than others. For instance, smartphone distractions constitute a prevalent
factor impacting consumer decisions in the retail setting (Taylor et al.,
2024). Research shows that some consumers, such as those with higher
compulsivity, are more sensitive to such rewarding stimuli than others
(Hubert et al., 2024). Retailers could establish a proxy for evaluating
which consumers might be more responsive to reward cues (e.g., acting
more promptly on notications, discounts, and points obtained through
the app). This could further help them tailor location-based strategies
while inducing reward mindsets for these particular consumer segments.
Additionally, retailers could consider activating the sense of pursu-
ing rewards for consumers entering a supermarket. This could be done
by notifying them about extra points or vouchers they can obtain, for
instance, through the loyalty program. Based on our results, consumers
should then gravitate more consistently towards the center of assort-
ments, irrespective of their complexity. As such, retailers would also
have a unique chance to inuence consumer choices to benet their
well-being, for instance, by nudging them into healthier or sustainable
options (Bucher et al., 2016). Moreover, rewards and reward cues are
present in various marketplace settings, for instance, close to bakeries,
casinos, or clubs and pubs. Shop owners located close to such areas, by
becoming more aware of the presence of reward-driven distractors for
consumers visiting their shop (for instance, visitors often come before or
after visiting a nearby bakery), could use this knowledge to reorganize
assortments to direct consumer attention towards specic under-
performing products. For instance, they could direct attention more
reliably towards the center if consumers are more likely to be cognitively
preoccupied with various rewarding cues but have not had a chance to
act upon them.
Our ndings are also relevant to consumer choices while sampling
new products or offerings. Such in-store sampling campaigns happen
frequently, predominantly in larger supermarkets (Chandukala et al.,
2017). One problem such campaigns might encounter is that consumers
repetitively sample the same products while disregarding the others
(Van Trijp, 1994). Our ndings demonstrate that incorporating reward
cues before product sampling through offering, for instance, a free snack
or inviting to participate in a small lottery and resolving the rewarding
pursuit before product sampling might help steer consumer attention
towards less sampled options located closer to the edge of the choice set.
Nowadays consumers are increasingly ‘connected’—being on social
media, apps, on calls and chats—also when making choices in the retail
setting (Grewal et al., 2013). Retailers have already been experimenting
with gamication and instigating consumer reward pursuit during the
consumer journey (Insley & Nunan, 2014). We demonstrate how to tie
this usage of gamication tools with assortment planning in order to
strategically direct consumer attention towards specic areas within
assortments. Additionally, consumers often enter the supermarkets
while pursuing unrelated or related rewards simultaneously—for
instance, on the way to a recent blockbuster movie or while having a
Starbucks Rewards app open, checking what extras and deals they can
obtain during their next visit to the coffee shop. Based on our ndings,
we would advise retailers to engage consumers in reward pursuits that
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
10
they can control—for instance, by sending them notications from the
retailer’s loyalty program app when the consumers are in the super-
market. Overriding other reward-related cognitions originating from
competing sources might be benecial in exerting more control over
consumers’ scattered attention (Kacen et al., 2012) while being more
assured that consumers are currently distracted by offered rewards and
extras without making uncertain assumptions. Additionally, our nd-
ings enhance our understanding of how consumers allocate their
attention depending on the assortment complexity in an increasingly
gamied marketplace (Insley & Nunan, 2014).
Apart from real-time assortment planning in physical supermarkets,
retailers have other options where adjusting the location of new or less-
known products within a given assortment is far more manageable. One
tool that could facilitate quicker implementation of our ndings would
be touchscreen kiosks at locations where consumers usually pursue or
complete their reward pursuits (e.g., at the cinema before or after the
blockbuster movie; Schmidtke et al., 2019). Such settings constitute a
fruitful opportunity to alternate the location of specic items that should
be made more salient for consumers. Retailers could additionally benet
signicantly from connecting the proposed manipulations in the
assortment structure with reward cues and a better understanding of
consumer characteristics on a segment level (e.g., interest in a specic
category, past purchases within a given category, Dholakia et al., 2010).
In this way, they could use the abundance of purchase and behavioral
data to attract specic segments to particular product categories and
further products that did not attract their interest.
Next to the previously mentioned relevance of our ndings for re-
tailers of different formats, we can anticipate that even more consumer
decisions will be made online shortly. Many retailers have been exper-
imenting with retail formats in virtual reality and the Metaverse (Yoo
et al., 2023). Furthermore, Augmented Reality (AR) has gained mo-
mentum in retail strategies (Kumar et al., 2024). These virtual retail
settings provide even more opportunities for a more tailored mix be-
tween the presence of desired rewards and retail choices due to more
opportunities for tweaking assortment presentation and the timing and
type of reward cues (Yoo et al., 2023). In addition, choosing products
through Augmented Reality applications increases the complexity of
selecting the most preferred products, which might further have a
relevant impact on the results we demonstrate in this article. Moreover,
consumers spend more and more time gaming and are projected to
spend more and more money in the in-game shops in the coming years
(Caporal, 2024; Hamari et al., 2017). Consumers pursue desired rewards
frequently while gaming, for instance, trying to level up their in-game
characters or being rewarded for their performance in the last round
of the game. Due to this natural presence of reward cues inherently
embedded in game designs, the developers could further utilize the
ndings presented in this article to strategically attract gamers’ atten-
tion toward specic options in the in-game shops. Game developers
could, for instance, present more complex assortments of in-game cos-
metics closer to the logout times of users and shift less attended options
to the edge of such assortments.
