# Journal of Consumer Research

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Darren Dahl (Editor in Chief)Eileen FischerGita JoharVicki Morwitz

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## Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload

Benjamin Scheibehenne , Rainer Greifeneder , Peter M. Todd
409-425 First published online: 1 October 2010

## Abstract

The choice overload hypothesis states that an increase in the number of options to choose from may lead to adverse consequences such as a decrease in the motivation to choose or the satisfaction with the finally chosen option. A number of studies found strong instances of choice overload in the lab and in the field, but others found no such effects or found that more choices may instead facilitate choice and increase satisfaction. In a meta-analysis of 63 conditions from 50 published and unpublished experiments (N = 5,036), we found a mean effect size of virtually zero but considerable variance between studies. While further analyses indicated several potentially important preconditions for choice overload, no sufficient conditions could be identified. However, some idiosyncratic moderators proposed in single studies may still explain when and why choice overload reliably occurs; we review these studies and identify possible directions for future research.

In today’s market democracies, people face an ever-increasing number of options to choose from across many domains, including careers, places to live, holiday destinations, and a seemingly infinite number of consumer products. While individuals may often be attracted by this variety, it has been suggested that an overabundance of options to choose from may sometimes lead to adverse consequences. These proposed effects of extensive assortments include a decrease in the motivation to choose, to commit to a choice, or to make any choice at all (Iyengar, Huberman, and Jiang 2004; Iyengar and Lepper 2000); a decrease in preference strength and satisfaction with the chosen option (Chernev 2003b; Iyengar and Lepper 2000); and an increase in negative emotions, including disappointment and regret (Schwartz 2000). These phenomena have been selectively referred to as “choice overload” (Diehl and Poynor 2007; Iyengar and Lepper 2000; Mogilner, Rudnick, and Iyengar 2008), “overchoice effect” (Gourville and Soman 2005), “the problem of too much choice” (Fasolo, McClelland, and Todd 2007), “the tyranny of choice” (Schwartz 2000), or “too-much-choice effect” (Scheibehenne, Greifeneder, and Todd 2009); an increasing number of products to choose from is sometimes termed “consumer hyperchoice” (Mick, Broniarczyk, and Haidt 2004). Common to all these accounts is the notion of adverse consequences due to an increase in the number of options to choose from. Following the nomenclature in the literature, we refer to this common ground as the “choice overload hypothesis.”

The choice overload hypothesis has important practical and theoretical implications. From a theoretical perspective, it challenges most choice models in psychology and economics according to which expanding a choice set cannot make decision makers worse off, and it violates the regularity axiom, a cornerstone of classical choice theory (Arrow 1963; Rieskamp, Busemeyer, and Mellers 2006; Savage 1954). From an applied perspective, a reliable decrease in satisfaction or motivation due to having too much choice would require marketers and public policy makers to rethink their practice of providing ever-increasing assortments to choose from because they could possibly boost their success by offering less. Wide proliferations of choice have also been discussed as a possible source for declines in personal well-being in market democracies (Lane 2000).

Given these implications, it is important to further understand the conditions under which adverse effects of choice overload are likely to occur. Therefore, in this article we aim to thoroughly reexamine the choice overload hypothesis on empirical and theoretical grounds. Toward this goal, we present a meta-analysis across all experiments we could find that investigated choice overload or provide data that can be used to assess it. This meta-analysis reveals to what extent choice overload is a reliable phenomenon and how much its occurrence depends on specific moderator variables. But first, we provide a brief summary of past research on choice overload and its underlying theoretical foundations, considering its proposed preconditions, what exactly constitutes “too much” choice, and arguments for and against the hypothesis that too much choice causes adverse consequences.

## Past Research on Choice Overload

The idea of choice overload can be traced back to the French philosopher Jean Buridan (1300–1358), who theorized that an organism faced with the choice of two equally tempting options, such as a donkey between two piles of hay, would delay the choice; this is sometimes referred to as the problem of “Buridan’s ass” (Zupko 2003). In the twentieth century, Miller (1944) reported early experimental evidence that relinquishing an attractive option to obtain another (a situation he referred to as “double approach-avoidance competition”) may lead to procrastination and conflict. The idea was further developed by Lewin (1951) and Festinger (1957), who proposed that choices among attractive but mutually exclusive alternatives lead to more conflict as the options become more similar. In his theory of attractive stimulus overload in affluent industrial societies, Lipowski (1970) extended this idea by proposing that choice conflict further increases with the number of options, which in turn leads to confusion, anxiety, and an inability to choose.

