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Constructing Consideration Sets: Consumers Change to an Include Strategy when Faced with
Information Overload
JOSEPH K. GOODMAN AND REBECCA WALKER RECZEK*
*Joseph K. Goodman is Associate Professor at Washington University in St. Louis, One
Brookings Dr., Campus Box 1156, Saint Louis, MO 63130, [email protected], 314.935.6354.
Rebecca Walker Reczek is Associate Professor at The Ohio State University, 2100 Neil Avenue,
Columbus, OH 43054, [email protected], 614-247-6433. Thanks to Barbara Kahn and Alex
Chernev for helpful feedback, and a special thanks to Susan Broniarczyk for invaluable feedback
on this research and previous versions of the manuscript.
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Abstract
Consumers can construct a consideration set from a product assortment using one of two
strategies: including the brands or products they wish to further consider or excluding those they
do not wish to further consider. This research examines how elements of the decision context,
specifically the amount of information present in the assortment (with larger assortments
containing more information) and the consumer’s mindset at the time the consideration set is
constructed (i.e., maximizing vs. satisficing), affect a consumer’s choice of consideration set
construction strategy. Drawing on the literature on task/goal compatibility, we argue that
consumers select the consideration set construction strategy most compatible with their goal and
that this differs by product assortment size due to variation in the amount of information present
in large versus small assortments. Across five studies, this research demonstrates that consumers
are more likely to use an include strategy when choosing from large product assortments due to
the overwhelming amount of information they contain. As the more selective of the two
strategies, inclusion is also more compatible with the goal of a maximizer to find the “best”
option, leading maximizers to be even more likely to use inclusion when choosing from large
assortments.
Keywords: consideration sets, include/exclude, decision making strategies, maximizing mindset,
product assortment
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Imagine a consumer in a small boutique trying to make a choice from a product
assortment of six shirts. Now imagine that same consumer in a national department store faced
with many more options—perhaps over 30. What is the consumer to do? Will she use the same
strategy to make a choice between six shirts as she would between 30? Does her selection of a
strategy depend on her mindset (i.e., trying to find the “perfect” shirt vs. trying to find a merely
acceptable shirt)? When consumers must choose an option from an assortment, they typically
narrow down the assortment to a smaller, more manageable consideration set before making a
final selection (Chakravarti, Janiszewski, and Ülkümen 2006; Nedungadi 1990; Ordóñez,
Benson, and Beach 1999). Consumers can use one of two strategies to do so—an inclusion
strategy (i.e., selecting all the brands or products the consumer is interested in further
considering) or an exclusion strategy (i.e., rejecting all of the options in which the consumer is
not interested and considering only the remaining options; Heller, Levin, and Goransson 2002;
Levin, Prosansky, and Brunick 2001). However, little research has examined how the decision
context or consumer mindset might affect this process.
In this research we examine how elements of the decision context, specifically the
amount of information contained in the assortment (with larger assortments containing more
information) and the consumer’s mindset (i.e., maximizing vs. satisficing; Schwartz et al. 2002),
affect the choice of strategy to narrow down the available choice options to construct a
consideration set. We draw on the literature on task/goal compatibility (Fischer et al. 1999;
Fischer and Hawkins 1993; Hsee 1996; Hsee, Loewenstein, Blount, and Bazerman 1999; Irwin
and Naylor 2009) to argue that consumers select the consideration set construction strategy most
compatible with their goal in the task at hand. Although the goal when faced with a large or
small product assortment is the same—to efficiently narrow down the assortment to a smaller,
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more manageable consideration set from which to make a final decision—we argue that the
consideration set strategy that most effectively accomplishes this goal varies by assortment size,
such that inclusion is more compatible with constructing a consideration set from a large
assortment due to the overwhelming amount of information large assortments contain. We
therefore predict, and demonstrate across five studies, that consumers are more likely to use an
include strategy when choosing from a large (vs. small) product assortment. Further, we argue
that maximizers (i.e., consumers who are searching for the “perfect” option) have an additional
goal beyond efficiently narrowing down the assortment when constructing a consideration set:
They also wish to create a set that contains the “best” options from which to choose in order to
increase the likelihood that they will end up with the best possible final outcome. Inclusion, as
the more selective of the two strategies, is therefore more compatible with both of the goals of a
maximizer when faced with a large assortment (i.e., efficiently narrowing down an assortment
and being careful not to leave out any highly desirable alternatives). Thus, we predict that
maximizers (vs. satisficers) are particularly likely to shift to an inclusion strategy when
constructing a consideration set from a large assortment.
Our research therefore contributes to the product assortment literature (e.g., Chernev,
Böckenholt, and Goodman 2015; Diehl and Poynor 2010; Broniarczyk 2008; Iyengar and Lepper
2000; Scheibehenne, Greifeneder, and Todd 2010; Sela, Berger, and Liu 2010) by demonstrating
that assortment size can affect a consumer’s choice of consideration set construction strategy due
to task/goal compatibility. We also contribute to the literature on maximizing versus satisficing
(e.g., Ma and Roese 2014; Schwartz 2004) by demonstrating that maximizers (vs. satisficers) are
particularly likely to use different strategies for construction of consideration sets when faced
with a choice set containing too much information. Finally, we also contribute to the literature
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examining consideration set construction as a separate but important phase in the choice process
(Chakravarti et al. 2006; Desai and Hoyer 2000; Kardes, Kalyanaram, Chandrashekaran, and
Dornoff 1993; Mitra 1995) by showing that the amount of information contained in the
assortment can determine the products that end up in a consideration set by influencing the
strategy consumers use to construct it.
The rest of the paper is organized as follows. First, we provide a brief overview of the
consideration set construction process and how it differs from choice rules/heuristics. Second, we
discuss task/goal compatibility and develop our hypotheses. Third, we present five studies (1A,
1B, 2, 3, and 4) to test our hypotheses and show that (1) consumers are more likely to use an
include strategy when choosing from large product assortments (studies 1A and 1B) and (2) this
shift in strategy is caused by the added information load associated with large assortments (study
2). Finally, we demonstrate that maximizers are more likely than satisficers to use an include
strategy when faced with a large assortment, as it is the more selective strategy, regardless of
whether maximizing is operationalized as a chronic personality trait (study 3) or as a
situationally-induced mindset (study 4).
CONSIDERATION SET CONSTRUCTION
Screening and Consideration Set Construction
Consumers typically construct consideration sets to simplify the difficult task of choosing
from an assortment by creating a smaller, more manageable set of options to consider further
(Chakravarti and Janiszewski 2003). A consideration set is a subset of options from the universal
set of available options (Shocker et al. 1991) defined as “those goal-satisfying alternatives salient
or accessible on a particular occasion” (Shocker et al. 1991, p. 183) or the set of options that has
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survived the screening process (Häubl and Trifts 2000; Gilbride and Allenby 2004).
Consideration set construction, also called screening, is the process of admitting options into the
consideration set (Beach 1993). A final choice is then made from this constructed set.
Past research has identified a number of variables that impact consideration set
composition, including usage situation (e.g., Desai and Hoyer 2000; Hutchinson, Ramam, and
Mantrala 1994), pioneering advantage (Kardes et al. 1993), and advertising (Mitra 1995; Mitra
and Lynch 1995). More recent research has demonstrated that the composition of the
consideration set can depend on the decision context. For example, providing recommendations
(e.g., signs indicating which options are “Best Sellers”) can increase the size of the consideration
set, leading to more choice difficulty (Goodman et al. 2013). In general, there is some evidence
that decision makers tend to construct larger consideration sets from larger choice sets (Heller et
al. 2002; Levin et al. 2001; Ordóñez et al. 1999). Given that assortment size and the decision
context can affect consideration set size, it is likely that other elements of the decision context
also have systematic effects on the strategy consumers choose to construct a set. We focus on
two such elements that we expect to influence this strategy selection: the amount of information
contained in a choice set (with larger assortments containing more information) and whether the
consumer is maximizing or satisficing. First, however, we discuss the two strategies consumers
employ when constructing a consideration set.
