ArticlePDF Available

Choice Overload: A Conceptual Review and Meta-Analysis

Journal of Consumer Psychology

April 2015
25(2):Pages 333–358

DOI:10.1016/j.jcps.2014.08.002

Authors:

Alexander Chernev

Northwestern University

Ulf Bockenholt

Northwestern University

Joseph Goodman

Despite the voluminous evidence in support of the paradoxical finding that providing individuals with more options can be detrimental to choice, the question of whether and when large assortments impede choice remains open. Even though extant research has identified a variety of antecedents and consequences of choice overload, the findings of the individual studies fail to come together into a cohesive understanding of when large assortments can benefit choice and when they can be detrimental to choice. In a meta-analysis of 99 observations (N = 7,202) reported by prior research, we identify four key factors—choice set complexity, decision task difficulty, preference uncertainty, and decision goal—that moderate the impact of assortment size on choice overload. We further show that each of these four factors has a reliable and significant impact on choice overload, whereby higher levels of decision task difficulty, greater choice set complexity, higher preference uncertainty, and a more prominent, effort-minimizing goal facilitate choice overload. We also find that four of the measures of choice overload used in prior research—satisfaction/confidence, regret, choice deferral, and switching likelihood—are equally powerful measures of choice overload and can be used interchangeably. Finally, we document that when moderating variables are taken into account the overall effect of assortment size on choice overload is significant—a finding counter to the data reported by prior meta-analytic research.

Conceptual model of the impact of assortment size on choice overload. Note.-The four antecedents of choice overload are operationalized as follows: (1) The complexity of the choice set describes the aspects of the decision set associated with the particular values of the choice options: the presence of a dominant option in the choice set, the overall attractiveness of the options in the choice set, and the relationship between individual options in the decision set (alignability and complementarity); (2) The difficulty of the decision task refers to the general structural characteristics of the decision problem: time constraints, decision accountability, and number of attributes describing each option; (3) Preference uncertainty refers to the degree to which individuals have articulated preferences with respect to the decision at hand and has been operationalized by two factors: the level of product-specific expertise and the availability of an articulated ideal point; and (4) The decision goal reflects the degree to which individuals aim to minimize the cognitive effort involved in making a choice among the options contained in the available assortments and is operationalized by two measures: decision intent (buying vs. browsing) and decision focus (choosing an assortment vs. choosing a particular option). In this context, we expect higher levels of decision task difficulty, greater choice set complexity, higher preference uncertainty, and a more prominent, effort-minimizing goal to produce greater choice overload.

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d. Forest plot showing how decision goal influences the impact of assortment size on choice overload. (continued).

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e. Forest plot showing the main effect (no moderators) of assortment size on choice overload. (continued).

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c. Forest plot showing how preference uncertainty influences the impact of assortment size on choice overload. (continued).

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b. Forest plot showing how decision task difficulty influences the impact of assortment size on choice overload. (continued).

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Research Review

Choice overload: A conceptual review and meta-analysis

Alexander Chernev

⁎, Ulf Böckenholt

, Joseph Goodman

Kellogg School of Management, Northwestern University, USA

Compass Lexecon, Chicago, USA

Received 5 October 2012; received in revised form 18 August 2014; accepted 23 August 2014

Available online 29 August 2014

Abstract

Despite the voluminous evidence in support of the paradoxical ﬁnding that providing individuals with more options can be detrimental to

choice, the question of whether and when large assortments impede choice remains open. Even though extant research has identiﬁed a variety of

antecedents and consequences of choice overload, the ﬁndings of the individual studies fail to come together into a cohesive understanding of when

large assortments can beneﬁt choice and when they can be detrimental to choice. In a meta-analysis of 99 observations (N= 7202) reported by

prior research, we identify four key factors—choice set complexity, decision task difﬁculty, preference uncertainty, and decision goal—that

moderate the impact of assortment size on choice overload. We further show that each of these four factors has a reliable and signiﬁcant impact on

choice overload, whereby higher levels of decision task difﬁculty, greater choice set complexity, higher preference uncertainty, and a more

prominent, effort-minimizing goal facilitate choice overload. We also ﬁnd that four of the measures of choice overload used in prior research—

satisfaction/conﬁdence, regret, choice deferral, and switching likelihood—are equally powerful measures of choice overload and can be used

interchangeably. Finally, we document that when moderating variables are taken into account the overall effect of assortment size on choice

overload is signiﬁcant—aﬁnding counter to the data reported by prior meta-analytic research.

Keywords: Choice overload; Assortment; Decision complexity; Meta-analysis

Contents

Introduction ............................................................... 334

The pros and cons of large assortments ................................................. 334

Choice overload in consumer decision making ............................................. 335

Choice overload in individual decision making ........................................... 335

Conceptualizing the impact of assortment size on choice overload .................................. 336

Decision task difﬁculty ........................................................ 337

Choice set complexity ........................................................ 337

Preference uncertainty ........................................................ 338

Decision goal ............................................................. 339

Method ................................................................. 339

The data ............................................................... 339

The model .............................................................. 340

Results .................................................................. 344

Model ﬁt............................................................... 344

Effects of the speciﬁc moderators of choice overload ........................................ 344

⁎Corresponding author. Fax: +1 847 491 2498.

E-mail address: ach@kellogg.northwestern.edu (A. Chernev).

http://dx.doi.org/10.1016/j.jcps.2014.08.002

Available online at www.sciencedirect.com

ScienceDirect

Journal of Consumer Psychology 25, 2 (2015) 333 –358

The mean effect of assortment size on choice overload ........................................ 346

The effect of assortment size across individual experiments ..................................... 348

Publication bias ............................................................ 350

Discussion ................................................................ 351

Key ﬁndings ............................................................. 351

Prior meta-analytic research ...................................................... 352

Future research ............................................................ 353

Appendix A. Overview of the analyzed studies .............................................. 354

Appendix B. Reanalyzing the data from prior meta-analytic research .................................. 356

References ................................................................ 354

Introduction

The importance of assortment decisions for both retailers and

manufacturers has been underscored by numerous research

articles, marketing textbooks, and the popular press (Iyengar,

2010; Levy & Weitz, 2006; Schwartz, 2003). Because of its

importance, the topic of how product assortment influences

consumer choice has generated a substantial amount of interest

across different research domains, including economics, analytical

and empirical modeling, individual and group decision making,

and social psychology (Broniarczyk, 2008; Chernev, 2012; Kahn,

1999; Kahn, Weingarten, & Townsend, 2013; Lancaster, 1990;

Lehmann, 1998; Simonson, 1999).

Within assortment research, the topic of the negative

consequences of large assortments has attracted a disproportion-

ate amount of interest among researchers. This interest can be

attributed largely to the paradoxical finding that variety can be

detrimental to choice, which challenged the conventional wisdom

that providing consumers with more options always facilitates

choice (Iyengar & Lepper, 2000; Reibstein, Youngblood, &

Fromkin, 1975). Building on these findings, recent research has

moved beyond simply documenting choice overload to identify-

ing its antecedents and boundary conditions. In doing so,

researchers have identified a number of important moderators

of choice overload, such as attribute alignability (Gourville &

Soman, 2005), consumer expectations (Diehl & Poynor, 2010),

availability of an ideal point (Chernev, 2003b), personality traits

and cultural norms (Iyengar, Wells, & Schwartz, 2006), option

attractiveness (Chernev & Hamilton, 2009), decision focus

(Chernev, 2006), construal level (Goodman & Malkoc, 2012),

time pressure (Haynes, 2009), product type (Sela, Berger, & Liu,

2009), consumer expertise (Mogilner, Rudnick, & Iyengar,

2008), and variety seeking (Oppewal & Koelemeijer, 2005).

Despite the voluminous evidence that large assortments can

lead to choice overload, the question of whether and when large

assortments are detrimental to choice remains open. Indeed, even

though extant research has identified a variety of antecedents and

consequences of choice overload, the individual studies use

diverse independent and dependent variables. As a result, the

findings of these studies fail to come together in a cohesive

understanding of whether and when assortment size is likely to

lead to choice overload. The goal of our research, therefore, is to

identify factors that reliably moderate the impact of assortment

size on choice overload and generalize them into an overarching

conceptual framework. To achieve this goal, we abstract from the

specific variables and manipulations reported in the individual

studies to identify the key drivers of choice overload, quantify the

effect sizes associated with these factors, and evaluate their

impact on choice overload.

Our analysis is organized as follows.First,wediscussthepros

and cons of large assortments, focusing on how assortment size

influences individual decision processes. This is followed by a

conceptual analysis of the antecedents of choice overload, in

which we identify four key drivers that are likely to influence the

impact of assortment size on choice overload. We then present our

methodology in more detail, followed by a summary of our key

findings. This research concludes with a discussion in which we

highlight our theoretical contributions, discuss the managerial

implications, and outline directions for future research.

The pros and cons of large assortments

Offering consumers a large variety of options to choose from

can have a two-pronged impact on choice: It can both benefit and

hinder choice. The most intuitive benefit, featured prominently in

economics research, is that the greater the number of options in the

choice set, the higher the likelihood that consumers can find a close

match to their purchase goals (Baumol & Ide, 1956; Hotelling,

1929). A related economic explanation of consumer preference for

larger assortments involves the greater efficiency of time and effort

involved in identifying the available alternatives in the case of

one-stop shopping associated with retailers offering larger

assortments (Betancourt & Gautschi, 1990; Messinger &

Narasimhan, 1997).

It has also been proposed that larger assortments might lead to

stronger preferences because they offer option value (Reibstein et

al., 1975), allow consumers to maintain flexibility in light of

uncertainty about future tastes (Kahn & Lehmann, 1991;

Kahneman & Snell, 1992; Kreps, 1979), and accommodate

consumers' future variety-seeking behavior (Inman, 2001; Kahn,

1995; Levav & Zhu, 2009; Ratner, Kahn, & Kahneman, 1999;

Van Herpen & Pieters, 2002). It has further been argued that

consumers might experience additional utility simply from having

multiple items in the choice set because it creates the perception of

freedom of choice (Kahn, Moore, & Glazer, 1987), enhances the

enjoyment of shopping (Babin, Darden, & Griffin, 1994), and

strengthens overall choice satisfaction (Botti & Iyengar, 2004).

Finally, it has been proposed that larger assortments influence

consumer preferences by reducing the uncertainty of whether the

357

334 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

choice set at hand adequately represents all potentially available

options. Prior research has shown that consumers may delay their

purchasing because they are uncertain about the degree to which

the available set is representative of the entire roster of possible

options (Greenleaf & Lehmann, 1995; Karni & Schwartz, 1977).

To illustrate, consumers might feel more confident when

selecting from a retailer that offers a larger assortment because

it is less likely that a potentially superior alternative is not

represented in the available choice set.

Despite their multiple benefits, larger assortments have a

number of important drawbacks. Prior research has shown that the

benefits of greater variety are, at least partially, offset by a

corresponding increase in the cognitive costs associated with

choosing from a larger assortment. In this context, it has been

shown that reducing the size of an assortment can actually increase

the purchase likelihood from that assortment. For example, it was

shown that consumers were more likely to make a purchase when

presented with an assortment comprising 6 flavors of jam than

with an assortment comprising 24 flavors (Iyengar & Lepper,

2000). Similar findings have been documented in a variety of

product categories, such as chocolates (Berger, Draganska, &

Simonson, 2007; Chernev, 2003b), consumer electronics

(Chernev, 2003a), and mutual funds (Huberman, Iyengar, &

Jiang, 2007; Ketcham, Lucarelli, Miravete, & Roebuck, 2012;

Morrin,Broniarczyk,Inman,&Broussard,2008).

Recent research also has argued that consumer preference

for larger assortments is likely to be subject to diminishing

returns because the marginal benefits from each additional

alternative tend to decrease with the increase in assortment size

(Chernev & Hamilton, 2009; Oppewal & Koelemeijer, 2005).

Given that the increase in benefits accrues at a decreasing rate,

at some point it is likely to be offset by the additional costs of

evaluating the available alternatives (Roberts & Lattin, 1991).

Thus, it has been shown that the probability of purchasing a

brand, reflected in the brand's market share, tends to decrease

after the product line achieves a certain size (Draganska & Jain,

2005).

It has further been argued that larger assortments tend to

shift consumers' ideal points in a way that makes them more

difficult to attain (Chernev, 2003b; Schwartz et al., 2002).

Larger assortments have also been found to inflate consumers'

expectations of finding their ideal option in the available

assortment and the degree of preference match they can achieve

(Diehl & Poynor, 2010). Consequently, it has been proposed

that choices from larger assortments can lead to disconfirmation

of consumer expectations, resulting in greater choice deferral

and lower satisfaction with the chosen option.

In this research we focus on the negative consequences of

large assortments, specifically those factors that are likely to

influence whether and how larger assortments will produce

choice overload. We discuss the antecedents and consequences

of choice overload in more detail in the following sections.

Choice overload in consumer decision making

We start by discussing prior research examining the impact of

assortment size on choice overload to underscore the importance

of developing a theory-based approach to generalizing the

findings of the individual studies. We then propose a conceptual

model that identifies four key factors that influence the impact of

assortment size on choice overload.

Choice overload in individual decision making

The term choice overload—also referred to as overchoice—

is typically used in reference to a scenario in which the

complexity of the decision problem faced by an individual

exceeds the individual's cognitive resources (Simon, 1955;

Toffler, 1970). In this research, our main focus is on a particular

type of choice overload—one in which the decision complexity

is caused, at least partially, by the (large) number of available

decision alternatives (Iyengar & Lepper, 2000).