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#### Influence of Moderators

Given the data coding used, the meta-regression showed that “more choice is better” for those experiments that use consumption quantity as a dependent measure, an effect that was driven by the data from Kahn and Wansink (2004). The analysis also confirmed previous findings showing that decision makers with strong prior preferences or expertise benefit from having more options to choose from (Chernev 2003b; Mogilner et al. 2008). This supports the intuitions of those experimenters who took measures to control for prior preferences, for example, by removing familiar options from the choice set or by using exotic products as in the studies by Iyengar and Lepper (2000).

The meta-regression results further show that published articles as compared to unpublished manuscripts are somewhat more likely to report positive effect sizes, indicating a slight publication bias in favor of choice overload results. Finally, there is an effect of the year of publication such that more recent experiments are less likely to find negative consequences of extensive choice sets. This may be indicative of a so-called Prometheus effect, according to which tantalizing counterintuitive findings have an initial advantage for getting published compared to follow-up experiments that often find less strong results (Trikalinos and Ioannidis 2005).

Beyond these results, no other moderators could be established. Effect sizes did not depend on whether the choice task in the experiment was hypothetical or real or whether satisfaction or choice was the dependent variable. Likewise, there were no differences between experiments conducted within or outside the United States, which questions cultural differences as an explanation for when choice overload occurs, at least on this broad level. This is also in line with the results of Scheibehenne et al. (2009), who directly tested and did not find cultural differences in choice overload between Germany and the United States by conducting closely matched experiments in both countries. Further experiments in other countries and with more fine-grained measures of cultural differences could still reveal other patterns in the future.

Furthermore, within the tested range, there is no linear relationship between the effect size and the number of options offered in the large choice set. However, this does not rule out a curvilinear relationship, which we will explore in more detail below.

With all moderators included, the meta-regression accounts for 56% of the effect size variance (τ2 = 0.07), indicating that there is still a moderate degree of variance between studies left unexplained. We proceed to use other meta-analyses to try to explore some of the sources of that remaining variance.

#### Curvilinear Relationship with Assortment Size

Past research suggested that possible consequences of having many options to choose among might follow a curvilinear relationship, such that an initial increase in the assortment size leads to a more-is-better effect but a further increase eventually leads to choice overload (Reutskaja and Hogarth 2009; Shah and Wolford 2007). To test this proposed relationship meta-analytically, we fitted a quadratic function to the relationship between the effect size and the size of the large assortment across all 63 data points (fig. 2). Yet the fit of the function was poor (R2 = 0.02), suggesting that a curvilinear relationship cannot be substantiated on the basis of the data on hand.

Figure 2

Relationship between the effect size and the size of the large set

Note.—The dotted line indicates the best-fitting quadratic regression curve. To improve presentation, jitter was added to the x-coordinate values and the x-axis was truncated at 80, omitting one data point representing a set size of 300 options.

#### Funnel Plot Analysis

As an alternative approach to exploring the heterogeneity (and hence possible systematic differences) across studies, Rosenthal and DiMatteo (2001) recommended looking for naturally occurring groupings in the plotted data that could point to potential moderators. Toward this goal, figure 3 shows the effect size of each data point plotted against its inverse sampling error variance (s−2) in a so-called funnel plot. Under the assumption of homogeneous variance, one would expect the points to scatter such that studies with a smaller error—thus higher on the y-axis—are closer to the grand mean effect size on the x-axis, so that the plot would look like an upside-down funnel or a volcano. Figure 3 indeed shows this pattern, as studies with a smaller sampling error scatter closer around the grand mean of zero, further supporting the notion of a true population effect size around zero. Yet there are also a few studies showing an effect size d ≥ 0.2, indicating choice overload, that appear to cluster slightly apart from the majority of studies with zero or negative effect size. In line with the results of the moderator analysis, most of the studies in this cluster were published as journal articles. It might be that this cluster indicates an artifact due to publication bias that would disappear with more data being collected in the future that would fill the gap. An alternative explanation is that the true distribution of effect sizes might be bimodal. When the data are split at d = 0.2, the variance within both subsets appears to be homogeneous according to the Q-statistic, which gives some preliminary support for the idea of two distinct clusters of studies. This post hoc split should be interpreted with caution, but it could nevertheless be of heuristic use for exploring how the studies in the two sets differ. Most of these differences involve study-specific moderators that cannot be tested meta-analytically but rather call for a descriptive examination, which we turn to next.

Figure 3

Funnel plot of all data

Note.—The dotted and dashed lines indicate confidence intervals under the assumption that the data set is homogeneous and normally distributed around the mean effect size.