Consideration Set Construction Strategies: Include versus Exclude
The decision-making literature identifies two strategies for constructing a consideration
set: inclusion and exclusion. A consumer using an include strategy seeks out alternatives to
include in the consideration set, which can be done by alternative or by attribute, but either
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method results in the inclusion of entire alternatives. In contrast, a consumer using an exclude
strategy seeks out alternatives to exclude from the consideration set, which are placed into an
inept set (Narayana and Markin 1975). The include versus exclude distinction has several aliases
but generally refers to the creation of a consideration set from a larger set of options (e.g., Heller
et al. 2002; Irwin and Naylor 2009; Levin et al. 2001). Related research has used the terms
“accept,” “select,” “choose,” or “retain” versus “reject” or “eliminate”; however, these terms are
generally used to refer to a choice between two (or three) options and not to the consideration set
construction process (e.g., Chernev 2009; Meloy and Russo 2004; Shafir 1993; Wedell 1997; but
see Ordóñez et al. 1999 for an exception in terminology). Therefore, we adopt the terminology of
the consideration set literature and use the terms “include” and “exclude.”
It is important to note that screening strategies (i.e., include vs. exclude) are different
from decision rules (e.g., disjunctive, conjunctive, lexicographic-by-aspects, elimination-byaspects; Hoyer, MacInnis, and Pieters 2012; Yee, Dahan, Hauser, and Orlin 2007). Rules are
used to execute a screening strategy or to make a final decision after the consideration set has
been formed. While decision rules can be executed by-attribute (e.g., elimination-by-aspects) or
by-alternative (e.g., conjunctive) and used in both the screening and choice phases of the
decision process (Hoyer et al. 2012), consideration set construction strategies are always byalternative (not by-attribute) and only used during the screening phase, and not the final choice
phase. While some research has focused on what rules consumers use to execute an include
strategy (Gilbride and Allenby 2004; Hauser and Wernerfelt 1990; Roberts and Lattin 1991; Yee
et al. 2007), there is little insight into the antecedents to consideration set construction strategy
selection, such as product assortment or decision making style (see Paulssen and Bagozzi’s 2005
work on self-regulation and construction set construction for an exception), and no research, to
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our knowledge, has explored assortment size or a maximizing mindset as a determinant of
consideration set construction strategy selection.
TASK/GOAL COMPATABILITY AND SELECTION OF A CONSIDERATION SET
CONSTRUCTION STRATEGY
Assortment Size and Consideration Set Construction Strategies
There is some evidence from past research that selection of a consideration set
construction strategy is context dependent, and there is reason to believe that assortment size is
an important factor in strategy selection. Despite the fact that consumers are attracted to retailers
with large assortments (Broniarczyk, Hoyer, and McAlister 1998; Chernev and Hamilton 2009;
Goodman and Malkoc 2012), larger assortments can result in more decision difficulty, regret,
and choice deferral compared to small assortments (Broniarczyk 2008; Chernev et al. 2015;
Iyengar and Lepper 2000; Scheibehenne et al. 2010; Schwartz 2004). Given that consumers have
been shown to adapt their search and decision making strategies to accommodate added
information and difficulty (Payne 1976; Payne, Bettman, and Johnson 1993), the difficulty of
processing the added information in a large assortment is likely to trigger the use of a different
strategy than what consumers would use when choosing from a smaller assortment (Broniarczyk
2008; Chernev 2003, Sela et al. 2009). However, no past research has directly addressed which
strategy, inclusion or exclusion, is more likely to be selected when faced with a large (vs. small)
assortment.
Task/Goal Compatibility. There is a rich literature in decision making on the role of
task/goal compatibility in influencing attribute weights, which shows that consumers weight
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attributes in a manner consistent with their goal in a specific task (Tversky, Sattath, and Slovic
1988). For example, prominent attributes are weighted more in choice tasks than in other types
of tasks, such as pricing or matching, because placing weight on prominent attributes simplifies
the choice task (Fischer et al. 1999; Fischer and Hawkins 1993; Irwin and Baron 2001; Tversky
et al. 1998). Similarly, more easily comparable attributes receive more weight in joint evaluation
modes than in single evaluation modes (Hsee 1996; Hsee et al. 1999). In choice-rejection tasks
there is also evidence of task/goal compatibility, such that choosing is more compatible with a
promotion focus and rejecting is more compatible with a prevention focus (Chernev 2009). In the
consideration set construction domain, the weighting of ethical attributes differs when consumers
construct consideration sets via exclusion versus inclusion, such that ethical attributes are
weighted more heavily in exclusion because this task is more compatible with expressing ethical
values (Irwin and Naylor 2009). Note that in this work, Irwin and Naylor (2009) manipulated
consideration set construction strategy, such that study participants were assigned to construct
consideration sets by including or excluding, and did not consider choice of strategy as the
dependent variable of interest (as we do in this research).
We propose that, just as consumers select attribute weights to match their goals in a
specific task or response mode, consumers also select a consideration set construction strategy to
match their task when forming a consideration set. We propose that an inclusion strategy is more
compatible with processing the large amounts of information required in order to winnow down
a large assortment. To understand why this is the case, we must first consider the task of
narrowing down from a large versus small product assortment and how well or poorly this task
aligns with the process of inclusion versus exclusion. Although the over-arching goal when faced
with a large or small assortment is the same (i.e., to narrow down the initial choice set), we
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propose that the actual task of efficiently narrowing down this set to a smaller, more manageable
consideration set varies by the number of options available.
When faced with a small assortment (e.g., of six potential options), it is possible for the
decision maker to process each option in the assortment. In most large retail assortments (e.g.,
20-50 options), while it may be technically possible to process each option, given the
diminishing marginal utility for search (Kuksov and Villas-Boas 2010), full processing becomes
arduous and inefficient. Because exclusion requires the consumer to focus on options that he or
she does not ultimately want in order to reject them, the more options in the assortment, the more
effortful it is to execute an exclude strategy. This is because the larger the assortment, the more
likely it is that the options one does not wish to consider significantly outnumber the options one
does wish to consider. Thus, examining all options and rejecting the bad options (i.e., exclusion)
is more efficient at narrowing down one’s options from a small assortment than a large
assortment: The decision maker can more efficiently focus on options he or she does not want
because there are fewer options to examine; that is, there is less information to process in a small
versus large choice set.
In general, as the number of options in an assortment increases, information increases and
by necessity search becomes less complete and more selective (Chernev 2003; Payne 1976;
Payne et al. 1993). As a result, once the consideration set is constructed, there may be options
that are left out and not evaluated for entry into the set. In fact, past work has shown that the
consideration sets that result from inclusion are generally smaller than those produced by
exclusion (Heller et al. 2002) and, importantly, more selective in that a given option has a lower
chance of being retained in the final set when inclusion is used (Levin, Jasper, and Forbes 1998;
Yaniv and Schul 2000; Yaniv, Schul, Raphelli-Hirsch, and Maoz 2002). Therefore, we predict
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that inclusion is more compatible with accomplishing a consumer’s goal when faced with the
task of narrowing down a large assortment into a smaller consideration set. As a result,
consumers will be more likely to choose inclusion as their consideration set construction strategy
when faced with a large assortment compared to a small assortment. Further, if the effect is, in
fact, due to the added information present in large assortments, as we propose, then we should
also predict more inclusion when choices have more information compared to less information
(holding assortment size constant).
Our predictions are consistent with past research documenting evidence of goal
compatibility in consideration set construction in terms of the framing of the problem and the
strategy selected. When choosing between two options, Meloy and Russo (2004) found evidence
of a “compatibility” effect such that when there is a match between the type of attributes
presented (e.g., negative attribute) and the choice strategy (e.g., choice rejection), participants
reported greater certainty, confidence, and information distortion compared to a mismatch (e.g.,
negative information and choice selection). Similarly, Levin et al. (2001) manipulated the
screening goal by instructing participants to identify a set of employees to either hire or fire and
found that participants were more likely to choose an include strategy (81%) when hiring
compared to firing (39%). We take these findings a step further and, based on our predictions
about set size and task/goal compatibility, also predict that using inclusion leads to a greater
focus on positive (vs. negative) attributes and more positive (vs. negative) thoughts while
constructing a consideration set.