Because choice overload is a mental construct describing

the subjective state of the decision maker, it cannot be

directly observed; instead, it is reflected in a series of

objective indicators, which, in turn, are used to measure

choice overload. In this context, two types of indicators of

choice overload can be identified: process-based indicators

describing the subjective state of the decision maker and

outcome-based indicators reflecting the decision maker's

observable behavior.

As a subjective state, choice overload is captured by

changes in consumers' internal states, such as decision

confidence, satisfaction, and regret, whereby higher levels

of choice overload are likely to produce lower levels of

satisfaction/confidence and higher levels of regret. Thus,

compared to individuals not experiencing choice overload,

those experiencing overload are (1) less likely to be satisfied

with their decisions (Botti & Iyengar, 2004), (2) less

confident that they have chosen the best option (Haynes,

2009), and (3) prone to more post-decision regret (Inbar,

Botti, & Hanko, 2011).

Behavioral consequences of choice overload, on the other

hand, include factors that capture consumer actions such as the

likelihood of deferring choice, the likelihood of reversing an

already made choice, the preference for larger assortments, and

the nature of the ultimately chosen option. In this context,

greater levels of choice overload are associated with greater

probability of choice deferral, greater switching likelihood,

decreased preference for larger assortments, and greater

preference for easily justifiable options. Thus, compared to

individuals not experiencing choice overload, those experienc-

ing overload are (1) less likely to make a choice from a

particular assortment (Iyengar & Lepper, 2000), (2) more

likely to reverse their initial choice (Chernev, 2003b), (3) less

likelytodisplayapreference for larger assortments (Chernev,

2006),and(4)morelikelytochooseanoptionthatcanbe

easily justified (Sela et al., 2009).

Note that the above indicators of choice overload do not

represent a complete list of all viable measures of choice

overload; rather, these are the measures that represent the most

common decision scenarios and have been frequently utilized

by prior research.

335A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

Conceptualizing the impact of assortment size on choice

overload

Recent assortment research has identified a number of

important antecedents and consequences of choice overload.

While this plethora of predictors and measures facilitates

understanding the impact of assortment size on choice

overload, it also complicates generalization of the findings of

the individual studies because different experiments use diverse

independent and dependent variables. Indeed, whereas numer-

ous studies identify a variety of factors that are likely to

influence choice overload—including attribute alignability,

attribute complementarity, ideal point availability, option

attractiveness, consumer expertise, variety seeking, time

pressure, product type, and need for cognition—convergence

of these findings is difficult to achieve because there is little

overlap between the study-specific independent variables. In

the same vein, different studies use different dependent

variables to measure choice overload, including satisfaction,

confidence, the likelihood of deferring choice, and the

likelihood of switching to an alternative option. Therefore, in

order to make meaningful cross-study comparisons, one must

generalize the study-specific moderators and measures into

theoretically meaningful constructs and examine whether and

how these constructs influence choice overload.

Generalizing the effects of study-specific factors involves

identifying higher level constructs and linking each study factor to

one of these constructs. Accordingly, in this research we identify

four such constructs and then test their validity by examining the

ability of these constructs to explain the variance in prior studies.

On a more general level, we view the impact of assortment size on

consumer decision processes as a function of two types of factors:

(1) extrinsic factors that define the decision problem and are

similar across individuals and (2) intrinsic factors that reflect

individuals' idiosyncratic knowledge and motivation and are

particular to each decision maker.

Building on prior research in the domain of behavioral

decision theory and choice, we divide extrinsic factors into two

categories: task factors and context factors (Payne, Bettman, &

Johnson, 1993). Here, task factors describe the general structural

characteristics of the decision problem, including number of

alternatives, number of attributes describing each option, time

constraints, decision accountability, and information presentation

mode. In contrast, context factors describe the aspects of the

decision associated with the particular values of the choice

options, including the similarity and the overall attractiveness of

the alternatives. In this research we refer to the task factors and

their impact on choice overload as decision task difficulty and to

the context factors as choice set complexity.

Unlike extrinsic factors, which refer to the characteristics of

the decision problem and are similar across individuals,

intrinsic factors are particular to the decision maker. Two

specific factors have been discussed in prior research examin-

ing the impact of assortment size on choice overload:

preference uncertainty and decision goals. Here, preference

uncertainty refers to the degree to which individuals have

articulated preferences with respect to the decision at hand and

includes factors such as the level of product-specific expertise

and the availability of an articulated ideal point (Chernev,

2003b). The decision goal, on the other hand, reflects the

degree to which a consumer's goal involves choosing among

the options in a given assortment (Chernev & Hamilton, 2009).

Our theorizing is summarized in Fig. 1. The four factors

discussed above—decision task difficulty, choice set com-

plexity, preference uncertainty, and decision goal—comprise

the key moderators that could potentially influence the impact

Number

of options

Choice set

complexity

Subjective

state

Choice

satisfaction

Decision

regret

Choice

overload

Decision task

diculty

Preference

uncertainty

Decision

goal Behavioral

outcome

Choice

deferral

Switching

likelihood

Assortment

choice

Antecedents of choice overload Consequences of choice overload

Option

selection

Decision

condence

Extrinsic (objective) factors

Intrinsic (subjective) factors

Fig. 1. Conceptual model of the impact of assortment size on choice overload. Note.—The four antecedents of choice overload are operationalized as follows: (1) The

complexity of the choice set describes the aspects of the decision set associated with the particular values of the choice options: the presence of a dominant option in

the choice set, the overall attractiveness of the options in the choice set, and the relationship between individual options in the decision set (alignability and

complementarity); (2) The difficulty of the decision task refers to the general structural characteristics of the decision problem: time constraints, decision

accountability, and number of attributes describing each option; (3) Preference uncertainty refers to the degree to which individuals have articulated preferences with

respect to the decision at hand and has been operationalized by two factors: the level of product-specific expertise and the availability of an articulated ideal point; and

(4) The decision goal reflects the degree to which individuals aim to minimize the cognitive effort involved in making a choice among the options contained in the

available assortments and is operationalized by two measures: decision intent (buying vs. browsing) and decision focus (choosing an assortment vs. choosing a

particular option). In this context, we expect higher levels of decision task difficulty, greater choice set complexity, higher preference uncertainty, and a more

prominent, effort-minimizing goal to produce greater choice overload.

336 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

of assortment size on choice overload. Choice overload is

then measured as a subjective state of the decision maker

(satisfaction, confidence, and regret) and/or as a specific

behavioral outcome (choice deferral, switching likelihood,

assortment choice, and option selection).

We discuss the impact of each of the four factors outlined in

Fig. 1 in more detail and make directional predictions regarding

their impact in the following sections. We then proceed to test

the validity of the proposed model by examining its ability to

account for the findings reported by the existing empirical data.

When discussing the specific measures encompassed by each of

the above factors, we focus on measures that have been used by

prior research and, thus, are available for the purposes of

meta-analysis. In the general discussion section, we address

some of the additional measures that are likely to moderate the

impact of assortment on choice overload but have not yet

gained empirical support.

Decision task difficulty

The difficulty of the decision task reflects the general

structural characteristics of the decision problem without

influencing the values of the particular choice options (Payne et

al., 1993). Prior research has argued that a number of

decision-task factors—including time constraints,decision

accountability,number of attributes describing each option,

and presentation format—are likely to influence the impact of

assortment size on choice overload. We discuss the effects of

these three factors on choice overload in more detail below.

One decision task factor that is likely to influence the impact of

assortment size on choice overload is the imposition of time

constraints. Specifically, it has been argued that an external limit

on the length of the evaluation period increases the cognitive

challenge associated with making a choice and forces consumers

to engage in a less systematic evaluation of the available

alternatives (Bettman, Luce, & Payne, 1998). This nonsystematic

processing of the available alternatives, in turn, has been shown to

lower consumer satisfaction with the chosen alternatives and

diminish consumers' confidence in their decisions (Dhar &

Nowlis, 1999; Haynes, 2009). It has further been shown that the

impact of the assortment size on decision regret is a function of the

time pressure experienced by individuals, such that the subjective

feeling of being rushed accounts for greater regret when choosing

from larger sets (Inbar et al., 2011).

Another decision task factor that could influence the impact

of assortment size on choice overload is decision accountability

(e.g., requiring consumers to justify their choices). Related

research has shown that preference for larger assortments tends

to increase when consumers expect to have to justify their

choice of an assortment to others but tends to decrease when

consumers have to justify the choice of a particular option from

the available assortments (Chernev, 2006; Ratner & Kahn,

2002; Scheibehenne, Greifeneder, & Todd, 2009). It has further

been shown that when making a choice from a given

assortment, decision accountability can decrease the likelihood

of making a choice from larger (relative to smaller) assortments

(Gourville & Soman, 2005).

Yet another decision task factor likely to influence the

impact of assortment size on choice overload is the number of

attributes describing the available options (Chernev, 2003b;

Greifeneder, Scheibehenne, & Kleber, 2010; Hoch, Bradlow, &

Wansink, 1999). Specifically, it has been argued that the more

dimensions on which products are differentiated, the more

complex a choice becomes since consumers need to sift through

additional information to compare the options before ultimately

making a choice. Thus, choosing from a set of items described

along a single dimension (e.g., color) is likely to be less

cognitively taxing than choosing from a set of items described

along multiple attributes (e.g., color, design, durability). In

addition to increasing the amount of information that needs to

be evaluated, increasing the number of attributes describing the

available options also increases the number of dimensions on

which each of the available options is inferior to the other

options in the choice set, further complicating choice.

Prior research has further argued that the impact of

assortment size on choice overload can also be influenced by

the presentation format of the individual options. Thus,

ordering options in a given assortment has been found to

decrease search costs, thus decreasing the difficulty of choosing

an item from larger assortments (Diehl, 2005; Diehl, Kornish,

& Lynch, 2003; Mogilner et al., 2008). In the same vein,

research by Hoch et al. (1999) has documented that consumers

are more satisfied with and are likely to choose assortments that

offer a high variety of options displayed in an organized rather

than random manner. Recent research has further shown that

choice overload is likely to be a function of whether the

assortments are presented in a visual or verbal format, whereby

visual presentation is associated with less systematic processing

and is more likely to lead to overload in the case of larger

assortments compared to verbal (text-based) presentation

(Townsend & Kahn, 2014).

Building on the above findings, we expect that decision task

difficulty can have a significant impact on the way assortment

size influences choice overload. Specifically, we expect that

higher levels of decision task difficulty—operationalized in

terms of time constraints, decision accountability, number of

attributes describing each option, and the complexity of the

presentation format—will lead to greater choice overload.

Choice set complexity

The complexity of the choice set reflects the aspects of the

decision task that influence the values of the particular choice

options without necessarily influencing the structural aspects of

the decision problem at hand (Payne et al., 1993). Prior research

has argued that a number of choice-set-complexity factors,

including the presence of a dominant option,theoverall

attractiveness of the choice options,aswellasthealignability

and the complementarity of the options, are likely to influence the

impact of assortment size on choice overload. We discuss the

effects of these factors on choice overload in more detail below.

One of the determinants of the complexity of the choice set is

whether it contains a dominant option—that is, an option superior

to all other available options for a given individual (Huber, Payne,

337A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

& Puto, 1982). In this context, it has been shown that consumers

are more likely to make a purchase from an assortment when it

contains a dominant option than when such an option is absent

(Boatwright & Nunes, 2001; Broniarczyk, Hoyer, & McAlister,

1998; Chernev, 2006; Oppewal & Koelemeijer, 2005). Similarly,

adding an inferior option that enhances the dominance of one of

the existing options has been shown to increase the likelihood that

a choice will be made from an assortment (Dhar, 1997), whereas

adding equally attractive optionshasbeenreportedtohavethe

opposite effect, increasing the likelihood that choice will be

deferred (Dhar, 1997; Dhar & Simonson, 2003; Tversky & Shafir,

1992). Furthermore, adding an inferior option has been shown to

increase the share of the dominant option, a finding commonly

referred to as the attraction effect (Huber et al., 1982; Simonson,

1989; Simonson & Tversky, 1992). Thus, the finding that

consumers are more likely to make a purchase from an assortment

containing a dominant option is consistent with the notion that the

availability of a dominant option decreases choice overload,

consequently increasing probability of purchase.

The impact of assortment size on choice overload is also

influenced by the attractiveness of the choice options.Some

assortments comprise options that are of higher quality and, hence,

are likely to be perceived as more attractive, whereas other

assortments comprise options that are of lower quality and are

likely to be perceived as relatively less attractive. In this context,

prior research has argued that option attractiveness influences the

way consumers choose among assortments, such that smaller

assortments are preferred to larger ones when these assortments

are composed of more attractive rather than less attractive options

(Chernev & Hamilton, 2009). Thus, consumers are more likely to

prefer smaller assortments when these assortments are curated to

include the most attractive options from larger assortments.

Prior research has further shown that the impact of assortment

size on choice overload is a function of the alignability of the

attributes describing the options in the assortment. Here alignability

describes the relationships among the attribute levels of the options

in a given assortment. Nonalignable attributes describe a scenario

in which a given feature is present in one of the options and absent

in the others, whereas alignable attributes describe a scenario in

which objects have different (but nonzero) levels of a given

attribute (Markman & Medin, 1995). In this context, it has been

argued that increasing the size of assortments whose options are

differentiated by alignable attributes reportedly can lead to an

increase in purchase probability from that assortment, whereas

increasing the size of assortments differentiated by options with

nonalignable attributes has been shown to have the opposite effect

of decreasing purchase probability (Gourville & Soman, 2005).

Moreover, research has linked attribute alignability to satisfaction

with choice, following an inverted U-shape for options differen-

tiated on nonalignable (but not alignable) attributes (Griffin &

Broniarczyk, 2010).