## Discussion

The overall mean effect size across 63 conditions from 50 experiments in our meta-analysis was virtually zero. On the basis of the data, no sufficient conditions could be identified that would lead to a reliable occurrence of choice overload. While this suggests that adverse consequences due to having too much choice are not a robust phenomenon, there are also still a number of studies that report effect sizes indicating choice overload. Also, the variance between the effect sizes in the whole data set is higher than what would be expected by a mere random distribution around an effect size of zero. Although most of this extra variance presumably arose from a small number of studies that found very high positive and negative effect sizes, it nevertheless appears instructive to further seek conditions that may propel or hinder choice overload. As a first step in this direction, the meta-regression indicated that some of the variance was due to the fact that using consumption as the dependent variable or having decision makers with well-defined preferences led to a more-is-better effect. Also, there was a slight publication bias such that unpublished and more recent studies were less likely to find an effect, contributing further to the variance. However, theoretically more meaningful moderators such as the magnitude of the assortment size difference did not account for the variance.

These results do not rule out the possibility that the reliable occurrence of choice overload may depend on particular conditions not included in our meta-analysis. A few experiments have explored other such conditions in the past. While those idiosyncratic conditions cannot be tested meta-analytically as indicated earlier, it is still valuable to review them qualitatively to identify and evaluate possible pathways for future research. Because human decision behavior can fruitfully be understood as an interaction between the mind and the environment (Simon 1990; Todd and Gigerenzer 2007), we organize this review by looking at three types of moderators, relating to the structure of the assortment or choice environment, to the goals and strategies of the individual decision makers, and to the interactions between them.

### Assortment Structure

#### Categorization and Option Arrangement

One aspect of the assortment structure that potentially moderates choice overload is the ease with which options can be categorized. For example, Mogilner et al. (2008) found that an increase in the number of options decreased satisfaction only if the options were not prearranged into categories. In line with research on the effect of ordered versus unordered assortments (Diehl 2005; Diehl, Kornish, and Lynch 2003; Huffman and Kahn 1998; Russo 1977), the authors argued that categories make it easier to navigate the choice set and decrease the cognitive burden of making a choice, especially in unfamiliar situations. Thus, the lack of categorization may be another contributing factor to choice overload. Yet it does not appear to be a sufficient condition in itself because most studies included in our meta-analysis that did not find the effect also did not categorize the options.

Besides the way the options are presented, there are additional aspects of the assortment structure that may explain some of the differences between the study results. For example, the similarity between available options and the degree to which a choice among them involves difficult trade-offs have often not been precisely controlled in studies on choice overload, even though they potentially affect choice satisfaction, regret, and motivation (Hoch, Bradlow, and Wansink 1999; Kahn and Lehmann 1991; Simonson 1990; Van Herpen and Pieters 2002; Zhang and Fitzsimons 1999). Especially if options possess complementary or unique features that are not directly comparable, the number of difficult trade-offs is likely to increase with assortment size (Chernev 2005; Gourville and Soman 2005). On the basis of this analysis, one may suspect that ease of comparison (or lack thereof) constitutes an important potential moderator of choice overload.

Reutskaja and Hogarth (2009) elicited reduced choice satisfaction by increasing the complexity of the offered options. In line with Mogilner et al. (2008), they hypothesized that choice overload is due to the increased cognitive effort needed to make a choice. This argument bears similarity to the information overload hypothesis that predicts a negative impact on decision making if the total amount of information concerning the choice assortment grows too large (Jacoby, Speller, and Kohn 1974; Jacoby, Speller, and Kohn Berning 1974). In its original formulation, the amount of information was calculated as the number of options within an assortment multiplied by the number of attributes on which the options are described. From this perspective, choice overload (where only the number of options is large) is a special case of information overload.

While the original conception of information overload was criticized on methodological and empirical grounds (Malhotra 1984; Malhotra, Jain, and Lagakos 1982; Meyer and Johnson 1989), more refined measures of information quantity, for instance, based on the entropy of a choice set, subsequently led to more reliable results (Van Herpen and Pieters 2002): decision makers who are confronted with more information, measured in terms of entropy, have been found to make less informed choices, presumably because cognitive limits prevent them from thoroughly processing the relevant information (Lee and Lee 2004; Lurie 2004). To the degree that people are aware of these limitations, they might feel less comfortable and hence avoid making a choice in such situations, manifesting choice overload.

The entropy-based information measure takes into account the number of options, but it is influenced more strongly by the number of attributes and the distribution and number of levels within each attribute. Past research on choice overload was often not concerned with the exact amount of information presented to decision makers, which might explain some of the diverging results captured in our meta-analysis. In line with this, Greifeneder et al. (2010) found a decrease in satisfaction with an increase in assortment size only when the options to choose from were described on many attributes.