Maximizing versus Satisficing and Consideration Set Construction Strategies
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The difference between maximizing and satisficing can be thought of as the difference
between trying to find the “best” or “optimal” option and settling for an acceptable, “good
enough” option, or “satisficing” (Schwarz 2004; Simon 1978). A tendency to maximize has been
conceptualized as both an individual-level personality trait (Schwartz et al. 2002) and as a
mindset that can be situationally induced (Ma and Roese 2014). Regardless of whether a
consumer is maximizing in a given decision context because of a chronic trait or a situationally
active mindset, maximizing introduces an additional goal beyond efficiently narrowing down the
assortment when constructing a consideration set: As part of their pursuit of perfection,
maximizers want to create consideration sets that contain the “best” options from which to
choose in order to increase the likelihood that they will end up making the single best possible
final choice. Thus, while satisficers simply have one goal when forming a consideration set—to
efficiently narrow down the set—maximizers have a second, additional goal: to create the “best”
set of options from which to choose.
This second goal, to find the best set of options, is likely to lead maximizers to be even
more likely to use inclusion when faced with a large product assortment, moderating our
proposed effect of assortment size on consideration set strategy. While inclusion is compatible
with the single goal of the satisficing consumer to efficiently narrow down a large assortment to
a more manageable consideration set, it is compatible with the two goals of a maximizer, both
the goal they share with satisficers and their unique goal of finding only the best options to
include in their consideration set. Inclusion, with its focus on positive attributes and desirable
alternatives, is much more compatible with the goal of accurately finding the best (where
accuracy means not accidentally “missing” an option that might be desirable). For a maximizer
to make sure he or she does not miss any of the “best” options from a large assortment while still
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efficiently narrowing down the set, he or she must use inclusion, ignoring any options that lack
clear positive attributes and selectively focusing only on clearly desirable alternatives.
We should also note that this prediction is consistent with past research demonstrating
that, compared to satisficers, maximizers rely more on the decision context and external sources
of information than do satisficers (Iyengar, Wells, and Schwartz 2006). Consistent with this
work, we argue that maximizers are particularly likely to adapt the strategy they use when faced
with the added difficulty of choosing from a large assortment. Satisficers, on the other hand,
should be less likely to shift their strategy to inclusion in larger assortments compared to small
assortments. For satisficers, the less selective exclude process is more likely to be deemed
sufficient in either situation since their goal is to end up with a “good enough” solution, not
necessarily the “best.”
OVERVIEW OF STUDIES
We test our predictions across five experiments. Studies 1A and 1B provide initial
support for our prediction that consumers are more likely to choose an include consideration set
construction strategy when faced with a large (vs. small) product assortment across two product
categories (chocolate in study 1A and pens in study 1B) and two elicitations of strategy selection
(a paper-and-pencil planogram in study 1A and a drag and drop computer procedure in study
1B). Study 2 holds assortment size constant, instead manipulating the amount of information
provided about the options in a choice set to be high or low. Results reveal that an increase in the
amount of information can also induce an inclusion strategy, thus providing evidence that
differences in strategy selection by assortment size are driven by differences in the amount of
information contained in large versus small assortments. In study 3, we measure chronic
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tendency to maximize versus satisfice and demonstrate that maximizers are especially likely to
use inclusion when faced with large assortments. We also provide process evidence to support
our task/goal compatibility theory, by demonstrating that inclusion results in more focus on
positive attributes, more positive thoughts, and greater focus on options that ultimately end up in
the consideration set than on options that do not. Finally, study 4 demonstrates that our
predictions hold when maximizing (vs. satisficing) is manipulated rather than measured.
STUDY 1A
The primary goal of study 1A was to test our central hypothesis, that consumers are more
likely to use an include (vs. exclude) consideration set construction strategy when faced with a
large (30 options) versus small (6 options) product assortment.
Method
Seventy-seven undergraduates from various colleges at a large university participated in
the study in exchange for extra credit in their introductory marketing class. Participants were
presented with a planogram representative of how products might be displayed at a high-end
chocolate retailer like Godiva, or in a non-store environment such as a catalog or online retailer.
Each planogram contained pictures of either a small (6) or large (30) assortment of chocolates,
depending on condition. All participants were first asked to read instructions about consideration
set construction strategies (see Appendix A for details). The experimenter also verbally
explained to the participants that two strategies exist to “narrow down the display to a smaller
group of chocolates that you would actually consider buying. People can either circle the options
they like, or they can cross out options they do not like.” Participants were then instructed to
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either circle (include) or cross-out (exclude) chocolates to narrow down the set of options to
those that they “would actually consider buying” (adapted from Heller et al. 2002). Participants
read that they would receive the chocolate they chose later in the study. After participants
constructed their consideration set, they made a final choice of chocolate from their set. They
then went into another room and received their chosen chocolate (or a comparable alternative if
it was not available).
Our two dependent variables were consideration set construction strategy choice (include
or exclude) and consideration set size. The distribution of consideration set size was skewed,
with a mean of 8.10 and a range from 2 to 29, suggesting that a few participants may not have
understood the instructions when narrowing the set. We used the recommended studentized
residuals method by McClelland (2000) to determine whether some observations were skewing
the results. Five participants with studentized residuals of greater than two were eliminated from
the analysis, leaving us with 72 valid observations to use in the primary analysis.
Results and Discussion
We first examined participants’ choice of consideration set construction strategy to
determine if it differed by assortment size condition. As predicted, participants were more likely
to use an include consideration set construction strategy in the large assortment condition (56%)
compared to the small assortment condition (31%, χ2 (1, N = 72) = 4.59, p < .05, OR = 2.84, φ =
.25). When we include the five participants that were eliminated, the results do not substantively
change: Participants were still more likely to include in the large assortment condition (54%)
compared to the small assortment condition (31%, χ2 (1, N = 77) = 4.18, p < .05, OR = 2.63, φ =
.23).
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Recall that we also predicted that consumers would be more likely to use an include
strategy in large assortments compared to small assortments because an include strategy allows
them to be more selective in their processing, consistent with the task/goal compatibility
explanation we propose. Supporting this notion, participants had smaller consideration sets after
using an include (M = 5.61) compared to an exclude strategy (M = 8.14, F(1,68) = 55.04, p <
.001, ηp2 = .45), and the effect was significantly larger when faced with a large assortment
(MInclude= 7.10 vs. MExclude= 16.00) compared to a small assortment (MInclude= 2.91 vs. MExclude=
3.12; assortment x strategy interaction: F(1,68) = 50.06, p < .001, ηp2= .42).
Study 1A therefore demonstrates that consumers are more likely to use an include
strategy when choosing from a large assortment compared to a small assortment and provides
evidence that inclusion is indeed the more selective strategy. However, one potential limitation
of this study is that circling or crossing out alternatives is not the typical way in which
consumers execute an include or exclude strategy in the marketplace (apart from how some
consumers use retailers’ catalogs). Therefore in study 1B, we use different elicitation procedures
and a different product category to provide evidence for the generalizability of the effect.
STUDY 1B
The primary purpose of study 1B was to test our central hypothesis using a different
elicitation procedure than that used in study 1A. Specifically, participants used a computer
interface to drag and drop alternatives rather than circling or crossing out alternatives on a paper
and pencil planogram. Study 1B also uses a new product category, writing pens.
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Method
One hundred ninety two undergraduates participated in the study in exchange for course
credit. Participants viewed a display of either 32 (large assortment) or 6 (small assortment) pens
via computer. Participants were informed that five participants would be randomly selected to
receive a box of the pens they selected as their final choice. Participants were first asked, before
making that choice, to “narrow down the display to a smaller group of pens that you would
actually consider.” They could choose to exclude, and drag-and-drop pens that they don’t like to
the box on the right, or they could include, and drag-and-drop pens that they do like to the box
on the right. Participants then indicated whether they wanted to include or exclude and
proceeded to drag and drop pens into the box. As a check to make sure participants were
following instructions, on the next page we asked participants whether they included pens,
excluded pens, did a little of both, or could not remember. Eleven participants said they did both,
eight people could not remember, and two did not answer. Nine people did not narrow down the
set (i.e., all options were left in the set or they constructed a consideration set of zero). Thus, a
total of 161 participants correctly followed instructions and constructed a consideration set (i.e.,
constructed a consideration set larger than zero). As in study 1A, we also used the studentized
residuals method recommended by McClelland (2000) to determine whether some observations
were skewing the results. Eight participants had studentized residuals of greater than two, seven
of whom either did not follow instructions or incorrectly remembered their screening strategy. In
other words, participants with large residuals were also likely to have not followed instructions.