A related argument has been advanced by Chernev (2005),who

shows that the impact of assortment size on choice overload is also

a function of feature complementarity, defined as the extent to

which features complement one another with respect to their ability

to fulfill a particular consumer need. Thus, increasing a product

assortment by adding options differentiated by complementary

features tends to lower the attractiveness of all alternatives in that

assortment. In this context, purchase likelihood from a given

assortment is shown to be a function of the complementarity of its

options, such that choice deferral is greater for assortments

comprising complementary rather than noncomplementary op-

tions. Furthermore, increasing assortment size by adding noncom-

plementary options tends to increase purchase likelihood from the

assortment, whereas increasing assortment size by adding

complementary options tends to decrease purchase likelihood

from the assortment.

The above research suggests that choice set complexity can

have a significant impact on whether and how assortment size

influences choice overload. Specifically, we expect that higher

levels of choice set complexity—operationalized in terms of the

presence of a dominant option, as well as the overall

attractiveness, alignability, and complementarity of the choice

options—will lead to greater choice overload.

Preference uncertainty

Preference uncertainty refers to the degree to which

individuals have articulated preferences with respect to the

decision at hand, meaning that they understand the benefits of

the choice options and can prioritize these benefits when

trading off the pros and cons of the choice options (Chernev,

2003b). This factor has been operationalized in prior research in

two ways: in terms of the level of product-specific expertise and

in terms of the availability of an articulated ideal point.

Research has argued that the impact of assortment size on

choice overload is a function of consumers' expertise and, in

particular, their knowledge about the attributes and attribute

levels describing the available alternatives. In this context, it has

been shown that for consumers who are unfamiliar with the

product category, choices from larger assortments are more likely

to lead to choice deferral and weaker preferences for the selected

alternative than choices from smaller assortments. In contrast, for

expert consumers, the impact of assortment size is reversed,

leading to greater likelihood of choice deferral and weaker

preferences for the chosen alternative in the context of smaller

rather than larger assortments (Chernev, 2003b; Mogilner et al.,

2008; Morrin, Broniarczyk, and Inman, 2012).

Choice overload is also likely to be a function of the degree

to which consumers have an articulated ideal point. Whereas

product expertise implies knowledge of the product category

and the product at hand, the availability of an articulated ideal

point implies that consumers have well-defined preferences

within a given category. Thus, the availability of an ideal point

goes beyond product expertise and implies a hierarchical

attribute structure and already articulated attribute trade-offs

(Carpenter & Nakamoto, 1989; Dhar, 1997; Wansink, Kent, &

Hoch, 1998). Because the articulation of attribute trade-offs is

essential for choice, the availability of an ideal attribute

combination effectively increases the compatibility of consum-

er preference structures with the decision task, thus reducing

the structural complexity of the decision. Therefore, prefer-

ences based on an articulated ideal point will be more effective

in reducing the structural complexity of the decision than will

338 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

preferences that do not involve an ideal point. Because large

assortments are generally associated with more complex

decisions, the differential impact of ideal point articulation is

likely to be more pronounced for larger than for smaller

assortments (Chernev, 2003b). Accordingly, consumers with an

available ideal point are more likely to have stronger

preferences for and make a purchase from larger assortments

than consumers without an available ideal point, who are more

likely to have stronger preferences for and make a purchase

from smaller assortments.

Building on the above research, we expect that individuals'

preference uncertainty can influence the impact of assortment size

on choice overload. Specifically, we predict that a greater degree

of choice overload will result from higher levels of preference

uncertainty, defined in terms of the level of product-specific

expertise and the availability of an articulated ideal point.

Decision goal

The decision goal reflects the degree to which individuals aim

to minimize the cognitive effort involved in making a choice

among the options contained in the available assortments. The

importance of the decision goal as a factor contributing to choice

overload is underscored by the fact that overload is, at least in

part, driven by consumers' inability to make a tradeoff among the

available options—an effect that is more pronounced for choices

from larger than from smaller assortments (Chernev, 2003b).

Whereas most assortment research has focused on scenarios in

which consumers' goals involve making a choice from the

available assortments, this is not always the case, and on many

occasions consumer decisions do not involve such choices. In this

research, we identify three factors that could lead to scenarios in

which consumers might not aim to minimize cognitive effort:

decision intent (buying vs. browsing), decision focus (choosing

an assortment vs. choosing a particular option), and level of

construal (high vs. low).

Consumers' decision intent reflects whether they approach

the decision task with the explicit goal of making a choice (i.e.,

buying) or merely to consider the available alternatives without

the explicit goal of selecting one or more of the available

options (i.e., browsing). Indeed, on some occasions, consumers

approach the available assortments with the cognitive goal of

learning more about the available options and/or their own

preferences. In the same vein, consumers might approach the

available assortments with the affective goal of deriving

pleasure from the evaluation process itself (Kahn & Ratner,

2005; Kahn & Wansink, 2004). In this context, one can argue

that decisions associated with a browsing goal are less likely to

lead to cognitive overload compared with decisions that involve

the goal of making a choice.

Consistent with this line of reasoning, prior research has

shown that consumers who evaluate assortment options with a

goal of browsing rather than making a choice are less likely to

face cognitive overload when presented with more extensive

assortments (Chernev & Hamilton, 2009; Choi & Fishbach,

2011; Hamilton & Chernev, 2010). In the same vein, research

has shown that experienced consumers who seek variety are

more satisfied with larger assortments than with smaller ones

(Oppewal & Koelemeijer, 2005). Indeed, because the search

itself is utility enhancing for these consumers, there are fewer

(if any) negative consequences associated with larger assort-

ments and, hence, less likelihood of choice overload.

Even when consumers approach assortments with the intent of

making a choice, their decision might not necessarily involve

choosing among the available options but might involve choosing

among the assortments themselves (Arentze, Oppewal, &

Timmermans, 2005; Kahn & Lehmann, 1991). Because choices

among assortments do not necessarily involve evaluating the

individual options in these assortments and trading off their pros

and cons, larger assortments are not necessarily associated with

greater cognitive effort and hence choice overload. Therefore, one

can argue that the impact of assortment size on choice overload is

likely to be a function of consumers' decision focus and,

specifically, whether they consider choosing among assortments

or choosing an item from a given assortment.

In this context, focusing consumerattentiononchoosingan

assortment tends to enhance the benefits of variety while

de-emphasizing the cognitive costs associated with making a

choice, thus strengthening the preference for larger assortments. In

contrast, focusing individuals' attention on choosing a specific item

from a given assortment tends to make the difficulty of choosing

from larger assortments more prominent, consequently increasing

the preference for smaller assortments (Chernev, 2006;seealso

Sood, Rottenstreich, & Brenner, 2004; Huffman & Kahn, 1998).

Therefore, varying the decision focus can systematically vary the

choice overload experienced by consumers, such that larger

assortments are more likely to lead to choice overload when the

goal involves choosing an option from an assortment rather than

choosing among assortments.

Research examining choice among assortments has further

shown that the impact of assortment size on choice overload is a

function of the level of construal. Specifically, the construal-level

theory predicts that the way individuals conceptualize the

decision process—as a high-level, abstract, and distant process

or a low-level, concrete, and proximate process—can signifi-

cantly influence decision outcomes (Trope & Liberman, 2010).

In this context, it has been shown that varying psychological

distance can influence consumers' awareness of the difficulty of

the decision task (choice feasibility), which in turn is likely to

influence their preference for larger versus smaller assortments

(Goodman & Malkoc, 2012).

The above reasoning suggests that the impact of assortment

size on choice overload is likely to be a function of individuals'

decision goals. Specifically, we expect that choice overload is

likely to be more pronounced in cases when consumers aim to

minimize the effort involved in making a choice from a given

assortment.

Method

The data

The data utilized in the meta-analysis were collected through a

literature review of articles published in refereed psychology and

339A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

marketing journals. We also examined the recently published

review papers in the domain of choice overload (Broniarczyk,

2008; Chernev, 2012; Scheibehenne, Todd, & Greifeneder, 2010)

to ensure comprehensive coverage of the published data. Given

our focus on identifying factors that moderate the impact of

assortment size on choice overload, we sought out studies that (1)

included assortment size as an independent variable and (2)

included a dependent variable that has been linked by prior

research to choice overload (e.g., choice satisfaction, decision

confidence, decision regret, choice deferral, switching likelihood,

assortment choice, and option selection).

Our units of analysis are the individual conditions of the studies

reported in the relevant articles—an approach common for

meta-analyses (Becker, 2001; Rosenthal, 1995; Sánchez-Meca,

Marín-Martínez, & Chacón-Moscoso, 2003). For example, a

study featuringa 2 (moderator: high vs. low) × 2 (assortment size:

high vs. low) design yields two meta-analytic observations—one

for each level of the moderating factor. In the same vein, an article

reporting three studies, each featuring a 2 × 2 design, yields six

observations (3 studies × 2 conditions).

Overall, the meta-analysis includes 99 observations derived

from 53 studies published in 21 articles across 7202

participants. Consistent with our theorizing, the observations

were assigned to one of the four experimental factors: choice

set complexity, decision task difficulty, preference uncertainty,

and decision goal. Specifically, the choice set complexity factor

involved 18 observations across 1336 respondents, the decision

task difficulty factor involved 17 observations across 1409

respondents, the preference uncertainty factor involved 22

observations across 1092 respondents, and the decision goal

factor involved 24 observations across 1778 respondents. In

addition, there were 18 observations across 1587 respondents

that captured only the main effect of assortment size and, thus,

did not involve any moderating factors.

Each observation was also coded to reflect the correspond-

ing dependent variable—satisfaction/confidence, decision re-

gret, choice deferral, switching likelihood, assortment choice,

and option selection. Because we were interested in estimating

the differential impact of choice overload using different

metrics, in studies with multiple dependent variables we

recorded each dependent variable as a separate observation.

All coding directly followed from the information reported by

the authors of the individual studies.

A summary of the experimental data is given in Table 1,which

outlines the underlying observations, studies, and articles and

indicates how each observation corresponds to our conceptual

model, presented in Fig. 1. A detailed overview of the individual

observations and the corresponding papers and an outline of the

rationale for their selection are offered in Appendix A.

The model

We use a theory-based meta-analytic approach (Becker, 2001)

to explore the drivers of choice overload. This approach facilitates

both quantifying and testing the effects of theoretically derived

moderators. The key benefit of this approach is that rather than

focusing on the presence or absence of a main effect of assortment

size on choice overload, it enables us to validate our conceptual

model by evaluating the relative impact of the four conceptually

derived antecedents of choice overload. This is important because

the published studies document choice overload in some

conditions but not in others. In this context, we aim to test the

validity of our conceptual model with respect to its ability to

account for the conceptual moderators that have been advanced

andtestedinpriorresearch.

As informed by our conceptual model, we identify four

factors—choice set complexity, decision task difficulty,

preference uncertainty, and decision goal—that are likely to

influence the impact of assortment size on choice overload. We

further operationalize choice overload by measuring choice

deferral, switching likelihood, option selection, assortment

choice, decision regret, and satisfaction/confidence (given the

low number of studies measuring satisfaction and confidence,

we combined these two factors into a single variable).

To integrate the individual studies into a format suitable for

meta-analysis, we transformed the differences between the small

and large assortments within individual studies into effect size

measures represented by Cohen's d—an approach commonly

used in meta-analysis (Cohen, 1988). In this context, a positive

d-value is associated with a negative impact of larger assortments

(choice overload), whereas a negative d-value is associated with a

positive impact of larger assortments. For the decision outcomes

measured on a continuous scale (e.g., regret, satisfaction, and

confidence), Cohen's dis calculated as the difference between the

two means divided by the combined standard deviation

(Cohen,

1988). For the decision outcomes measured on a binary scale

(choice deferral, switching likelihood, option selection), Cohen's

dis calculated using the arcsine transformation

(Lipsey &

Wilson, 2001; Scheibehenne et al., 2010). Using the log-odds

ratio, which has been shown to perform well for binary outcomes

(Sánchez-Meca et al., 2003) yielded nearly identical results.

To illustrate, consider the data by Iyengar and Lepper (2000;

Study 3), which involves both continuous and binary measures.

Specifically, the data measured on a continuous scale show that

participants in the small assortment condition were more

satisfied than participants in the large assortment condition

(M= 6.28, SD = 0.54 vs. M= 5.46, SD = 0.82), and the data

measured on a binary scale show that participants given the

smaller assortment condition were more likely to make a purchase

compared to those given the larger assortment (16 out of 33,

versus 4 out of 34). Accordingly, we calculate d=1.18 for

satisfaction and d= .84 for the choice likelihood measure,

whereby the positive dindicates the presence of choice overload.

To analyze the data, we use a meta-analytic model that

regresses effect sizes (Cohen's deffects) of the explanatory

variables on the observed measures of choice overload. Given

the nested nature of the data (99 observations derived from 53

studies published in 21 separate articles), we use a three-level

d¼meansmall−meanlarge

s, where s ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

nsmall−1

ðÞ

smallþnlarge −1

ðÞ

s2

large

nsmallþnlarge −2

d¼2arcsine ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Psmall

p−2arcsine ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Plarge

p, where P

small

and P

large

are the

proportions of participants who make a selection from each assortment.

d¼6:28−5:46

:696 ¼1:18, where s¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

33−1ðÞ:542þ34−1ðÞ:822

33þ34−2

q¼:696 and d¼2

arcsine ﬃﬃﬃﬃ

q−2arcsine ﬃﬃﬃﬃ

q¼1:54−:70 ¼:84.

340 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

Table 1

Overview of the studies included in the Meta-Analysis.