15.3. Future research Directions
Our research demonstrates not only when the environment shapes
consumer decision-making but also when it fails to affect consumer
choices. Future research could dedicate more attention towards disen-
tangling the effects of reward-related cognitions brought to the retail
setting (for instance, obtaining more points in the loyalty program that
could be redeemed at Starbucks only after the visit to the store) or
activated within the retail setting (for instance, through a loyalty pro-
gram managed by a particular retailer) on the choices from various as-
sortments. Moreover, in the omnichannel age (Bijmolt et al., 2021),
consumers come to the retail setting at different stages of their reward
pursuit—sometimes active, sometimes just completed. Research could
focus on painting a more dynamic picture depicting the tensions and
interdependencies between concurrent reward pursuit brought to the
retail setting or activated only in the particular retail setting and sub-
sequent choices made during a shopping trip. Another possibly fruitful
avenue for future explorations pertains to the generalizability of our
ndings when consumers are making decisions either under time pres-
sure (Kim et al., 2022) or are characterized by various levels of moti-
vation to make choices (Pieters & Warlop, 1999). We also invite
researchers to replicate our ndings in the future with assortments
composed of products accompanied by textual information (i.e., brand
logos and product labels, Townsend & Kahn, 2014).
Our research on consumer responsiveness to assortment effects is
related to a broader research stream on the context-driven consumer,
which presents consumer decision-making as often irrational and
prompted by subtle environmental inuences (Baker et al., 2002;
Spangenberg et al., 1996). Marketing scholars have documented, for
instance, how store environments such as store design cues and store
ambient cues (e.g., music) drive consumer value perceptions and,
consequently, store patronage intentions (Baker et al., 2002) and how
scents present in the retail environment boost purchase intentions
(Spangenberg et al., 1996). We highlight a potentially relevant bound-
ary condition crucial for these effects to surface—reward-related
thoughts might attenuate ambient inuences. Consistent with our ex-
pectations regarding the susceptibility to variations in assortment
complexity, we also might predict that responsiveness to contextual cues
would be attenuated when consumers are preoccupied with reward-
driven distractors. Conversely, we could predict stronger and more
frequent contextual inuences on consumer decision-making when re-
wards have already been obtained. Even though these expectations
could be seen as puzzling at rst glance, especially from the perspective
of the Elaboration Likelihood Model (Petty & Cacioppo, 1986), the
present results support a recently identied ‘third route’ of information
processing (Rusz et al., 2020) and show that distraction may not
invariably increase sensitivity to environmental cues as previous studies
have suggested (Cohen & Babey, 2012; Conner, 1993). That is, the
present results suggest that attention and information processing do not
only take place via two routes: a top-down route (in which a current
mindset determines attention and information processing) and a
bottom-up one (in which salience of environmental cues determines
this). Rather, the present ndings suggest there might be a third route by
showing that attention and information processing can be a function of
rewards independently of consumers’ current goals and independently
of the physical salience of environmental cues (Rusz et al., 2020). Future
research could further examine the dynamic interplay between moti-
vation to obtain specic rewards, different stages in reward pursuit, and
susceptibility to environmental cues related and unrelated to the current
reward pursuit.
As the German philosopher Arthur Schopenhauer (2014) said: ‘Every
man takes the limits of his own eld of vision for the limits of the world.’
Further investigation into these various parallel worlds of consumers,
some constrained by the presence of reward-driven distractors and
others by personality traits or other situational factors, could help
marketers implement more real-time-based and exible marketing
strategies to target customers more efciently and with offerings that are
better tailored to their current needs and wants.
CRediT authorship contribution statement
Sebastian Sadowski: Writing – review & editing, Writing – original
draft, Supervision, Project administration, Methodology, Formal anal-
ysis, Conceptualization. Bob M. Fennis: Writing – review & editing,
Writing – original draft, Supervision, Methodology, Conceptualization.
Koert van Ittersum: Writing – review & editing, Writing – original
draft, Supervision, Methodology, Conceptualization.
S. Sadowski et al.
Journal of Business Research 190 (2025) 115227
11
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Appendix. . Methodological Detail Appendix
Experiment 1
Fig. 7. Non-Complex Assortment
Fig. 8. Complex Assortment
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Experiment 2
Fig. 9. Non-Complex Assortment
Fig. 10. Complex Assortment
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Experiments 3–4
Fig. 11. Non-Complex Assortment
Fig. 12. Complex Assortment
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Fig. 13. Coding of Choice Centrality
Data availability
Data will be made available on request.
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Sebastian Sadowski is an Assistant Professor in Marketing Communication at the Center
for Language Studies, Radboud University Nijmegen. His research examines the impact of
subtle environmental cues such as primes or assortment structure and subtle language
variations on consumer judgment and decision-making.
Bob M. Fennis is a Professor in Consumer Behavior at the Department of Marketing,
University of Groningen. His main research interest is what could be labeled “Hidden
Persuasion” and focuses on how subtle (and not so subtle) marketing cues inuence
consumers in their emotions, thoughts, and behavior, frequently without them being
aware of this inuence.
Koert van Ittersum is a Professor of Marketing and Consumer Well-being at the University
of Groningen since 2013. In his research and teaching he argues and demonstrates how
marketing can contribute to the well-being of consumers. Van Ittersum focuses predomi-
nantly on healthier and more sustainable diets. He among others collaborates with all
Dutch retailers to research how transparency interventions can help interested shoppers
make healthier purchases while shopping for groceries.
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