Therefore, our analysis was conducted with 152 valid observations after removing participants
who failed to follow instructions and/or had large residuals. After the study, five participants
were randomly selected and received a box of the pens they chose.
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Results and Discussion
Consistent with study 1A, participants were more likely to use an include consideration
set construction strategy in the large assortment condition (78%) compared to the small
assortment condition (62%, χ2 (1, N = 152) = 5.10, p < .05, OR = 2.27, φ = .18). When we
include the dropped participants, the results do not substantively change: Participants were still
more likely to include in the large assortment condition (77%) compared to the small assortment
condition (63%, χ2 (1, N = 161) = 3.66, p = .056, OR = 1.96, φ = .15). Supporting the theory that
an include strategy allows consumers to be more selective in their processing, we again found
that participants had smaller consideration sets after using an include strategy (M = 5.61)
compared to an exclude strategy (M = 8.14, F(1,148) = 138.07, p < .001, ηp2= .48). Further, the
effect was significantly bigger when faced with a large assortment (MInclude= 6.02 vs. MExclude=
12.06, F(1,148) = 61.85, p < .001, ηp2= .29) compared to a small assortment (MInclude= 2.88 vs.
MExclude= 3.50, F(1,148) < 1, ηp2= .01; assortment x strategy interaction: F(1,148) = 29.60, p <
.001, ηp2= .17).
STUDY 2
The primary purpose of study 2 was twofold. First, we wanted to examine whether it is
the increased information load of large assortments that is leading to the use of an include
strategy—and thus the need to be more selective and more efficient in one’s search—or whether
there was something else about large assortments driving our results (e.g., the excitement of the
large assortment or the greater expectations of satisfaction with the final choice; Diehl and
Poynor 2010).
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Our second goal was to expand the applicability of our effects beyond assortment. If
large assortments lead to the use of an include strategy, then we might expect that any assortment
that leads to information overload will increase the use of an include strategy. In other words,
will consumers faced with an assortment that contains more information about each option be
more likely to include compared to those faced with an assortment containing less information
about each option even when both assortments contain the same number of options? We tested
this hypothesis in study 2 by manipulating the number of reviews associated with each option in
an assortment, while holding the number of options constant.
Method
Two hundred undergraduates participated in the study in exchange for course credit.
Participants viewed a display of 12 vacation options via computer. Each vacation either had two
reviews (low information load) or six reviews (high information load; see Appendix B for an
example). Participants were first asked to “narrow down the display to a smaller group of
vacations that you would actually consider.” They could choose to exclude, and click on
vacations that they don’t like, or they could include, and click on vacations that they do like.
After indicating whether they wanted to include or exclude, participants proceeded with the task.
As a check to make sure participants were following instructions, on the next page we asked
participants whether they included or excluded. Four participants incorrectly responded (e.g.,
chose to exclude but indicated they included), two people could not remember, and 53 said they
did both. Four people did not narrow down the set (i.e., all options were left in the set). Thus, a
total of 137 participants correctly followed instructions and constructed a consideration set. As in
studies 1A and 1B, we also used the studentized residuals method recommended by McClelland
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(2000) to determine whether some observations were skewing the results. Two participants had
studentized residuals of greater than two, leaving us with 152 valid observations.
Results and Discussion
Consistent with studies 1A and 1B, participants were more likely to use an include
consideration set strategy in the high information (57%) compared to the low information
condition (42%, χ2 (1, N = 135) = 3.22, p = .07, OR = 1.87, φ = .15). When we include dropped
participants, our results do not substantially change and in fact become more reliable:
Participants were more likely to include in the high information (59%) compared to the low
information condition (45%, χ2 (1, N = 200) = 3.93, p < .05, OR = 1.75, φ = .14).
Supporting the theory that an include strategy allows consumers to be more selective in
their processing, we again found that participants had smaller consideration sets after using an
include strategy (M = 6.08) compared to an exclude strategy (M = 7.51, F(1,131) = 16.11, p <
.001, ηp2= .11).
Thus, the results of studies 1A, 1B, and 2 provide evidence in support of our prediction
that, based on task/goal compatibility, consumers are more likely to use inclusion when faced
with the task of constructing a consideration set from a large product assortment than they are to
use exclusion and this difference is driven by the increased information present in a large versus
small assortment. Further, these studies provide evidence that an include strategy is more
selective, resulting in smaller consideration sets than exclusion. It is, in part, this selectivity that
makes inclusion (vs. exclusion) more compatible with the task of efficiently narrowing down a
large assortment to a smaller consideration set. We also expect that this compatibility is driven
by a greater focus in inclusion on positive attributes, on positive thoughts, and more deliberation
21
on options in the consideration set (vs. options not ultimately chosen to be in the set). In study 3
we test these predictions, along with our prediction that maximizers (vs. satisficers) are
particularly likely to choose inclusion when faced with a large assortment.
STUDY 3
In order to test our hypotheses in study 3, we once again manipulated assortment size
using retail planograms, as in study 1A, and used a new product category: backpacks.
Importantly, we also measured which attributes participants were focused on during the process
of constructing a consideration set and collected thought listings while participants were
completing the task. We also measured participants’ chronic tendency towards maximizing vs.
satisficing using the maximizer/satisficer scale (Nenkov et al. 2008; Schwartz et al. 2002). Recall
that we predict that maximizers are particularly likely to use inclusion when faced with a large
assortment because it is compatible with both of their goals in the consideration set construction
task to: (1) efficiently narrow down the options to a smaller set and (2) ensure that none of the
“best” options are left out of the consideration set in order to help them make the perfect final
choice. Since chronic satisficers will not have this second goal, we predict that they are less
likely than maximizers to use inclusion when faced with a large assortment.
Method
Design. The study was a 2(Assortment Size: Small vs. Large, between-subjects) x
2(Product Category Replicate: Chocolates vs. Backpacks, within-subjects) x 2(Replicate Order,
between-subjects) mixed design. We manipulated assortment size in the same fashion as study
1A (30 vs. 6 options), with the chocolate replicate identical to that used in study 1A. To create
22
the backpack assortments, we selected the top 30 backpacks in terms of sales on ebags.com. We
created the small assortment by taking the top selling bags, while also making sure that some
variance in attributes (e.g., color) was achieved. Participants constructed consideration sets in
both categories, with the order counterbalanced.
Procedure. One hundred fifty-seven students from various colleges at a large university
participated in the study in exchange for extra credit in their introductory marketing class. One
participant did not complete all the questions and was removed from the analyses. Participants
received the same include-exclude consideration set strategy instructions used in study 1A. We
then informed participants that we were interested in their thoughts as they completed the task
and that they would first practice writing their thoughts before the study began. They were asked,
“Please write all your thoughts, including those dealing with the products as well as any other
random thoughts you might have. For instance, we are interested in: 1) Which items you are
considering or not considering, and 2) Why you are considering or not considering each item.”
Participants first wrote their responses on a practice category (chairs) to make sure that
all participants understood the instructions and to allow them to practice writing their cognitive
responses in the packet. As they practiced, the administrator walked around the room and
checked with each participant to ensure they understood the instructions before beginning the
target task. After completion, participants were given a second packet where they shopped for a
backpack and a box of chocolates (with order counterbalanced). Participants read the same
instructions and were asked to write their responses in the space provided as they narrowed the
set down to a smaller, more manageable set of options.
Next participants responded to two questions to measure which attributes were salient to
them during the consideration set construction task. They were asked to “Think back to when
23
you were narrowing down the set of chocolates. Which attributes were important to you when
narrowing down the set of chocolates?” After writing down the attributes, participants then
coded whether they considered each attribute to be positive or negative by writing “either a
positive (+) or a negative (-) sign next to each attribute that you just wrote down to indicate
whether each attribute is positive or negative.”