Study ID Product

category

Sample

size

Moderators of choice overload

(independent variables)

Indicators of choice

overload

Assortment size Effect size

Author/article Conceptual factor Factor

level

Condition (dependent variables) Small Large Cohen's dS

Weight

Mogilner et al. (2008),“TheMereCategorization

Effect: How the Presence of Categories

Increases Choosers' Perceptions of

Assortment Variety and Outcome

Satisfaction.”

3 44 Coffee 39 Preference uncertainty High Ideal point not available Satisfaction 5 50 1.21 0.12 0.00

Chernev and Hamilton (2009),“Assortment Size

and Option Attractiveness in Consumer Choice

Among Retailers.”

1 45 Sandwich shops 30 Choice set complexity Low Less attractive options Assortment choice 9 38 −1.65 0.07 0.00

1 46 Sandwich shops 30 Choice set complexity High More attractive options Assortment choice 9 38 −0.40 0.07 0.00

2 47 Assorted products 126 Choice set complexity Low Less attractive options Assortment choice 8 24 −0.78 0.02 0.02

2 48 Assorted products 118 Choice set complexity High More attractive options Assortment choice 8 24 0.44 0.02 0.01

3 49 Jam 69 Choice set complexity Low Less attractive options Assortment choice 9 54 −1.58 0.03 0.01

3 50 Jam 72 Choice set complexity High More attractive options Assortment choice 9 54 0.00 0.03 0.01

4 51 Assorted products 19 Choice set complexity Low Less attractive options Assortment choice 9 54 −1.58 0.03 0.00

4 52 Assorted products 23 Choice set complexity High More attractive options Assortment choice 9 54 0.02 0.02 0.00

5 53 Chocolate stores 47 Choice set complexity Low Less attractive options Assortment choice 9 54 −1.02 0.04 0.01

5 54 Chocolate stores 47 Choice set complexity High More attractive options Assortment choice 9 54 −0.21 0.04 0.01

Fasolo et al. (2009),“The Effect of Choice

Complexity on the Time Spent Choosing.”

1 55 Mobile phones 64 No moderators –– Satisfaction 6 24 0.06 0.06 0.01

2 56 Mobile phones 60 No moderators –– Satisfaction 6 24 0.52 0.07 0.01

2 57 Mobile phones 60 No moderators –– Satisfaction 6 24 0.11 0.07 0.01

Haynes (2009),“Testing the Boundaries of the

Choice Overload Phenomenon: The Effect of

Number of Options and Time Pressure on

Decision Difficulty and Satisfaction.”

1 58 Various prizes 36 Decision task difficulty Low Low time pressure Satisfaction 3 10 0.28 0.11 0.00

1 59 Various prizes 33 Decision task difficulty High High time pressure Satisfaction 3 10 0.64 0.13 0.00

1 60 Various prizes 36 Decision task difficulty Low Low time pressure Regret 3 10 0.17 0.11 0.00

1 61 Various prizes 33 Decision task difficulty High High time pressure Regret 3 10 −0.04 0.12 0.00

Scheibehenne et al. (2009),“What Moderates the

Too-Much-Choice Effect.”

1 62 Restaurant coupons 80 No moderators –– Choice deferral 5 30 −0.11 0.05 0.01

2a 63 Charity 60 Preference uncertainty Low High expertise Choice deferral 2 30 −0.32 0.07 0.01

2a 64 Charity 57 Preference uncertainty High Low expertise Choice deferral 5 40 −0.13 0.07 0.01

2b 65 Charity 75 No moderators –– Choice deferral 5 79 −0.18 0.05 0.01

2c 66 Charity 80 Decision task difficulty High Justification required Choice deferral 5 80 0.37 0.05 0.01

3a 67 Music 80 Preference uncertainty Low High expertise Satisfaction 6 30 −0.25 0.03 0.01

3b 68 Music 87 Preference uncertainty Low High expertise Satisfaction 6 30 −0.05 0.02 0.01

Sela et al. (2009),“Variety, Vice, and Virtue: How

Assortment Size Influences Option Choice.”

1a 69 Ice cream 121 No moderators –– Option selection 2 10 0.38 0.03 0.02

1b 70 Food 75 No moderators –– Option selection 4 12 0.43 0.05 0.01

2 71 Printers, MP3 players 50 No moderators –– Option selection 4 12 0.89 0.08 0.01

3 72 Printers, MP3 players 156 No moderators –– Option selection 4 12 0.35 0.03 0.02

342 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

4 73 Laptops 86 Choice set complexity Low Dominant (justifiable)

option available

Option selection 4 12 −0.47 0.05 0.01

4 74 Laptops 85 Choice set complexity High Dominant (justifiable)

option not available

Option selection 4 12 0.45 0.05 0.01

5 75 Printers, MP3 players 84 Choice set complexity Low Dominant (justifiable)

option available

Option selection 4 12 −0.46 0.05 0.01

5 76 Printers, MP3 players 84 Choice set complexity High Dominant (justifiable)

option not available

Option selection 4 12 0.48 0.05 0.01

Diehl and Poynor (2010),“Great Expectations?!

Assortment Size, Expectations and Satisfaction.”

2 77 Camcorder 165 No moderators –– Satisfaction 8 32 0.33 0.02 0.02

3 78 Computer wallpaper 65 No moderators –– Satisfaction 60 300 0.54 0.06 0.01

Greifeneder et al. (2010),“Less May Be More When

Choosing Is Difficult: Choice Complexity and Too

Much Choice.”

1 79 Pens 40 Decision task difficulty Low Low number of attributes Satisfaction 6 30 −0.12 0.10 0.01

1 80 Pens 40 Decision task difficulty High High number of attributes Satisfaction 6 30 0.81 0.11 0.01

2 81 MP3 players 52 Decision task difficulty Low Low number of attributes Satisfaction 6 30 −0.28 0.08 0.01

2 82 MP3 players 52 Decision task difficulty High High number of

attributes

Satisfaction 6 30 0.54 0.08 0.01

Inbar et al. (2011),“Decision Speed and Choice

Regret: When Haste Feels Like Waste.”

1 83 DVDs 27 No moderators –– Regret 5 30 1.22 0.18 0.00

2 84 DVDs 78 Decision task difficulty Low Low time pressure Regret 15 45 −0.31 0.05 0.01

2 85 DVDs 78 Decision task difficulty High High time pressure Regret 15 45 0.68 0.05 0.01

Goodman and Malkoc (2012),“Choosing Here and

Now Versus There and Later: The Moderating Role

of Psychological Distance on Assortment Size

Preferences.”

1A 86 Restaurant coupons 63 Decision (effort-minimizing) goal Low Low level of construal Assortment choice 7 14 −0.55 0.03 0.01

1A 87 Restaurant coupons 67 Decision (effort-minimizing) goal High High level of construal Assortment choice 7 14 0.15 0.03 0.01

1B 88 Ice cream 78 Decision (effort-minimizing) goal Low Low level of construal Assortment choice 6 18 −1.53 0.03 0.01

1B 89 Ice cream 82 Decision (effort-minimizing) goal High High level of construal Assortment choice 6 18 −0.80 0.02 0.01

2 90 Vacation packages 51 Decision (effort-minimizing) goal Low Low level of construal Assortment choice 6 18 −1.14 0.04 0.01

2 91 Vacation packages 47 Decision (effort-minimizing) goal High High level of construal Assortment choice 6 18 −0.30 0.04 0.01

3 92 Blenders 42 Decision (effort-minimizing) goal Low Low level of construal Assortment choice 6 18 −0.99 0.05 0.01

3 93 Blenders 45 Decision (effort-minimizing) goal High High level of construal Assortment choice 6 18 0.04 0.04 0.01

4 94 Blenders 56 Decision (effort-minimizing) goal Low Low level of construal Assortment choice 4 24 −1.49 0.04 0.01

4 95 Blenders 41 Decision (effort-minimizing) goal High High level of construal Assortment choice 4 24 −0.54 0.05 0.01

Morrin et al. (2012),“Plan Format and Participation

in 401(k) Plans: The Moderating Role of Investor

Knowledge.”

1 96 Mutual funds 77 Preference uncertainty Low High expertise Choice deferral 9 21 −0.56 0.05 0.01

1 97 Mutual funds 73 Preference uncertainty High Low expertise Choice deferral 9 21 0.33 0.05 0.01

Townsend and Kahn (2014),“The “Visual Preference

Heuristic”: The Influence of Visual versus Verbal

Depiction on Assortment Processing, Perceived

Variety, and Choice Overload.”

5 98 Crackers 129 Decision task difficulty Low Verbal presentation format Choice deferral 8 27 −0.32 0.03 0.02

5 99 Crackers 107 Decision task difficulty High Visual presentation format Choice deferral 8 27 0.37 0.04 0.01

Note.—Study indicates the number of the study in the corresponding paper. ID is the identification number assigned to each observation for the purposes of the meta-analysis. Conceptual factors are the four factors—

decision task difficulty, choice set complexity, preference uncertainty, and decision (effort-minimizing) goal—depicted in Fig. 1,operationalized by a variety of study-specific variables. Assortment size refers to the

number of options used to represent small and large assortments in each study. Cohen's d is the measure of the effect size, S

is the corresponding variance, and weight is the relative weight of each observation as a

function of the sample size. Studies using multiple dependent variables are reported in the table multiple times; this replication is controlled for in the statistical analysis.

343A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

meta-regression model, in which the first two levels capture the

specific effect sizes across studies and the third level captures the

underlying articles. This approach enables us to account for the

fact that some of the individual observations are part of the same

experiments, a number of which belong to the same articles.

Controlling for interdependencies across experiments is impor-

tant because effect sizes reported in the same article are likely to

be more similar compared to effect sizes in experiments reported

in articles following different research paradigms. Thus, the

three-level meta-regression approach allows us to reduce the risk

of spurious findings that may result from treating effect sizes that

are reported in the same article as independent sources of

information. The resulting model is as follows:

=β

0NM

+β

SetComplexity + β

TaskDifficulty + β

PreferenceUncertainty + β

DecisionGoal + β

ChoiceDeferral +

SwitchingLikelihood + β

OptionSelection + β

Assortment

Choice + β

Satisfaction + β

DecisionRegret +υ

+η

Here, the intercept β

0NM

represents the overall effect of

assortment size in the absence of moderators. The next four factors

(β

–β

) capture the four hypothesized moderators of choice

overload (choice set complexity, decision task difficulty, prefer-

ence uncertainty, and decision goal). The following six factors

(β

–β

) reflect the six outcome measures used in different

experiments (satisfaction/confidence, choice deferral, switching

likelihood, assortment choice, regret, and option selection).

The model further includes two random sources of variance

that capture effects not explained by the four moderators. Thus,

the between-study variability η

estimates random deviations

from the intercept and moderator effects, whereby its size reflects

the degree to which the intercept and moderators can capture any

observed differences among the effect sizes. We also estimate the

variance of υ

, which capturesthe fact that that some experiments

stem from the same article and reflects the degree to which effect

sizes in these experiments differ from one another.

Results

We present our findings starting with describing the fit of

our model with the existing empirical data and its ability to

account for the variance across existing studies. We then

proceed to test the effects of the specific drivers of choice

overload identified by our conceptual model. Finally, we test

for the effects of additional factors that might influence choice

overload, as well as for the presence of publication bias in the

data.

Model fit

The data show that the moderators and outcome measures

capture 68% of the residual variances estimated at the study and

article levels (Huedo-Medina, Sanchez-Meca, Marin-Martinez,

& Botella, 2006) compared to the intercept-only model. The

fit difference between the model including the conceptual

moderators and outcome measures outlined in the previous

section and the intercept-only model is highly significant

(χ

(10) = 118.1, p b.001), indicating that our conceptual

framework receives support by accounting for a substantial

variation in the effect sizes. We further estimate the residual

variances as 0.05 at the study level and as 0.13 at the article

level. Overall, these data suggest that the observed heteroge-

neity in the reported effect sizes of the studies are reasonably well

accounted for by the moderators as derived by our theoretical

framework.

To further test the validity of our conceptual model, we

investigated whether the effects of the moderating factors

interacted with any of the outcome measures. The interaction

test showed that the effects of each moderator across the

outcome measures did not vary significantly. The correspond-

ing test statistics for the interaction effect of choice set

complexity, decision task difficulty, preference uncertainty,

and decision goal are χ

(2) = 3.6, p = .16, χ

(3) = .6, p=

.90, χ

(3) = .5, p= .92, and χ

(2) = 4.3, p= .12, respec-

tively. These results for each of the four factors are consistent

with our conceptual model presented in Fig. 1, showing that

different moderators yield similar effects across different

outcome measures. Another test of the validity of our grouping

of empirical factors into conceptual factors is the degree to

which each of the conceptual factors captures the variability

among the effect sizes across its different operationalizations.

In this context, the fact that different operationalizations lead

to similar effect sizes would be reflected in a nonsignificant

interaction between the individual operationalizations of each

conceptual factor and the impact of assortment size on choice

overload. The test statistics for the grouping hypothesis of the

four moderators are: χ

(3) = 3.7, p= .29 for choice set

complexity; χ

(3) = 4.4, p= .23 for decision task difficulty;

(1) = 1.9, p= .17 for preference uncertainty; and χ

(2) =

6.8, p= .03 for decision goal. The absence of significant

interactions for three of the four factors is consistent with the

notion that these different operationalizations lead to similar effect

sizes. For example, in the case of choice set complexity, this

means that its different operationalizations—the availability of a

dominant option, option attractiveness, option alignability, and

option complementarity—have similar explanatory power. For

the decision goal factor, we find that decision focus has greater

impact on the overload effect compared to the other two factors

(decision intent and level of construal). We also tested for

multicollinearity by examining the correlation matrix of the

independent variables and found no evidence for it; all of the

correlations were small and most were nonsignificant.

Effects of the specific moderators of choice overload

To examine the effect of the four factors identified as the

potential drivers of choice overload, we first examine a model

that does not include these moderators and then compare this

Fig. 2.Forest plot of the overall effect of assortment size on choice overload in a model without conceptual moderators.Note.—The Forest plot depicts effect sizes and their respective conﬁdence intervals for

individual observations. The individual observations are arranged by effect size, starting with the strongest positive effect at the top and ending with the strongest negative effect on the bottom. The data show

that the effect sizes vary from −4.9 to 1.6, suggesting signiﬁcant variability in the experimental results. The large negative effect sizes in the four observations at the bottom of the chart are consistent with

individuals' natural preference for large assortments in the context of the assortment choice task.

344 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

345A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

model with one that includes the four moderators implied by

our conceptual model—an approach consistent with prior

meta-analytic research (Scheibehenne et al., 2010). Specifical-

ly, the simple model involves the intercept β

and the two

random sources of variance described earlier—υ

and η

—as

shown below.

ESia ¼β0þυ0a þηia

The overall effect of assortment size for each observation

can be visually represented using a Forest plot that depicts

effect sizes (Cohen's d) and their respective confidence

intervals for each specific observation (Fig. 2). The data show

that in the absence of the conceptual moderators, the mean

effect of assortment size on choice overload is nonsignificant

(t(20) = −.10; p= .48)—a finding consistent with the findings

reported by prior research (Scheibehenne et al., 2010). At the

same time, there was a significant unexplained variability of the

effect size across individual articles and studies not accounted

for by the simple model, suggesting that the simple model is not

representative of the data (χ

(78) = 665.5, pb.001 at the

study level and χ

(20) = 130.3, pb.001 at the article level).

Given the significant amount of unexplained variance by the

simple model, we then proceed with testing a more compre-

hensive model that includes the four conceptual moderators

stemming from our theorizing. The results of the statistical

analysis are summarized in Table 2, which reports that all four

factors—choice set complexity, decision task difficulty,

preference uncertainty, and decision goal—identified by our

conceptual model as potential antecedents of choice overload

are statistically significant (p b.001) and have relatively strong

effects on choice overload. This finding lends support to our

conceptual model depicted in Fig. 1, showing that each of these

four factors has a significant impact on choice overload,

whereby higher levels of decision task difficulty, greater choice

set complexity, higher preference uncertainty, and a more

prominent effort-minimizing goal lead to a greater choice

overload.

With respect to the dependent variables used to capture

choice overload, we find that the effect estimates of four of the

six outcome measures reflecting overload effects above and

beyond the moderator effects are nonsignificant. Thus, because

satisfaction/confidence, regret, choice deferral, and switching

likelihood do not produce an effect that is not already captured

by the four moderating variables, we conclude that they are

equally powerful in capturing the impact of assortment size on

choice overload. The two outcome measures producing a

significant effect are assortment choice and option selection,

and we conjecture that their ability to capture unique aspects of

the impact of assortment size on choice overload is a function

of the specifics of the decision task associated with these

outcome measures. Specifically, the assortment-choice task

typically involves a very strong preference for large assort-

ments, which is likely to skew individuals' choices in favor of

the larger assortment (Chernev, 2006). The option-selection

task, on the other hand, employed assortments that were on

average significantly smaller than those used with the other

dependent variables (the mean assortment sizes in the

option-choice task were 4 and 12, significantly smaller than

the corresponding assortment sizes of 8 and 34 in the other

decision tasks). The fact that the remaining four dependent

variables—satisfaction/confidence, regret, choice deferral, and

switching likelihood—do not produce a significant effect

above and beyond the four conceptual moderators, is important

because it suggests that these measures could be used

interchangeably to capture the impact of assortment size on

choice overload.

The effect sizes and the respective confidence intervals for

the specific observations can be visually represented using

Forest plots, as shown in Fig. 3. Given our focus on identifying

the differential impact of the four moderating factors, we

organized the Forest plot by effect size within each moderator.

The data represented by the Forest plots show that varying each

of the four factors influences the direction of choice overload,

such that the effect is positive in conditions where these factors

are present and negative in conditions where these factors are

less pronounced or absent.

The mean effect of assortment size on choice overload

Following the examination of the individual effects of the

four moderators included in our conceptual model, we examine

the mean overload effect in studies that do not include

moderators (identified in Table 1). The data show that for

studies that do not include moderators, the intercept term

Table 2

A summary of the meta-analysis results.

Effect Estimate SE T P

Intercept (no moderators) .41 .14 3.0 .01

Moderators of choice overload

Choice set complexity .55 .07 7.9 b.001

Decision task difficulty .37 .08 4.7 b.001

Preference uncertainty .32 .07 4.5 b.001

Decision goal .56 .06 8.8 b.001

Measures of choice overload

Satisfaction .12 .12 1.1 .29

Regret −.17 .25 −.69 .49

Choice deferral −.08 .15 −.50 .62

Switching likelihood .23 .22 1.1 .28

Assortment choice −.72 .16 −4.4 .001

Option selection −.45 .21 −2.10 .04

Note.—Individual cells represent the estimates of the meta-regression model.

The data further show that for the model that includes the conceptual

moderators the intercept term reflecting the main effect of assortment size is

significant (t(67) = 3.0; p= .004), indicating that studies that do not include

moderators tend to exhibit a systematic overload effect. All four moderators

identified by our conceptual model (Fig. 1) are significant and the

corresponding coefficients indicate comparable effects, with the decision goal

and choice set complexity having the strongest effects and preference

uncertainty the weakest effect on choice overload. Thus, higher levels of

decision task difficulty, greater choice set complexity, higher preference

uncertainty, and a more prominent, effort-minimizing goal tend to produce

greater choice overload. The last six rows indicate the differential impact of the

individual response variables. The data show that four of the six factors—

satisfaction/confidence, regret, choice deferral, and switching likelihood—do

not have a significant impact, suggesting that they capture the impact of

assortment size on choice overload equally well.

346 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

reflecting the main effect of assortment size is significant

(t(67) = 3.0; p= .004), indicating that studies that do not

include moderators tend to exhibit a systematic overload effect

(Fig. 3E).

In addition to examining the mean overload effect in studies

without moderators, we also examine the mean effect of choice

overload across studies both with and without moderators. In

this analysis, we exclude studies in which the decision goal

involved choice among assortments. Indeed, whereas studies

examining assortment choice are relevant for capturing the

effect of the conceptual moderators (e.g., decision goal) on the

impact of assortment size on choice overload, decisions that

involve choices among assortments (rather than choices of an

option from a given assortment) tend to display an inherent

strong preference for larger assortments (Chernev, 2006).

Accordingly, to examine the mean effect of choice overload

we excluded observations that involved assortment choice

(29–40, 45–54, and 86–95). The data show that across all studies

Fig. 3a. Forest plot showing how choice set complexity influences the impact of assortment size on choice overload. Note.—The Forest plots 3A–3E depict effect

sizes and their respective confidence intervals for individual observations for each of the four moderating factors identified in Fig. 1, as well as for observations from

studies without moderators. Here, the individual observations are grouped into one of the four moderating factors, with the individual observations within each factor,

as well as the observations without moderators, arranged by effect size. Varying each factor influenced the direction of the effect of assortment size, such that choice

overload is present in conditions where these factors are present/more pronounced and is in the opposite direction when these factors are less pronounced/absent.

Within each of the plots depicting moderators (3A–3D), the level of each factor (high vs. low) is correlated with the direction of the effect, such that high levels are

associated with choice overload whereas lower levels are associated with the opposite (more is better) effect. The mean values of the effect size for each of the two

levels of a particular moderator are indicated with a diamond, whereby the size of the diamond reflects the uncertainty associated with estimating these effects. The

overall effect (across the two conditions of each moderator) of assortment size in these plots is given by the difference between the dashed vertical lines. Studies

without moderators (plot 3E) show the tendency of larger assortments to produce choice overload. Studies examining choice among assortments (Chernev &

Hamilton, 2009 in plot 3A and Chernev, 2006 in plot 3D) display a less pronounced ability to engender choice overload—a finding that likely stems from the lower

decision costs associated with choice among assortments.

347A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

that examine choices from an already given assortment,

assortment size has a significant impact on choice overload,

such that in comparison to smaller assortments larger assortments

are more likely to produce choice overload (t(39) = .17,

pb.001). We also tested the mean effect of assortment size

on choice overload by isolating the effects of studies with and

without moderators (i.e., using a model with separate

intercepts for the studies with and without moderators rather

than using a single intercept). This test yielded a

non-significant effect for studies with moderators (t(39) =

.04, p = .41) and a significant effect for studies without

moderators (t(39) = .41, p b.001)—a finding suggesting that

aggregating the observations across different levels of a given

moderating factor is likely to attenuate (or eliminate) the

choice overload effect (Chernev, Böckenholt, & Goodman,

2010).

The finding that assortment size can have a significant

main effect on choice overload is counter to the data

reported by prior meta-analytic research, which finds this

effect to be nonsignificant (Scheibehenne et al., 2010). This

discrepancy suggests that the mean effect of assortment size

on choice overload is likely to be contingent on the subset

of studies included in the meta-analysis and/or the

conceptual model tested (Chernev et al., 2010). We address

this discrepancy in more detail in the discussion section of

this research.

The effect of assortment size across individual experiments

Most studies investigating the impact of assortment size on

choice overload have treated assortment size as a binary

variable, differentiating between small and large assortments.

However, the operationalization of small versus large assort-

ments has varied across these studies, enabling us to examine

whether choice overload is indeed more pronounced in studies

that utilize relatively larger assortments. Analysis of the choice

set sizes of small and large assortments across the individual

studies shows that the most common comparison involved 6

options, representing a small assortment, and 24 options,

representing a large assortment (6 and 24 are the median

values; the corresponding means are M

Small

= 5.4, SD = 18.6

and M

Large

= 27.8, SD = 3.7). This assortment size selection

Fig. 3b. Forest plot showing how decision task difficulty influences the impact of assortment size on choice overload. (continued).

348 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

*The values reported in this section as t-statistics are b coefficients; the complete statistics are b=.17, t(39)= 4.5; b=.04, t(39)= .9; b=.41, t(39)=5.3

was likely influenced by the methodology used for the

choice set sizes in the pioneering article reporting empirical

evidence of choice overload (Iyengar & Lepper, 2000). The

study-specific assortment sizes are given in Table 1 and the

distribution of the sizes of small and large assortments across

individual experiments is shown in Fig. 4.

To examine the effect of assortment size across studies, we

added assortment size to our model as a variable. For the

purposes of this analysis we excluded one observation (78) as

an outlier because it used choice sets that were disproportion-

ately larger than those used in the other studies. The data show

that the effect of the larger assortment size across studies was

not significant (b=−.005, t(37) = −1.5, p=.13).We also

examined whether the effect of assortment size was likely to

decline as the number of options in the choice set increased

(Chernev & Hamilton, 2009; Ratner et al., 1999)bytestingfor

a quadratic trend. However, the data showed no evidence that

this effect diminishes as the size of the larger assortment

increases (b= .002, t(36) = 1.4, p=.17).

The lack of a significant monotonically increasing relation-

ship between assortment size and choice overload is consistent

with the findings reported by prior research (Scheibehenne et

al., 2010). This finding is further consistent with the argument

made by Chernev et al. (2010), who reason that although

conceptually such a relationship should exist, the failure of a

meta-analysis to document it is not surprising, since there are a

number of intervening factors (e.g., decision-maker's expertise,

the composition and the organization of the assortment, and the

nature of the decision task) that ultimately determine whether

increasing assortment size will result in choice overload.

Because the experiments included in the meta-analysis vary on

a number of dimensions (e.g., option complexity, organization of

the choice set, and product familiarity) that likely contribute to

choice overload, the absence of a monotonic (linear or curvilinear)

effect of assortment size is not inconsistent with the choice

overload hypothesis. This line of reasoning is consistent with the

distribution of small and large assortment sizes across individual

experiments illustrated in Fig. 4. Thus, some studies demonstrate

choice overload in choice sets with relatively few options

(Chernev, 2005; Gourville & Soman, 2005; Haynes, 2009)

whereas other studies show that overload is unlikely to be

prominent even in relatively large choice sets (Chernev, 2006;

Fig. 3c. Forest plot showing how preference uncertainty influences the impact of assortment size on choice overload. (continued).

349A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

Diehl & Poynor, 2010). The overlap between the two distribu-

tions suggests that some assortment sizes have been used across

studies as both large and small assortments—a finding consistent

with the existence of significant moderators that influence the

impact of assortment size on choice overload.

Publication bias

An important consideration when conducting meta-analytical

studies is to control for the tendency of authors and journals to

overemphasize significant findings and underreport nonsignificant

or inconclusive findings. This publication bias is likely to lead to

asymmetric relationships between the effect sizes and their

standard errors in the absence of moderators (Sterne & Egger,

2001). Such asymmetric relationships can be diagnosed with a

funnel plot, which offers a visual tool for identifying publication

bias in studies (Rothstein, Sutton, & Borenstein, 2005; Sterne,

Becker, and Egger, 2005).

In the absence of moderators, the funnel plot typically displays

effect sizes and their standard errors. However, when the tested

model includes moderators, effect sizes are heterogeneous. Thus,

it is more informative to examine the relationship between

residual effect sizes and their standard errors after the effect of the

moderators has been accounted for rather than between the effect

sizes and their standard errors as they appear in the model

that includes moderators. In addition to being directly compara-

ble, the residual effect sizes provide information about the

meta-regression's ability to account for the observed effect-size

differences (Sutton et al., 2011).