Participants then chose a final option and answered questions measuring decision regret,
difficulty, satisfaction, and affect, which did not differ significantly across assortment conditions
(p’s > .2). They then answered three category involvement questions and two need for cognition
questions (see Appendix C for details), which did not moderate or mediate the findings; adding
them to the model only aids reliability measures (i.e., results in smaller p-values). To measure
maximizing mindset, participants responded to four questions from the maximizer/satisficer
scale (Nenkov et al. 2008; Schwartz et al. 2002). The maximizer/satisficer questions were
averaged and mean-centered for analyses.
Coding. Two independent coders blind to the hypotheses coded participants’ thoughts as
positive, negative, or neutral. They then coded whether each thought referred to an alternative in
the consideration set, not in the consideration set, or neither. The average respondent expressed
22.2 thoughts, for a total of 3,430 thoughts. The two coders showed acceptable levels of
reliability (α = .91 for positive thoughts, α = .96 for negative thoughts), and their two measures
were averaged for analyses. To create one thought measure per participant, we averaged each
participant’s thought measure across the two product replicates. To control for the number of
thoughts expressed, a proportion of thoughts relative to total thoughts was used as the dependent
variable (e.g., # of positive thoughts / total # of thoughts). For the attribute valence measure, we
also averaged across both replicates and created a proportion-based measure to control for the
24
variance in the number of attributes listed: the proportion of positive or negative attributes listed
relative to the total number of attributes listed (e.g., # of positive attributes / total # of attributes).
Analysis. All models controlled for the product replicate factor and the potential
interaction (there were no significant interactions with product replicate). Thus, all models
included assortment, replicate order, and their interactions. All variables were mean-centered at
zero to properly interpret their main effects (Irwin and McClelland 2001). As in previous studies,
some participants did not follow instructions and constructed unreasonable consideration sets.
We again used the studentized residuals method recommended by McClelland (2000) to
determine whether some observations were disproportionally affecting the results. As in our
previous studies, those with studentized residuals greater than two were removed to create a
sample of 142 participants to use in the primary analysis, but, as in prior studies, we also report
the results including these participants.
Results
Consideration Set Construction Strategy. Our main goal was to test whether participants
were more likely to use an include consideration set construction strategy when constructing a
consideration set from a large assortment and whether this is especially true for people in a
maximizing mindset. We had two choice observations (backpacks and chocolates) for each
participant. There are several approaches to analyze the data (e.g., multivariate probit, ordinal
logistic regression, repeated measures logistic regression, etc.) with varying tradeoffs and
assumptions, but a repeated measures logistic regression is arguably the most appropriate.
Nonetheless, we conducted several analyses that all resulted in extremely similar model
25
estimates and p-values. For simplicity, we next report the repeated measures logistic regression
statistics (multivariate logit using GENMOD in SAS).
As expected, we found that participants were overall more likely to use an include
strategy when choosing from large assortments (50%) compared to small assortments (34%, b =
.31, 95% CI: .01 to .63, χ2 (1, N = 142) = 4.19, p < .05, OR = 1.39). Further, these results were
moderated by whether a participant was more of a maximizer or a satisficer (b = .31, 95% CI: .03
to .60, χ2 (1, N = 142) = 4.44, p < .05). Using a spotlight analysis (Fitzsimons 2008; Irwin and
McClelland 2001, 2003) to plot the continuous model one standard deviation (SD = 1.2) above
and below the mean of the maximizer/satisficer scale, we see that extreme maximizers were
indeed more likely to choose an include strategy when constructing a consideration set in large
assortments (58%) compared to small assortments (26%, χ2 (1, N = 142) = 7.58, p < .01, OR =
2.02). Extreme satisficers, on the other hand, did not exhibit any differences in strategy due to
assortment size (42% vs. 44%, χ2 (1, N = 142) < 1, OR = .95). Nonetheless, at the mean level of
the maximize/satisficer scale (the “main effect”), people were more likely to choose an include
strategy in large assortments compared to small, and a floodlight analysis (Spiller, Fitzsimons,
Lynch, and McClelland 2013) showed that as participants become more satisficing, they are less
likely to choose an include strategy in large assortments compared to small (Johnson-Neyman
point: .21, 49% vs. 36%, χ2 (1, N = 142) = 2.70, OR = 1.30). When we include participants that
did not follow instructions and those with large residuals, the results do not significantly change
(assortment x maximizer interaction: χ2 (1, N = 142) = 2.97, p = .08). Maximizers are still more
likely to choose an include strategy in large compared to small assortments (χ2 (1, N = 142) =
5.59, p < .05, OR =1.71), and satisficers are not (χ2 (1, N = 142) < 1, OR = .97).
26
Consideration Set Size. To examine how strategy affected consideration set size, we
conducted a repeated measures analysis (GLM) regressing consideration set size on assortment,
replicate order, and screening strategy. Replicating our previous results, participants formed
smaller consideration sets when using an include strategy (M = 4.34) compared to an exclude
strategy (M = 7.48, b = 3.14, 95% CI: 2.11 to 4.16, z = 6.00, p < .001), and this main effect was
qualified by an assortment by consideration set strategy interaction (b = 3.15, 95% CI: 2.12 to
4.18, z = 6.00, p < .001). The effects of consideration set strategy on set size were bigger when
participants were screening from a large assortment (MInclude= 5.88 vs. MExclude= 12.19, z = 6.10,
p < .001) compared to a small assortment (MInclude= 2.82 vs. MExclude= 2.83, z < 1). When we
include the dropped participants, the results do not change significantly: Participants had smaller
sets including (M = 4.66) compared to excluding (M = 8.08, b = 3.44, 95% CI: 2.45 to 4.43, z =
6.82, p < .001), and there was an assortment by strategy interaction (b = 3.88, 95% CI: 2.12 to
4.18, z = 6.00, p < .001): The effects was larger in a large assortment (MInclude= 6.52 vs. MExclude=
13.40, z = 6.96, p < .001) compared to a small assortment (MInclude= 2.82 vs. MExclude= 2.82, z <
1). The fact that an include strategy leads to smaller consider sets provides more support for the
notion that it is, in part, the more selective processing in inclusion that makes it particularly
compatible with a maximizer’s additional goal of “perfection” when faced with the task of
narrowing down a large assortment.
Thought and Attribute Valence. We conducted two repeated measures analyses
(multivariate normal using GENMOD in SAS)—one to compare the proportion of positive and
negative thoughts and a second comparing the proportion of positive and negative attributes
listed. Both models showed the same results. We found that participants listed more positive
thoughts when using an include (M = .66) compared to an exclude strategy (M = .39, b = .28,
27
95% CI: .21 to .34, z = 8.34, p < 0.001), and they listed fewer negative thoughts when using an
include (M = .26) compared to an exclude strategy (M = .56, b = .29, 95% CI: .23 to .36, z =
8.82, p < 0.001). We found the same pattern when examining the proportion of positive and
negative attributes listed by participants: Participants listed more positive attributes when using
an include (M = .85) compared to an exclude strategy (M = .70, b = .15, 95% CI: .09 to .22, z =
4.57, p < 0.001) and listed fewer negative attributes when using an include (M = .17) compared
to an exclude strategy (M = .29, b = .12, 95% CI: .06 to .19, z = 3.60, p < 0.001). Both results
support the notion that when using an include strategy consumers tend to focus on more positive
attributes and less on negative attributes. Thus, inclusion is more compatible with a maximizer’s
goal of focusing on the “best” options and making sure these are retained in the consideration
set.
FIGURE 1
Study 3: Results by Assortment Size and Consideration Set Construction Strategy
28
Elaboration on Alternatives. To test our prediction that consumers focus more on options
that end up in the final consideration set when including (and therefore less on options that do
not end up in the final consideration set), we divided the number of thoughts about options in the
consideration set by the number of total thoughts. The same proportion was created for the
number of thoughts about options not in the consideration set. A repeated measures analysis
(multivariate normal using GENMOD in SAS) showed that participants using an include strategy
had more thoughts about options in the consideration set compared to those using an exclude
strategy (MInclude = .73 vs. MExclude= .44, b = .29, 95% CI: .22 to .36, z = 8.39, p < 0.001).