To test for the existence of publication bias in the data, we

examined the dispersion of the residual effects as a function of

the size of the studies' standard errors. In general, the absence

of publication bias is indicated by the fact that residual

estimates with larger standard errors (located at the bottom of

the funnel plot) are more widely dispersed than estimates with

smaller standard errors (located at the top). These dispersions

are expected to be symmetric and scattered around both sides

Fig. 3d. Forest plot showing how decision goal influences the impact of assortment size on choice overload. (continued).

350 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

of zero, with the size of the scatter increasing with less

precision in the estimates. Deviations from a symmetric

scatter of the residual effects and the studies' standard errors

suggest publication bias because one would expect the residual

effects to be randomly distributed in the absence of publication

bias.

The funnel plot of the studies included in the meta-analysis

is presented in Fig. 5. The pattern of dispersion of the residual

effects in the considered studies shows little evidence for

publication bias since the funnel plot appears to be symmetric

and a regression test for funnel plot asymmetry yielded

nonsignificant results. Specifically, we used a weighted

regression model with a multiplicative dispersion term and the

studies' standard errors as predictor. The results show that the

residual effect size estimates cannot be predicted by the

standard errors (z = .34, p= .74). This finding is consistent

with the visual inspection of the funnel plot, suggesting a lack

of systematic relationship between the standard errors of the

effect sizes and their residual estimates. Thus, one can

reasonably expect that the results of the meta-analysis are

unlikely to be systematically influenced by publication bias.

Discussion

Key findings

Despite the plethora of prior studies examining whether and

when large assortments are likely to lead to choice overload, there

have been few attempts to develop an integrative, overarching

model that characterizes the impact of assortment size on choice

overload. In this context, our research contributes to the literature

by identifying the conceptual drivers of the impact of assortment

size on choice overload. To the best of our knowledge, this is the

first attempt to identify the key conceptual drivers of choice

overload, empirically test their validity, and quantify their relative

effects.

Specifically, in this research we identify four key factors that

can reliably predict whether, when, and how assortment size is

Fig. 3e. Forest plot showing the main effect (no moderators) of assortment size on choice overload. (continued).

351A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

likely to influence choice overload: (1) the difficulty of the

decision task, which reflects the structural properties of the

decision task operationalized in terms of time constraints,

decision accountability, number of attributes describing each

option, and the complexity of the presentation format; (2) the

complexity of the choice set, which reflects the value-based

relationships among the choice alternatives, including the

presence of a dominant option, as well as the overall

attractiveness, alignability, and complementarity of the choice

options; (3) consumers' preference uncertainty, which reflects

the degree to which consumers can evaluate the benefits of the

choice options and have an articulated ideal point; and (4)

consumers' decision goal, which reflects the degree to which

individuals aim to minimize the cognitive effort involved in

making a choice among the options contained in the available

assortments. More important, we show that each of these four

factors has a directionally consistent and significant impact on

choice overload, such that higher levels of decision task

difficulty, greater choice set complexity, higher preference

uncertainty, and a more prominent, effort-minimizing goal

facilitate choice overload.

In addition to identifying the key factors that moderate the

impact of assortment size on choice overload, we identify

several common outcomes of choice overload used as

dependent measures in prior research—satisfaction/confidence,

regret, choice deferral, switching likelihood, assortment choice,

and option selection—and examine the ability of these

measures to capture unique aspects of the impact of assortment

size on choice overload. In this context, we find that four of the

six dependent measures—satisfaction/confidence, choice de-

ferral, switching likelihood, and regret—are not significantly

different from one another, suggesting that these four measures

capture the impact of assortment size in a similar way and

hence can be used interchangeably. To the best of our

knowledge, this is the first systematic attempt to compare the

degree to which different measures are able to capture the

impact of assortment size on choice overload. Documenting the

convergence of different operationalizations not only validates

choice overload as a construct but also defines a set of

equivalent operationalizations that could be used interchange-

ably in future research.

Our analysis further documents the presence of a significant

main effect of assortment size on choice overload across studies

that test the main effect of choice overload without explicitly

controlling for moderating effects. Although not central to our

analysis, this finding is notable because it is counter to the

findings reported by prior research advocating the absence of

such an effect (discussed in more detail in the following

section). This discrepancy lends support to the notion that the

main effect of assortment size is vulnerable to a variety of

context effects and therefore is not a reliable measure of choice

overload (Chernev et al., 2010).

Prior meta-analytic research

A prior meta-analytic review found little evidence of choice

overload, concluding that the mean effect of assortment size on

choice overload is nonsignificant (Scheibehenne et al., 2010).

This analysis further questioned the existence of factors that can

systematically lead to choice overload, arguing that no sufficient

conditions could be identified that would lead to a reliable

occurrence of choice overload. The discrepancy between the

findings reported by prior research and the findings of our

meta-analysis raises the question of identifying the key factors

contributing to such disparate findings. We believe that this

discrepancy can be attributed to two key factors: differences in the

underlying data and differences in the model used to analyze the

data. We address these two factors in more detail below.

-1 -.5 0 .5 1

Residual Value

Standard Error

Fig. 5. Funnel plot of publication bias. Note.—The funnel plot with pseudo

95% confidence limits is approximately symmetrical, indicating that the effect

sizes do not appear to exhibit a systematic pattern. The slightly slanted solid line

represents the regression test for funnel-plot asymmetry proposed by Egger et

al. (1997). The symmetric distribution of the effect sizes suggests the absence of

a significant publication bias in the analyzed studies.

2-3

Assortment size

Frequency

Small assortment Large assortment

4-5 6 7-10 11-15 2416-23 25-30 31-50 51-70 70-300

Fig. 4. Distribution of the sizes of small and large assortments across individual

experiments. Note.—Black bars indicate assortment sizes representing smaller

assortments and white bars indicate assortment sizes representing larger

assortments. The overlap between the two distributions suggests that some

assortment sizes have been used as both large and small assortments (in

different experiments)—a finding consistent with the existence of significant

moderators that influence the impact of assortment size on choice overload.

352 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

In their meta-analysis, Scheibehenne et al. (2010) report 63

observations from 50 published and unpublished experiments

(N= 5036) conducted in the fields of psychology and marketing.

The data used in our meta-analysis are different in several respects.

First, our dataset is significantly larger, including 99 observations

from 53 published studies (N= 7202). Second, to ensure

consistent standards of experimentation, we focused only on

peer-reviewed papers published in academic journals. Overall, our

dataset included 78% of the Scheibehenne et al. (2010)

observations reported in published articles.

The model used by Scheibehenne et al. (2010) differed from

our model in several respects. First, these varied in the selection

of potential moderators of the impact of assortment choice: Our

model focused on four key conceptually derived factors,

whereas the model used by Scheibehenne et al. (2010) included

a mix of conceptual and descriptive moderators (i.e., whether

the study controlled for respondents' expertise or prior

preferences, whether the study was published or unpublished,

publication year, whether the study included a hypothetical or a

real choice, whether the study used satisfaction as a dependent

variable, whether the study used consumption quantity as a

dependent variable, the size of the large assortment, and

whether the study was conducted in or outside of the United

States). Second, our model distinguished among a variety of

different outcome measures used by the individual studies,

which enabled testing for their moderator-specific effects.

Finally, we used a three-stage hierarchical model that controls

for the fact that some of the observations are derived from the

same article and, therefore, are not independent. Accordingly,

our model accounts for 68% of the residual variances in the

underlying studies—a substantial improvement over that of the

model reported in Scheibehenne et al. (2010), which explains

only 36% of the variance in the underlying data.

To examine whether the discrepancy between our findings

and those reported by prior research can be attributed to the

differences in the data or the differences in the method, we

reanalyzed the studies used Scheibehenne et al. (2010) with our

conceptual model. The analysis produced results (reported in

the Appendix B) consistent with those reported in Table 2

earlier in this paper. The fact that we validated our model in the

context of the studies used by prior research suggests that the

differences in the findings reported by the two meta-analyses

cannot be attributed only to the differences in the underlying

studies, but that they also stem from differences in conceptu-

alizing the effects of assortment size on choice overload.

Future research

Despite its conceptual contributions, our research is only a

first step toward developing a comprehensive understanding of

how assortment size influences consumer choice and identifying

conditions in which larger assortments produce choice overload.

Indeed, the ability of meta-analytic research to uncover novel

factors and quantify their effect is bound by the underlying

empirical research. Therefore, further research might identify

factors in addition to the four outlined in our study that are likely

to influence choice overload. In this context, our analysis helps

pinpoint the areas that would benefit from further investigation.

One particular area in need of further investigation is the

impact of the decision maker's goals on choice overload.

Indeed, compared to the other moderators of choice overload

that have been investigated in multiple experiments, we were

able to identify only a handful of studies explicitly examining

how consumer goals influence choice overload. Accordingly,

further research might seek to identify whether and how other

goal-related factors such as decision accuracy, effort minimi-

zation, and purchase quantity influence consumer decision

processes (Kahn et al., 2014).

A related overlooked area is how consumers' affective states

influence the impact of assortment size on choice. Specifically,

future research might examine the effect of positive versus

negative emotions on the likelihood that consumers will

experience choice overload. Our meta-analysis is consistent

with the notion that the effect of assortment size on choice

overload is likely to be a function of the affective state of the

decision maker. Indeed, our data show that regret, as an

operationalization of individuals' decision goal, was a partic-

ularly strong driver of choice overload—a finding consistent

with the fundamental role of regret in self-regulation (Gilovich

& Medvec, 1995; Simonson, 1992). In this context, future

research might examine whether other affective states are likely

to amplify or attenuate the impact of assortment size on choice

overload. Furthermore, in addition to studying emotions as

antecedents of choice overload, future research might examine

emotions as a consequence of choice overload, focusing on the

different types of affective outcomes associated with choosing

from large assortments (Inbar et al., 2011).

It is also notable that studies measuring choice among

assortments, while very similar in terms of their ability to capture

the effects of the four moderators, stand out in terms of their main

effect. Thus, the observations obtained from the three papers using

assortment choice as a dependent variable (Chernev, 2006;

Chernev & Hamilton, 2009; Goodman & Malkoc, 2012)indicate

a much weaker choice overload effect, often displaying the

opposite (more is better) effect. This result likely stems from the

fact that when choosing among assortments, consumers express

their expectations of choice overload rather than the actual

overload. This finding suggests that when choosing among

assortments, consumers are likely to underestimate the choice

overload they will experience when making their final choice from

the assortment selected. In this context, investigating consumer

accuracy in anticipating and measuring choice overload is an

important area for further research.

The 56% estimate reported by Scheibehenne et al. (2010) is calculated as

VarianceReducedModel−VarianceFullModel

VarianceFullModel , which is an unconventional measure that we

believe does not accurately report the amount of explained variance and can

lead to outcomes whereby the amount of explained variance exceeds 100%. The

conventional and more meaningful measure VarianceReducedModel −VarianceFullModel

VarianceReducedModel

suggests that the 2010 analysis accounts for only 36% of the variance in the

data.

353A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

Further research might also investigate the relationship

between the individual factors influencing choice overload. Our

analysis examines the impact of choice set complexity, decision

task difficulty, preference uncertainty, and decision goal in

isolation from one another. This is because a very small fraction

of the extant research has explicitly examined the simultaneous

impact of multiple moderating factors on choice overload.

Therefore, future empirical research is needed to examine the

way in which the four factors identified by our research interact

with one another—an approach that involves using more

complex (e.g., three-factor) experimental designs.

The analyses of new empirical data collected to address

these issues will benefit from an overarching framework that

provides a basis for comparing the data reported by the

individual studies. Our model provides such a framework to

guide future research on choice overload. The data compiled by

future research will, in turn, serve as the basis for further

theory-guided analysis to enhance our understanding of the

decision processes that determine when and how assortment

size leads to choice overload.

Appendix A. Overview of the analyzed studies

The meta-analysis is based on 99 observations, 81 of which

capture the four experimental factors: choice set complexity,

decision task difficulty, preference uncertainty, and decision

goal.

Specifically, the impact of choice set complexity is

captured by 18 observations derived from nine studies reported

in four different articles by Chernev (2005);Gourville and

Soman (2005);Chernev and Hamilton (2009), and Sela et al.

(2009). The impact of decision task difficulty is captured in 17

observations from eight studies reported in six articles by

Gourville and Soman (2005);Haynes (2009);Scheibehenne et

al. (2009);Greifeneder et al. (2010);Inbar et al. (2011), and

Townsend and Kahn (2014). The impact of preference

uncertainty is captured in 22 observations from ten studies

reported in five articles by Chernev (2003a, 2003b);Mogilner

et al. (2008);Scheibehenne et al. (2009), and Morrin et al.

(2012). Finally, the impact of the decision goal is captured in

24 observations from eleven studies reported in four articles by

Oppewal and Koelemeijer (2005);Chernev (2006);Lin and Wu

(2006);Goodman and Malkoc (2012).

In addition to studies explicitly measuring the effect of the

four factors identified in our model, there are several studies

examining choice overload that did not include moderating

factors. Some of these studies aim simply to document the

choice overload phenomenon (Diehl & Poynor, 2010; Fasolo,

Carmeci, & Misuraca, 2009; Iyengar & Lepper, 2000; Shah &

Wolford, 2007), whereas others function as control conditions

in articles that contain other moderators (Inbar et al., 2011;

Scheibehenne et al., 2009; Sela et al., 2009). Collectively, these

data are represented in 18 observations from 15 studies reported

in seven articles.

Below, we offer a list of articles included in the meta-analysis,

outlining the studies and observations included from each article.

1. Research by Iyengar and Lepper (2000) examines the

impact of assortment size on choice overload. It consists of

three studies without moderators, each involving two

conditions (study 3 has an additional no choice condition;

for the meta-analysis, we consider only the small and large

assortment conditions). In all studies, the main dependent

variable is choice deferral, but the second and third studies

additionally report satisfaction. Therefore, this paper is

responsible for five total observations (coded as 1–5; one

from study 1 and two each from studies 2 and 3).

2. Research by Chernev (2003a) examines how the existence

of an ideal point moderates the impact of assortment size on

choice overload. In our meta-analysis, we use only the first

study (studies 2, 3, and 4 analyze decision processes, not

choice overload). This 2 × 2 study measures two separate

dependent variables: the percentage of participants choos-

ing from the larger assortment and participants' confidence

with their selection. This article is thus responsible for four

observations in our meta-analysis (coded as 6–9).

3. Research by Chernev (2003b) examines how preference

uncertainty moderates the impact of assortment size on

choice overload. Through four parallel studies, the depen-

dent variable is operationalized as the percentage of subjects

who switch their selection; in addition, the final study

measures respondents' confidence in their decisions. Each

study follows a 2 × 2 design in which one of the factors is

assortment size and the other indicates whether there is an

articulated ideal point. Consequently, these four studies are

recorded as 10 separate observations (coded as 10–19; two

observations apiece for studies 1–3 and four observations

for study 4).

4. Research by Chernev (2005) examines how feature

complementarity moderates the impact of assortment

size on choice overload. The experimental task involves

choice from within an assortment, and the dependent

variable is operationalized as choice deferral—the

percentage of participants who postpone a selection. In

our meta-analysis, we use the second of three studies

(studies 1 and 3 do not use assortment size as a

moderator). The relevant study follows a 2 × 2 design

and is responsible for two observations (coded as

20–21).

5. Research by Gourville and Soman (2005) presents three

diverse studies dealing with the impact of assortment size

on choice overload. Specifically, study 1 examines how

alignability of product attributes moderates the impact of

assortment on choice overload, operationalized as the

percentage of respondents choosing from the larger

assortment. The second study examines how the number

of attributes of the available options influences consumer

preferences for the larger assortment. The third study

examines the impact of accountability. Generalizing to

the conceptual factors, the first study examines the

impact of choice set complexity (operationalized in

Note that some studies/articles test multiple experimental factors and, as a

result, the sum of articles across factors is larger than the total number of

articles.

354 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

terms of alignability), while the latter two consider

decision task difficulty (operationalized in terms of the

number of attributes and decision accountability). Each

study follows a 2 × 2 design (study 1 has five

assortment-size conditions; we combine the two smallest

and the two largest assortments to construct small and

large sets). In total, this paper is responsible for six

observations in our meta-analysis (coded as 22–27; two

observations per study).

6. Research by Oppewal and Koelemeijer (2005) exam-

ines the impact of assortment size on choice overload

when effort minimization is not a decision goal. From

the perspective of the meta-analysis, this paper follows

a 1 × 2 design in which all participants are variety

seeking (the study has eight separate assortment size

conditions; we combine the two smallest and the two

largest assortments to construct small and large sets).

Satisfaction with the assortment is the lone depen-

dent variable, so we record a single observation (coded

as 28).

7. Research by Chernev (2006) examines how decision focus

moderates the impact of assortment size on choice

overload. The experimental task involves choice among

assortments, and the key dependent variable is operation-

alized as the percentage of participants who select each

assortment. The third and fourth studies also measure

choice satisfaction as a secondary dependent variable. The

first three studies use a 2 × 2 design in which one of the

factors is assortment size and the other is decision goal

(choosing an assortment vs. choosing a specific option).

The fourth study uses a 2 × 2 × 2 design (the additional

moderator is the presence of a dominant option); for the

purposes of meta-analysis, we focus only on the control

condition of the third factor, which results in a 2 × 2

design. Overall, this paper is responsible for twelve total

observations (coded as 29–40; two observations each

for studies 1 and 2; four observations each for studies 3

and 4).

8. Research by Lin and Wu (2006) examines how the need for

cognition moderates the impact of assortment size on

choice overload. The dependent variable is regret, which is

operationalized as the willingness to switch to another

option. In a single 2 × 2 study, subjects are split into

conditions by assortment sizeand need for cognition, which

determine whether or not they have an effort-minimization

goal. Even though this design lends itself to two

meta-analytic observations, the data reported in the

low-need-for-cognition condition had a much smaller

variance (more than 12 times smaller than the other

condition's reported variance) and was excluded from the

analysis. Accordingly, this paper accounts for one obser-

vation (high need for cognition) in our meta-analysis

(coded as 41).

9. Research by Shah and Wolford (2007) examines the

impact of assortment size on choice overload without

moderators. This is a single-factor (assortment size) study

that measures the percentage of participants who select

any option (we combined the eight assortment-size

conditions to construct small and large sets). According-

ly, we record a single observation in the meta-analysis

(coded as 42).

10. Research by Mogilner et al. (2008) examines how the

existence of an ideal point moderates the impact of

assortment size on choice overload. Satisfaction with the

chosen alternative operates as the dependent variable. In

the meta-analysis, we utilize a subset of the third study

(the others do not manipulate assortment size), which has

a 2 × 2 setup. One of the factors is assortment size and

the other indicates whether there is an articulated ideal

point. Accordingly, we record two observations from this

paper (coded as 43–44).

11. Research by Chernev and Hamilton (2009) examines

how option attractiveness moderates the impact of

assortment size on choice overload. The experimental

task involves choice among assortments, and the

dependent variable is operationalized as the percentage

of participants who select each assortment. In our

meta-analysis, we use the first five studies (the sixth

study does not directly measure choice overload). All five

studies follow 2 × 2 designs in which one of the factors is

assortment size and the other is the attractiveness of the

available options (study 4 has three assortment size

conditions; for the meta-analysis, we consider only the

smallest and largest sets). Therefore, in our meta-analysis

these five studies are recorded as ten separate observa-

tions (coded as 45–54; two observations per study).

12. Research by Fasolo et al. (2009) examines the impact of

assortment size on choice overload. The experimental

task involves choice from an assortment, and the

dependent variable is operationalized as satisfaction

with the chosen alternative. Study 1 utilizes a

single-factor design and study 2 has a 2 × 2 design,

which is treated as comprising two simple effects.

Consequently, this article is responsible for a total of

three observations (coded as 55–57; one observation for

the first study and two for the second).

13. Research by Haynes (2009) examines how time pressure

moderates the impact of assortment size on choice

overload. In this paper, there are two dependent variables

as participants report their satisfaction and regret with the

chosen alternative. The article involves a single study with

a 2 × 2 design, which is responsible for a total of four

observations (coded as 58–61; two per dependent variable).

14. Research by Scheibehenne et al. (2009) presents three

sets of studies dealing with the impact of assortment size

on choice overload. Whereas the first two sets measure

choice deferral as the dependent variable, the third

records choice satisfaction. The first study follows a

1 × 2 design without any moderators. In the second set of

studies, 2a utilizes expertise to moderate preference

uncertainty, 2b has no moderator, and 2c requires

subjects to justify their selections, thereby altering the

difficulty of the decision task. Study 2a follows a 2 × 2

design, while 2b and 2c have 1 × 2 setups. (Studies 2b

355A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

and 2c have three assortment size conditions; for the

meta-analysis, we consider only the smallest and largest

sets.) Finally, the third set of studies consists of two

equivalent 1 × 2 within-subject designs; all participants

are primed to gain expertise, which results in low

preference uncertainty. Accordingly, this paper accounts

for seven total observations (coded as 62–68; two

observations for study 2a and one observation apiece

for studies 1, 2b, 2c, 3a and 3b).

15. Research by Sela et al. (2009) examines the impact of

product type—utilitarian or hedonic—on choice overload

and selection. Throughout the dependent variable is

measured as the percentage of respondents choosing a

hedonic option (we focus on the assortments with an equal

number of utilitarian and hedonic goods). The paper

consists of six studies. Studies 1a, 1b, 2, and 3 have no

moderators, while 4 and 5 examine how choice set

complexity (operationalized in terms of ease of justification

that creates a dominant product or category) moderates the

impact of assortment on choice overload. Accordingly,

studies 1–3 follow 1 × 2 designs, whereas studies 4 and 5

have 2 × 2 set-ups. In our meta-analysis, these six studies

are recorded as eight separate observations (coded as

69–76; one observation each for studies 1a, 1b, 2 and 3;

two observations each for studies 4 and 5).

16. Research by Diehl and Poynor (2010) examines the impact

of assortment size on choice overload. The experimental

task involves a choice within an assortment, and the

dependent variable is operationalized as choice satisfaction.

In our meta-analysis, we include studies 2 and 3 (study 1

does not directly manipulate assortment size). Both utilize

1 × 2 experimental designs, accounting for a total of two

observations (coded as 77–78; one from each study).

17. Research by Greifeneder et al. (2010) examines how

decision task difficulty moderates the impact of assortment

size on choice overload. In two similar studies, the

dependent variable is recorded as satisfaction with the

chosen alternative. Both studies manipulate the number of

product attributes in a 2 × 2 design, and they are

collectively responsible for four observations (coded as

79–82; two observations per study).

18. Research by Inbar et al. (2011) examines the impact of

assortment size on choice overload. The experimental

task involves choice from an assortment, and the

dependent variable is operationalized as regret with the

chosen alternative. In our meta-analysis, we use the first

two studies (the third study has a different focus). Study 1

follows a 1 × 2 design without any moderating variable;

study 2 follows a 2 × 2 design and examines how time

pressure (the feeling of being rushed) influences the

impact of assortment size on choice overload. Accord-

ingly, this article is responsible for three observations

(coded as 83–85; one for study 1 and two for study 2).

19. Research by Goodman and Malkoc (2012) examines how

psychological distance moderates the impact of assortment

size on choice among assortments. The experimental task

involves choice among assortments, and the dependent

variable is operationalized as the percentage of participants

who select each assortment. In our meta-analysis, we use

the first five studies (the sixth study does not directly

measure choice among assortments). Studies 1A, 1B, 2, and

3 follow a 2 × 2 design, in which one of the factors is

assortment size and the other is the psychological distance.

Study 4 uses a 2 × 2 × 2 design (the additional moderator is

the prominence of the choice tradeoffs); for the purposes of

meta-analysis, we focus only on the control condition of the

third factor, thus resulting in a 2 × 2 design. Consequently,

this paper is responsible for ten observations (coded as

86–95; two for each study).

20. Research by Morrin et al. (2012) examines how expertise

moderates the impact of assortment size on choice. The

experimental task involves choice among assortments,

and the dependent variable is operationalized as the

percent of participants who select an alternative. In the

meta-analysis, we utilize the first study (the other studies

do not manipulate assortment size). This study uses a

2 × 2 × 2 design (the third factor is the existence of a

default option [target fund]); for the purposes of

meta-analysis, we focus only on the control condition of

the third factor, thus resulting in a 2 × 2 design.

Accordingly, this paper is responsible for two observa-

tions in the meta-analysis (coded as 96–97).

21. Research by Townsend and Kahn (2014) examines how

presentation format moderates the impact of assortment

size on choice overload. The experimental task involves

choice among assortments, and the dependent variable is

operationalized as the percentage of participants who

select each assortment. In our meta-analysis, we focus on

study 5 (studies 1 and 2 do not assign experimental

conditions randomly, and studies 3 and 4 do not directly

measure overload). Study 5 has a 2 (presentation format:

verbal vs. visual) × 2 (assortment size: high vs. low)

experimental design. Accordingly, this paper accounts

for a total of two observations (coded as 98–99).

The data used in the meta-analysis were obtained directly

from the published papers with the exception of cases in which

the published version of the paper did not report all of the data

necessary to calculate effect sizes (Chernev, 2003a, 2006;

Mogilner et al., 2008; Oppewal & Koelemeijer, 2005; Lin &

Wu, 2006; Townsend & Kahn, 2014; Goodman & Malkoc,

2012; Morrin et al. (2012);Ketcham et al., 2012). In such cases,

additional data were obtained by contacting the authors. The

additional data for the studies by Greifeneder et al. (2010) and

Scheibehenne et al. (2009) were obtained from the dataset used

by Scheibehenne et al. (2010). We were unable to obtain the

necessary data from three papers (Huberman et al., 2007;

Ketcham et al., 2012; Reutskaja & Hogarth, 2009) and

accordingly did not include these papers in the meta-analysis.

Appendix B. Reanalyzing the data from prior meta-analytic

research

Supplementary data to this article can be found online at

http://dx.doi.org/10.1016/j.jcps.2014.08.002.

356 A. Chernev et al. / Journal of Consumer Psychology 25, 2 (2015) 333–358

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Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development

Article

Jan 2025

Alexander Waworuntu

The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.

The effect of default opt-ins and social proof tags on high-stake decision-making in an e-commerce context

Article

Jan 2025

Previous research has investigated how certain strategies can influence people's decisions in simple, everyday choices, such as selecting a loaf of bread or purchasing a book online. The objective of this study was to further the understanding on choice architecture elements of default opt-ins and social proof tags, which are interface elements that signal the use of a product by other individuals. We analyzed their effect in an e-commerce context, specifically exploring high-stake economic decision-making that is characterized by high economic cost (financial or opportunity cost) and high decision importance. We achieved this through investigating the effect of default opt-ins on test ride bookings for an automobile, as well as the influence of social proof tags on click-through rates and ‘bookings’, which involve a payment of ~5% of the vehicle price made by customers to reserve a place for them on the purchase waitlist. We hypothesized that a default opt-in in the test ride form would have a significant positive influence on the conversion rate. Our findings supported our hypothesis. We further hypothesized that the addition of social proof tags on choices within a set of alternatives would result in a significant increase in the consideration of those choices for purchase. Though the results were inconclusive, our comparative analysis showed the potential importance of both the default options and social proof tags on consumer decision-making, creating an opportunity for further research into the effective use of the combination of defaults and social proof tags in an e-commerce context.