Similarly, participants using an include strategy also had fewer thoughts about options not in the
consideration set (MInclude = .27 vs. MExclude= .56, b = .15, 95% CI: .22 to .36, z = 8.39, p < 0.001).
Thus, these results provide additional support for our proposition that inclusion is more
compatible with a maximizer’s goals of finding the best possible options because inclusion is
more focused on attractive options that are ultimately considered rather than less attractive
options that have no chance of being chosen.
Discussion
29
The results of study 3 show that consumers are more likely to use an include strategy as
the assortment size increases, consistent with studies 1A and 1B, and study 2, and that this effect
is especially strong for consumers in a maximizing mindset. The results support the notion that
large assortments lead consumers to be more selective, and thus they turn to an include strategy.
Maximizing consumers are particularly likely to be selective in their choices, which is why they
are more likely to use an include strategy when faced with choosing from a large assortment.
Further, Study 3 provides additional evidence that selectivity drives maximizers’ choice of an
include strategy when faced with large assortments. The results show that consumers using an
include strategy not only construct smaller (i.e., more selective) consideration sets, but they also
had more positive thoughts and fewer negative thoughts, focused more on positive attributes and
less on negative attributes, and elaborated less on options not in their consideration set and more
on options that ultimately made it into their consideration set.
STUDY 4
In study 4, we test our prediction that consumers in a maximizing mindset are more likely
than those in a satisficing mindset to choose an inclusion consideration set construction strategy
when faced with a large assortment by manipulating (rather than measuring) maximizing (using
a procedure drawn from Ma and Roese’s 2014 work on the maximizing mindset).
Method
Three hundred ninety-eight U.S. Mechanical Turk workers (M = 34.52 years, 67% male)
completed the short survey in exchange for $.30. The study was a 2(Assortment: Large vs.
30
Small) x 2(Mindset: Maximizer vs. Satisficer) between subjects design. Participants first
answered five questions that either primed a maximizing or satisficing mindset (adapted from
Ma and Roese 2014). They were asked to either select the “best” (maximize condition) or select
all that were “good enough” (satisficer condition). For example, “Please choose the singer you
think has the best vocal ability” versus “Please choose the singers you think are good enough to
listen to.” The options were the same in both conditions (e.g., Beyoncé, Rihanna, Akon, Shakira,
Eminem).
Then, in what was ostensibly a separate study, participants were asked to imagine that
they were trying to decide where to go on vacation with a group of friends. They were asked to
“narrow down the set to a smaller group of vacations that you would actually consider.” They
could choose to exclude, and drag-and-drop cities that they don’t like to the box on the right, or
they could include, and drag-and-drop cities that they do like to the box on the right, using a
similar procedure to that used in study 1B.
As in all our previous studies, we also used the recommended studentized residuals
method (McClelland 2000) to determine whether some observations were skewing the results.
Twenty-five participants had studentized residuals of greater than two, leaving a sample of 373
participants for use in the primary analysis. We also conducted the analysis including these
participants and the same pattern of results emerges, as noted next.
Results and Discussion
As expected, we found that, overall, participants were more likely to use an include
strategy when choosing from a large assortment (85%) compared to small assortment (72%, b =
.44, 95% CI: .49 to .71, Wald χ2(1, N = 373) = 9.55, p < .01, OR = 1.55). Further, these results
31
were moderated by whether a participant was primed with a maximizing or satisficing mindset (b
= .28, 95% CI: 01 to .56, Wald χ2(1, N = 373) = 4.01, p < .05). Consistent with study 3, where
chronic tendency to maximize versus satisfice was measured instead of manipulated, those in the
maximizing mindset condition were especially likely to choose an include strategy when
constructing a consideration set from a large assortment (92%) compared to small a assortment
(73%, b = .72, 95% CI: .28 to 1.17, χ2(1, N = 373) = 10.08, p < .01, OR = 2.06). Those primed
with a satisficing mindset, on the other hand, did not exhibit any differences in strategy due to
assortment size (71% vs. 79%, b = .15, 95% CI: -.18 to .49, χ2(1, N = 373) < 1, OR = 1.17).
When we include the dropped participants that did not follow instructions and that had large
residuals, the results do not change significantly (assortment x maximizer interaction: Wald χ2(1,
N = 398) = 2.89, p = .089): Maximizers are marginally more likely to choose an include strategy
in large compared to small assortments (χ2(1, N = 398) = 2.63, p = .1, OR =1.33), and satisficers
are not (χ2(1, N = 398) < 1, OR = .89).
Further, consistent with the results of previous studies, we find that participants had
smaller consideration sets when using an include strategy (M = 5.77) compared to an exclude
strategy (M = 8.46, b = 3.22, 95% CI: 2.86 to 3.58, t(369) = 17.67, p < .001), and this was
qualified by an assortment by consideration set strategy interaction (b = 2.78, 95% CI: 1.88 to
3.67, t(369) = 6.09, p < .001). The effects of consideration set strategy on set size were larger
when participants were screening from a large assortment (MInclude= 8.40 vs. MExclude= 13.85,
t(369) = 7.51, p < .001) compared to a small assortments (MInclude= 3.17 vs. MExclude= 3.07, t(369)
< 1). When we include dropped participants, results do not change significantly: Participants had
smaller consideration sets when including (M = 5.83) compared to excluding (M = 10.97, b =
4.05, 95% CI: 3.66 to 4.44, t(394) = 20.35, p < .001), and there was an assortment by strategy
32
interaction (b = 5.24, 95% CI: 4.35 to 6.12, t(394) = 11.63, p < .001): The effects was larger in a
large assortment (MInclude= 8.49 vs. MExclude= 18.86, t(394) = 16.11, p < .001) compared to a small
assortment (MInclude= 3.17 vs. MExclude= 3.07, t(394) < 1).
FIGURE 2
Study 4: Choice of Consideration Set Construction Strategy by Mindset and Assortment
Size
GENERAL DISCUSSION
Across five studies, we examined how elements of the decision context—amount of
information in an assortment (with larger assortments containing more information) and the
consumer’s mindset (i.e., maximizing vs. satisficing)—affect the strategy consumers choose to
narrow down an assortment to a more manageable consideration set. Drawing on the literature on
task/goal compatibility, we provided evidence that consumers select the consideration set
construction strategy most compatible with their goal in the task at hand. Although the goal when
faced with a large or small product assortment is the same—to efficiently narrow down the
assortment to a smaller, more manageable consideration set from which to make a final
33
decision—we argue that the consideration set strategy that most effectively accomplishes this
goal varies by the amount of information contained in an assortment, such that inclusion is more
compatible with constructing a consideration set from a large (vs. small) product assortment or
from an assortment containing more (vs. less) information about available options. Further,
maximizers (i.e., consumers who are searching for the “perfect” or optimal final choice) have an
additional goal. They also wish to create consideration sets that contain the “best” options in
order to increase the likelihood of ending up with the best possible final outcome. Thus,
maximizers are especially likely to use the include strategy, as it is the more selective of the two
strategies, when faced with a large assortment.
In studies 1A and 1B, we provided evidence that large assortments lead to the use of
inclusion strategies using two different product categories (chocolate and pens) and elicitation
techniques (a paper and pencil planogram and a drag-and-drop computer procedure), suggesting
that these results will hold for decisions made in both brick-and-mortar and online retail
contexts. In both studies the results showed that participants were more likely to use an include
strategy in the large (30 option) assortment than in the small (6 option) assortment condition. In
addition, this shift to an include strategy led to smaller consideration sets, supporting the notion
that an include strategy is more selective than an exclude strategy. We note that both of these
studies involved real choice consequences, as all study participants received the chocolate they
chose in study 1A and five study participants from study 1B were randomly selected to receive a
box of the pens they chose. Study 2 extended these findings by examining whether it is the added
information load associated with large assortments that leads to the use of an include strategy.
Consistent with studies 1A and 1B, more option information led to the use of more inclusion
compared to less option information while keeping the assortment size constant.