A conceptual and computational framework for modeling the complex, adaptive dynamics of epidemics: The case of the SARS-CoV-2 pandemic in Mexico

Article

Full-text available

May 2025
PLOS ONE

In the quest to ensure adequate preparedness for health emergencies caused by infectious disease pandemics, there is a need for tools that can address the myriad relevant questions related to the spread and trajectory of pandemics. A hybrid intelligence model that combines human and artificial intelligence may provide a viable solution, as it can process data from models that comprehensively integrate contextual and direct factors, effectively mimicking the social processes surrounding transmission while incorporating human interpretation to enhance our understanding of pandemics. Using data from the COVID-19 pandemic, we demonstrate the implementation of this approach with the publically available EpI-PUMA (Epidemiological Intelligence Platform for the Universidad Nacional Autónoma de México (“PUMA”)) project and platforms, where a user may create their own hybrid intelligence Bayesian classifier models for a range of epidemiological indicators of interest. EPI-Puma integrates data from various public sources (including the national registry of SARS-CoV-2 cases, census data, poverty indicators, climate data and data related to atmospheric contaminants), enabling the deployment of models that predict a range of relevant outcomes. The main criteria for the data included was its coverage (at least at the municipality level) and availability (public data). EPI-Puma was able to identify both the differential predictive value of the different sets of factors related to the epidemic path and well as anticipate with a high probability the path of the pandemic (typical areas under the ROC curve for the associated classifiers being 0.8–0.9).

A timeline of cognitive costs in decision-making

Article

May 2025
TRENDS COGN SCI

The Economics of Curated Shopping: Optimal Product Variety and Subscription Pricing

Article

Jan 2025

The Impact of Price Discrepancies Across Channels on Consumers' Choice Deferral: The Role of Lay Belief About Price Discrepancies and Anticipated Regret

Article

May 2025
J Consum Behav

With the rise of e‐commerce, offline retailers face significant challenges from online counterparts. The online low‐price advantage not only leads to the loss of offline customers but also makes offline customers hesitant and indecisive. In this research, we explore how the actual price discrepancies across channels, interacting with consumers' lay beliefs about these discrepancies and their anticipated regret, impact a novel downstream consumer decision: choice deferral. Across six studies involving one field study, we first show consumers generally believe the online price of the same product to be lower than its offline counterpart. We then demonstrate when actual online–offline price discrepancies contradict this prevailing belief, it results in decreased choice deferral in offline contexts, mediated by greater anticipated regret. Additionally, we highlight that the choice deferral effect induced by price discrepancy across channels is diminished among individuals who do not hold the general lay belief regarding the online–offline price discrepancy. Furthermore, individual differences in the valuation of transaction utility moderate this effect, such that the choice deferral likelihood is higher among individuals who place a high value on transaction utility. Our findings elucidate how consumers' beliefs about price discrepancies across channels influence purchasing decisions in an omnichannel market both theoretically and practically.

Competition and causal inference: Economic choice and welfare analysis under potential outcomes

Article

May 2025
Polit Philos Econ

We apply widely accepted arguments regarding the inferential properties of scientific experiments to analyse markets and consumer decision-making. Market competition is depicted as a means of generating counterfactual data regarding product characteristics resembling experiments’ use of treatment and control conditions to facilitate causal inference. Much like scientific experimentation, competition creates counterfactual data that allows consumers to make causal inferences regarding rival products’ treatment effects upon their welfare. Absent the multiplicity of differentiated goods that competitive markets produce, consumers cannot engage in causal inference and demand curves may become inaccurate specifications of marginal social benefit. This application of the potential outcomes framework to microeconomics offers an account of market competition that has implications for understanding welfare problems created by uncompetitive and monopolistic markets, industrial organisation, and the underlying justifications for anti-trust policy.

When multi-labelling backfires: The influence of sustainability FOP labels valence and orientation

Article

May 2025

To inform and encourage responsible consumer choices, the use of sustainability (i.e., health and/or environmental), front-of-package labels on food products is becoming increasingly widespread. While the benefits of a single label are well-documented, less is known about the effects of multi-labelling – when multiple front-of-package labels are displayed on packaging – especially when these labels differ in valence (i.e., positive versus negative) and orientation (i.e., self-oriented versus others-oriented). Through two experimental studies, we examine how consumers respond to multi-labelling in this context. We show that conflicting labels, displaying opposite valences, cause confusion, regardless of their orientation, without affecting purchase intentions. Furthermore, they reduce moral satisfaction when both others-oriented, which decreases purchase intentions. Finally, same-valence labels, regardless of their orientation, do not impact the mediating psychological mechanisms (moral satisfaction and consumer confusion) but instead directly influence purchase intentions, positively when both labels are others-oriented and negatively when the labels differ in orientation. In line with Kahneman’s dual process theory, consumers process multi-labelling differently depending on labels valence and orientation: similar-valence labels seem to prompt automatic processing (System 1), whereas opposite-valence labels appear to require more deliberate cognitive processing (System 2). These findings provide new insights on multi-labelling, offering practical implications for managers and policymakers seeking to optimize labelling strategies for health and environmental sustainability.

Evidence-Informed Wisdom: Making Better Decisions in Education

Book

May 2025

It just would not work for me: perceived preference heterogeneity and consumer response to AI-driven product recommendations

Article

May 2025

Purpose Previous research has not examined the influence of perceived preference heterogeneity, a contextual antecedent, on consumer response to artificial intelligence (AI)-driven recommendations. This paper aims to address this gap by exploring this issue through the theoretical perspective of lay belief, which suggests that AI recommendations, often thought to rely on “collective wisdom,” may overlook individual idiosyncratic preferences. Design/methodology/approach A survey (involving 159 consumers) is conducted to provide general evidence (across product categories) for the impact of perceived preference heterogeneity on consumer acceptance of AI-driven recommendations. Three follow-up experiments with 597 participants provide causal evidence and examine the mediating role of cognitive trust (versus affective trust) as well as the moderating role of recommendation framing and the human likeness of the AI recommender. Findings Perceived preference heterogeneity negatively affects consumer acceptance of AI-driven recommendations, which is caused by decreased cognitive (rather than affective) trust. The effect is eliminated when the unfavorable lay belief is attenuated by a personal-based (versus others-based) recommendation framing and by increasing the AI recommender’s human likeness in cognitive ability or appearance. Research limitations/implications This paper conducts survey and lab experiments, and future research may use field experiments to provide further evidence. Future research may extend the study to categories where consumers are highly uncertain about their own preference and use highly humanlike AI recommenders. Practical implications Companies may highlight the personalization nature of the recommendations rather than the powerful and “collaborative” recommendation process. When consumers perceive high preference heterogeneity, companies would better use personal-based framings and increase the AI recommender’s human likeness in cognitive ability or appearance. Originality/value This research shows that perceived preference heterogeneity is an important antecedent that hinders consumer acceptance of AI-driven recommendations because of consumers’ unfavorable lay belief. This research also suggests that AI (when adopted to offer product recommendations) may not generate positive responses even in the cognitive domain, whereas most research focuses on its lack of affective abilities.

Variety for the sake of variety? Diversification motives in consumer choice

Article

Full-text available

Jan 2005

The “Visual Preference Heuristic”: The Influence of Visual versus Verbal Depiction on Assortment Processing, Perceived Variety, and Choice Overload

Article

Full-text available

Feb 2014

The “visual preference heuristic” suggests that consumers prefer visual to verbal depiction of information in a product assortment. Images produce greater perceptions of variety than text, which is appealing in assortment selection, but can result in choice complexity and overload when choice sets are large and preferences are unknown, suggesting a moderator for Iyengar and Lepper’s results. Eye-tracking results reveal that the natural gestalt processing of individual visual stimuli, as compared to the piecemeal processing of individual textual stimuli, affects the processing of the assortment as a whole with visual (compared to verbal) presentation facilitating a faster, though more haphazard, scanning of the assortment. While the less systematic processing that results from visual presentation feels easier, it is not ideal for larger assortments resulting in higher complexity ratings and choice overload than with text depiction. These findings reveal that, like many heuristics, preference for visual depiction may be overapplied.

Consumer and managerial goals in assortment choice and design

Article

Full-text available

Sep 2014

In many domains, consumers must deal with an increasing number of choices-spanning where, when, what, and how many items to buy; how many and which options to consider; and how best to weigh the pros and cons of these options. This paper considers how consumer and managerial goals and the ensuing tradeoffs affect the optimal design of assortments in order to help enhance our understanding of assortment choice, identify issues that merit particular attention, review some of the recent research in pertinent areas, and suggest directions for future research.

The Adaptive Decision Maker

Book

May 1993

The Adaptive Decision Maker argues that people use a variety of strategies to make judgments and choices. The authors introduce a model that shows how decision makers balance effort and accuracy considerations and predicts which strategy a person will use in a given situation. A series of experiments testing the model are presented, and the authors analyse how the model can lead to improved decisions and opportunities for further research.

An Anchoring and Adjustment Model of Purchase Quantity Decisions

Article

Feb 1998

How do consumers decide how many units to buy? Whereas prior research on individual consumers' purchases has focused primarily on purchase incidence and brand choice, the authors focus on the psychological process behind the purchase quantity decision. The authors propose that a simple anchoring and adjustment model describes how consumers make purchase quantity decisions and suggests how point-of-purchase promotions can increase sales. Two field experiments and two lab studies show that anchor-based promotions-presented as multiple-unit prices, purchase quantity limits, and suggestive selling-can increase purchase quantities. The final study shows that consumers who retrieve internal anchors can counter these anchor-based promotions effectively. Firms might receive net benefits from anchor-based promotions depending on whether increases in unit sales reflect increased category consumption, brand switching, variety switching, store switching, or stockpiling.

The Paradox of Choice

Chapter

Jun 2015

Barry Schwartz

Choice is what enables each person to pursue precisely those objects and activities that best satisfy his or her own preferences within the limits of his or her resources. This chapter argues that choice, and with it freedom, autonomy, and self-determination, can become excessive, and that when that happens, freedom can be experienced as a kind of misery-inducing tyranny. Though one cannot be free without choice, it is arguable that choice-induced paralysis is a sign of diminished rather than enhanced freedom. Though policy initiatives can operate to minimize the negative effects of choice overload, they contain the danger that they will simultaneously undermine the positive effects of freedom of choice. The reason people can say anything and be understood is that they cannot say anything in any way they want. It is linguistic constraint, in the form of these rules, that makes linguistic freedom possible.

Product assortment

Article

Jan 2008

S.M. Broniarczyk

Assortment Variety: Too Much of a Good Thing?

Article

Jan 2013

Purpose - The purpose of this chapter is to examine the connection between actual variety (the number of stock-keeping units (SKUs)) and amount of useable variety that the consumer perceives. The optimal combination for a retailer is to offer an assortment that maximizes the perceived assortment variety while minimizing the perceived inter-item complexity. Both measures are a function of the actual variety offered in an assortment but other factors such as attribute structure of the individual items, assortment organization, and individual differences can alter the way the actual variety is perceived. Design/methodology/approach - The main methodology used in the chapter is a comprehensive, critical literature review of the empirical research on the topic. Findings - We find that while assortments with a large number of SKUs are desirable for attracting consumers to the category, too large assortments can result in consumer frustration and confusion. On the other hand, when assortments are small, the perceived variety or attention to the category may be limited. Value/originality - Our review shows ways a retailer can adapt to these challenges. First, we show that assortments are viewed in stages. In the first stage, high perceptions of variety are beneficial. When assortments are small, increasing perceived variety can be accomplished by increasing the number of subcategories within the assortment, adding in packaging cues, or using other emotional affective descriptors to further define options within the assortment. In the second or choice stage, too much variety can increase perceived complexity. Perceived complexity at this stage can be reduced by simplifying the complexity of the individual items within the assortment by increasing alignability of attributes, using a simplifying external organizational structure for the assortment, or helping consumers learn their preference.

Constructive Consumer Choice Processes

Article

Jan 1998

Consumer choices concerning the selection, consumption, and disposal of products and services can often be difficult and are important to the consumer, to marketers, and to policy makers. As a result, the study of consumer decision processes has been a focal interest in consumer behavior for over 30 years (e.g., Bettman, 1979; Hansen, 1972; Howard & Sheth, 1969; Nicosia, 1966). One can infer from recent trends in the nature and structure of the marketplace that the importance of understanding consumer decision making is likely to continue. Rapid technological change, for instance, has led to multitudes of new products and decreased product lifetimes. In addition, new communications media, such as the World Wide Web, have made enormous amounts of information on options potentially available (Alba et al., 1997). Further, consumers are often asked to make difficult value tradeoffs, such as price versus safety in purchasing an automobile, environmental protection versus convenience in a variety of goods, and quality of life versus longevity in complex health care decisions. How do consumers cope with the decisions they must make, some of which involve difficult tradeoffs and uncertainties? One approach to studying consumer decisions has been to assume a rational decision maker with well-defined preferences that do not depend on particular descriptions of the options or on the specific methods used to elicit those preferences. Each option in a choice set is assumed to have a utility, or subjective value, that depends only on the option.

The Funnel Plot

Chapter

Mar 2006

Choice Overload: A Conceptual Review and Meta-Analysis

Abstract and Figures

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