34
Studies 3 and 4 provided additional evidence that selectivity drives choice of an include
strategy when faced with large assortments, particularly for maximizing consumers. Supporting
our predictions, maximizing consumers were more likely to shift to an include strategy in large
assortments compared to satisficing consumers. Further, study 3 showed that participants using
an include strategy demonstrated a systematic difference in their choice process: Consumers
using an include strategy not only construct smaller (i.e., more selective) consideration sets, but
they also had more positive thoughts and fewer negative thoughts, focused more on positive
attributes and less on negative attributes, and elaborated less on options not in their consideration
set and more on options that ultimately made it to their consideration set. Finally, study 4
replicated the results of study 3 that maximizers are more likely to choose inclusion when faced
with a large assortment using a manipulated maximizing mindset (based on a procedure drawn
from Ma and Roese 2014).
Theoretical Contributions and Implications for Consumers
These changes in consideration set construction strategy, and the subsequent effect on
consideration set size, have important implications not only for our theoretical understanding of
consideration set construction, but also for consumer choice. First, previous literature has
demonstrated the effect of assortment size on post-choice regret and satisfaction (e.g., Chernev et
al. 2015; Iyengar and Lepper 2000; Scheibehenne et al. 2010). We show that assortment affects
the decision process itself, influencing the strategy consumers use to make the decision by
changing the consumer’s choice of consideration set construction strategy due to task/goal
compatibility. Large assortments, because of the increased information they contain, lead to a
35
different decision process where consumers become more selective and focus more on positive
attributes and on desirable options that ultimately end up in the consideration set.
Thus, consumers choosing from large assortments will deliberate more on good
alternatives that will be included in the consideration set, most of which will ultimately be
foregone (i.e., options not chosen from the consideration set). This heightened choice
deliberation on foregone alternatives may lead to post choice discomfort (Carmon, Wertenbroch,
and Zeelenberg 2003) and negative feelings, similar to regret. There are several reasons (e.g., an
increase in expectations, Diehl and Poynor 2010, larger consideration sets, Goodman et al. 2013)
why consumers experience negative consequences when choosing from large assortments
(Chernev et al. 2015; Iyengar and Lepper 2000), but our findings suggest that another potential
cause to this overchoice phenomenon could be due to the inclusion strategy used in large
assortments and the fact that it leads consumers to deliberate more on foregone alternatives in the
consideration set. Though this conclusion was not the focus of our studies, we should note that it
is consistent with our results.
Second, we also contribute to the literature examining consideration set construction as a
separate but important phase in the choice process (Chakravarti et al. 2006; Desai and Hoyer
2000; Irwin and Naylor 2009; Kardes et al. 1993; Mitra 1995). Our results show that using an
inclusion strategy results in smaller consideration sets and that large assortments lead to the use
of an include strategy. Therefore, initial assortment size or the amount of information provided
about each option in an assortment can determine the number of options that end up in a
consideration set by influencing the strategy consumers use to construct it.
Finally, we also contribute to the literature on maximizing versus satisficing (e.g., Ma
and Roese 2014; Schwartz 2004) by showing that maximizers not only strive for optimality, but
36
also use fundamentally different decision strategies to get there, given that the tendency to use an
include strategy in large assortments is strongest for maximizing consumers. Satisficers, in
contrast, were less likely to shift their strategy based on the context, suggesting that they may
have pre-set default strategies for solving decision problems. Maximizers, however, appear to be
more likely to construct their strategy (much like consumers construct their preferences, Amir
and Levav 2008) dependent on the decision context and may thus be more susceptible to various
other decision context effects. Although maximizing has been associated with many negative
consumer outcomes in the past, including decreased happiness and satisfaction and increased
regret (Schwarz et al. 2002), our research suggests that maximizing also makes consumers more
adaptive to the decision context in which they find themselves.
Practical Implications
Our research also has important implications for marketing managers, both for retailers,
who must make decisions about the assortment they will offer and how to present it, and for
manufacturers and product marketers, who must design products and decide how to promote
them in ways that increase the likelihood that the product they design ends up in a consumer’s
consideration set so that it can ultimately be chosen and purchased.
For retailers, deciding on the size of the product assortment to be offered is a key
strategic decision. Consumers are attracted to retailers that offer larger assortments (Broniarczyk
et al. 1998; Chernev and Hamilton 2009; Goodman and Malkoc 2012) but they are often more
satisfied with choices made from smaller assortments (Chernev et al. 2015; Huffman and Kahn
1998; Iyengar and Lepper 2000). Retailers have resolved this conflict in different ways, with
some choosing to cut SKUs while others have kept their large assortments with the goal of
37
attracting shoppers. Our research can help retailers decide both the types of products they should
feature as part of their assortment as well as the type of information they should highlight as part
of the shopping experience.
Based on our findings, retailers offering large assortments can predict that their
customers are more likely to use an include strategy, leading them to place more weight on
positive attributes. Thus, our results suggest that retailers offering large assortments should
highlight products and brands with clear, positive attributes, even if these products potentially
also have some negative attributes. These retailers should avoid impoverished options (i.e.,
options with average performance across a variety of attributes with no extreme positive or
negative attributes; Shafir 1993) since consumers screening via inclusion are engaging in
selective processing, focusing on positive thoughts and positive attributes. Retailers offering
large assortments would also arguably benefit from highlighting positive attributes using in-store
signage (in brick and mortar retailers) or online cues and featuring the most desirable options in
key positions in their assortment (either in-store at eye level or online). While highlighting
alternatives (e.g., “best seller” recommendation signs) can backfire and increase consideration
set size and choice difficulty (Goodman et al. 2013), highlighting positive attributes instead of
alternatives may make the screening process easier for consumers.
The reverse strategy is true for retailers offering small assortments given that their
customers are more likely to be forming consideration sets via exclusion. These retailers should
avoid offering brands with clear negative attributes as part of the assortment – even if those
brands offset their negative performance with clear positive performance on another attribute.
Impoverished options (that have average performance across all attributes) are more likely to end
up in consideration sets constructed via exclusion, as negative performance on an attribute can
38
still result in an overall desirable product being excluded. Retailers offering small assortments
and using these recommended strategies should also be cautious no to provide too much
information about each option, as increased information load can shift consumers to using an
include strategy even for small assortments.
For manufacturers, being part of the consideration set is a necessary condition for
purchase (Desai and Hoyer 2000; Nedungadi 1990). Thus, knowing how consideration sets are
formed from different assortments can increase the likelihood that one’s brand is part of the
consideration set and, therefore, increase the likelihood that it is ultimately chosen for purchase.
Just as retailers offering large assortments should make sure these assortments contain products
and brands with clear positive attributes (vs. more options with average performance across all
attributes but no clear positives or negatives), manufacturers who expect to sell their products
either at retailers who offer large assortments need to avoid impoverished options, focusing
instead on options with clear positive attributes. In contrast, if the product will instead be
featured as part of a small assortment (e.g., via warehouse, boutique, or small online/direct-toconsumer channels), marketers should avoid any negative attributes (even at the cost of including
positive attributes) given that consumers will likely use exclusion to form their consideration set
in these contexts.
Thus, for a manufacturer in a competitive environment with several offerings, these
findings translate into offering (and promoting) more enriched options (i.e., those with extreme
positive attributes), even if it comes at the expense of increasing the saliency of more negative
attributes, and fewer neutral impoverished options (i.e., those with neither strong positives nor
strong negative attributes). Manufacturers producing such options might also consider promoting
them in a way likely to induce a maximizing mindset (either on package or in other promotional
39
messages), representing the product as “the best” on a particular attribute or “the perfect
solution” for a particular problem, since a maximizing mindset will increase the likelihood that
consideration sets are constructed using inclusion, where their products will be more likely to
end up in consideration sets. On the flip side, manufacturers in smaller, more homogeneous
markets, with few options available to the consumer will benefit from offering more neutral and
less extreme options.
We should note that there are likely other tools that retailers might be able to use to
encourage one type of consideration set construction strategy over the other. For example, real
estate broker websites often allow consumers to save their favorite properties (an include
strategy), and nearly all websites encourage consumers to place options in their cart (an include
strategy). Some (e.g., Amazon.com) also allow consumers to add options to wish lists or
shopping lists. Retailers could also consider using in-store messaging consistent with a
maximizing mindset (e.g., “find the perfect gift today,” “you deserve only the best”) in order to
encourage the use of an inclusion strategy.
There are fewer tools, however, that allow consumers to eliminate or exclude options that
are not acceptable. Retailers, especially those on the Internet, may want to consider offering tools
that allow consumers to use both exclude and include strategies to be more compatible with a
consumer’s goal. For instance, the original Sidestep.com website allowed consumers to save
(i.e., include) in a consideration set potential flight, car, and hotel reservations and/or remove
entire reservations that are not acceptable (i.e., an exclude strategy). In the end, encouraging
consumers to use either strategy (include or exclude) to narrow the options down to a
manageable set depending on which is best for achieving their goal is likely to provide additional
customer insights and lead to a more satisfied customer.
40
APPENDIX A
Study 1A and Study 2 Instructions (Study 2 modified to refer to vacations)
On the next page you will see a display of chocolates. We would like to know which of these chocolates
you would consider buying. That is, we would like you to narrow down the display to a smaller group of
chocolates that you would actually consider buying.
To thank you for participating, we will actually give you one of the chocolates that you choose later in the
experiment, so keep that in mind when making your choices!
Now, there are two ways you may narrow down the display. You can use either method—it doesn’t
matter to us. You can use:
Option (1): Exclusion Decide which chocolates you WOULD NOT consider and then CROSS-OUT
these choices, or
Option (2): Inclusion Decide which chocolates you WOULD consider and then CIRCLE these choices.
After you look at the chocolates, you may then decide which way you will narrow down the display into a
smaller group. You may CROSS-OUT the ones you do NOT like OR CIRCLE the ones you DO like —
it’s up to you!
Example
Let’s say there are 4 chocolates to choose from:
Chocolate A
Chocolate B
Chocolate C
Chocolate D
Of these 4 chocolates, let’s say you would consider buying only 2 of them (chocolates A and D). You
would narrow the 4 chocolates down to 2 chocolates in ONE OF TWO METHODS:
I. If you chose Option (1) Exclusion you would CROSS OUT the chocolates that you DID NOT like:
Chocolate A
Chocolate B
Chocolate C
Chocolate D
II. If you chose Option (2) Inclusion you would CIRCLE the chocolates that you DID like:
Chocolate A
Chocolate B
Chocolate C
Chocolate D
You may now go to the real display of chocolates and decide which method you would use.
41
Appendix B
Study 2: Stimuli Example
Vacation with two reviews (low information load)
Vacation Destination: Aruba
Aruba is known for its all sun and sea with stretches of powdery, white sand. Not too far from
Eagle Beach, you will also find Arikok National Park with various hiking trails as well as
caves.
Review 1: “Beautiful beach, the bluest water and it wasn't too wavy. Not too crowded and it is
very laid back. There is a great breeze that kept it from being too hot. The sand is so soft and
never hot to walk on without sandals. One of the prettiest beaches I've ever had the pleasure of
visiting.”
Review 2: “Although it’s a beautiful beach it is full of tourists. The water is a little foggy due
to the water crafts which are offered there. I spent a day there to see what the "resort goers"
were doing, but I preferred driving a little bit to visit the other beaches like ARASHI and
BABY BEACHES.” - Julieta
Vacation with six reviews (high information load)
Vacation Destination: Aruba
Aruba is known for its all sun and sea with stretches of powdery, white sand. Not too far from
Eagle Beach, you will also find Arikok National Park with various hiking trails as well as
caves.
Review 1: “One of the most beautiful beaches I've been to. Amazing. The water is crystal clear
and warm. No worries about sea life lurking around. I haven't encountered any since I was
there.”
Review 2: “Beautiful beach, the bluest water and it wasn't too wavy. Not too crowded and it is
very laid back. There is a great breeze that kept it from being too hot. The sand is so soft and
never hot to walk on without sandals. One of the prettiest beaches I've ever had the pleasure of
visiting.”
Review 3: “Traveled there a few years ago (3-5). It was great. Beautiful blue water, nice soft
sand, wide expanse of beach. Most of the hotels host palapas for shade. Sit back with a good
book and watch the dinner sunset cruise and sailboats go by. The locals are very friendly. Low
rise section of the beach.”
Review 4: “This is a beautiful white sand, blue-green clear water beach. Absolutely gorgeous.
There's not a lot to see if you like to snorkel. If you love a beautiful Caribbean beach to relax
and swim, this is the place. The wind can be strong and whip the sand at you, but that's only
certain days, and common at Aruban beaches.” – Sue S.
Review 5: “The wind was so strong that the sand slammed into you as well as other guest’s
belongings. Water was beautiful.” – Jean B.
Review 6: “Although it’s a beautiful beach it is full of tourists. The water is a little foggy due
to the water crafts which are offered there. I spent a day there to see what the "resort goers"
were doing, but I preferred driving a little bit to visit the other beaches like ARASHI and
BABY BEACHES.” - Julieta
42
Appendix C
Study 3: Additional Instructions and Measures
Remember, please write ALL your thoughts, including those dealing with the products as
well as any other random thoughts you might have. For instance, we are interested in:
1) Which items you are considering or not considering
2) Reasons why you are considering or not considering each item
Think back to when you were narrowing down the set of chocolates. Which attributes
where important to you when narrowing down the set of chocolates? List them below:
Would you say that each attribute is positive or negative? Write either a positive (+) or a
negative (-) sign next to each attribute that you just wrote down to indicate whether each attribute
is positive or negative.
Please look back at the chocolates that you considered. If you had to choose ONE chocolate,
which chocolate would you choose?
Write the name of the chocolate you would like to receive:
The following questions are about your choice of ONE chocolate:
Regret, Difficulty, and Satisfication
 When you were trying to decide, how concerned were you that other choices might be
better than the one you were considering? (1= Not At All Concerned, 7=Extremely
Concerned)
 When you were trying to decide, how concerned were you that you might regret your
decision? (1= Not At All Concerned, 7=Extremely Concerned)
 How confident are you about your choice of chocolate? (1=Not At All Confident,
7=Extremely Confident)
 How difficult was it to make your decision of which chocolate to pick? (1=Not At All
Difficult, 7=Extremely Difficult)
 How complex did you find it to make your decision of which chocolate to pick? (1=Not
At All Complex, 7=Extremely Complex)
 How much did you enjoy making the choice of a chocolate? (1=Not At All Enjoy,
7=Extremely Enjoyed)
 How pleasant did you find the process of making the choice of a chocolate? (1=Not At
All Pleasant, 7=Extremely Pleasant)
 How frustrated did you feel when making the choice of a chocolate? (1=Not At All
Frustrated, 7=Extremely Frustrated)
 How annoyed did you feel while you were making the choice of a chocolate? (1=Not At
All Annoyed, 7=Extremely Annoyed)
 How overwhelmed did you feel while making the choice of a chocolate? (1=Not At All
Overwhelmed, 7=Extremely Overwhelmed)
 How satisfied do you think you will be with the chocolate you chose? (1=Not At All
Satisfied, 7=Extremely Satisfied)
43
Affect
How do you feel right now? (1=Not At All, 7=Very Much)
 I am happy
 I am calm
 I am frustrated
 I am annoyed
 I am upset
Involvement (1=Not At All, 7=Very Much)
 How important is knowledge of chocolates in your life?
 How interested are you in the subject of chocolates?
 How often do you find yourself thinking about chocolates?
Need for Cognition (1=Strongly Disagree, 7=Strongly Agree)
 I enjoy a task that involves coming up with solutions to problems.
 I would rather do something that requires a little thought than something that is sure to
challenge my thinking abilities.
Maximizer/Satisficer (1=Strongly Disagree, 7=Strongly Agree)
 When I watch TV, I channel surf, often scanning through the available options even
while attempting to watch one program.
 When I am in the car listening to the radio, I often check other stations to see if
something better is playing, even if I’m relatively satisfied with what I’m listening to.
 I often find it difficult to shop for a gift for a friend.
 No matter what I do, I have the highest standards for myself.
44
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