#### Time Pressure

Inbar et al. (2008) found that more options decreased satisfaction with the choice outcome and increased regret only when decision makers felt rushed because of experimentally induced time pressure. To the degree that time pressure kept participants from processing all the information they needed to make a satisfactory choice, they might have suffered from too much information relative to the amount of time they had to consider it. Similar results were reported by Haynes (2009), who found evidence for choice overload only if he constrained the decision makers’ time to make a decision. Time pressure must be considered relative to the amount of information being presented to decision makers, so that this moderator and the previous one are intertwined.

### Decision Strategies

Participants’ choice strategies and motivations have often not been thoroughly assessed or controlled in experiments on choice overload. Here we highlight some aspects of decision strategies that could explain some of the variance we found between experiments in the meta-analysis.

#### Relative versus Absolute Evaluations

The effect of a given assortment structure on a choice crucially depends on the goal and strategy of the individual decision maker. Accordingly, Gao and Simonson (2008) found decision makers to be overloaded by choice when they first selected a specific option from an assortment and then decided whether they wanted to purchase it, but not when they first decided if they wanted to purchase from a given assortment and only then chose a specific option. The authors argued that in the latter case (first purchase, then choose), people are more likely to focus first on the overall attractiveness of an assortment, which tends to increase with size. In contrast, in the former case (first choose, then purchase), individuals might be overloaded with choice because they initially focus on the relative attractiveness of a specific option, which tends to decrease with increasing assortment size because the options become more similar to each other (Fasolo et al. 2009). Thus, to the degree that decision makers are looking for the relative best option within a given set, choice overload might occur.

#### Maximizing

The tendency to search for the relative best available rather than a merely satisfactory option is at the core of the maximizing versus satisficing personality construct, reflecting the degree to which decision makers aim to maximize their outcomes (Schwartz et al. 2002). This construct seems to be a plausible moderator for choice overload, as maximizers tend to desire large choice sets while at the same time finding it more difficult to commit to a choice. Furthermore, maximizers are often less satisfied with their selection compared to satisficers (Dar-Nimrod et al. 2009). However, single studies in our meta-analysis that formally tested maximizing in a context of choice overload could not establish it as a moderator (Gingras 2003; Kleinschmidt 2008; Scheibehenne 2008; Scheibehenne et al. 2009). A more reliable, domain-specific measure of maximizing might change these results in the future.

#### Choice Justification

From their third study, Iyengar and Lepper (2000) advanced the conclusion that choice overload might be driven by an increased feeling of personal responsibility when choosing from extensive choice sets. Putting this reasoning to the test, Scheibehenne et al. (2009) found an effect of too many options when people knew that they would have to justify their choice later on. Presumably justification becomes more difficult when choosing from a large set where the best options are more similar, which apparently led people to avoid making a choice in the first place. Along the same lines, Sela et al. (2009) hypothesized that large assortments make it more difficult to come up with a good reason for any particular choice, which might make it harder for some people to commit to a decision. Thus, even though most studies on choice overload did not explicitly ask for a reason for choices made, it could still be that some decision makers felt the need to justify their decisions anyhow, which could contribute to the differences in results between studies.

#### Simple Decision Heuristics

Irrespective of individual goals and motivations, decision makers commonly cope with excessive choice and information by means of simple choice heuristics (Anderson et al. 1966; Gigerenzer, Todd, and the ABC Research Group 1999; Hendrick, Mills, and Kiesler 1968; Payne, Bettman, and Johnson 1993). Classic examples of such heuristics are the satisficing heuristic that guides individuals to choose the first option that exceeds their aspiration level (Simon 1955), the elimination-by-aspects strategy that quickly screens out unattractive options (Davey, Olson, and Wallenius 1994; Huber and Klein 1991; Tversky 1972), the choice of a default option (Johnson 2008; Johnson and Goldstein 2003), and the consideration-set model that balances search costs and expected outcomes (Hauser and Wernerfelt 1990). There is ample evidence showing that decision makers adaptively apply such heuristic strategies across a wide range of situations (Gigerenzer et al. 1999). In line with this, Jacoby (1984), the original proponent of information overload, concluded that in most real situations decision makers will stop far short of overloading themselves. Consequently, a potential moderator of choice overload is the degree to which decision makers make use of simplifying decision heuristics. This calls for assessing more than just final choice outcomes in studies on choice overload by adding measures of decision processes (see Scheibehenne and Todd [2009b] for a first step in this direction).

### Perception of the Distribution

Empirical evidence suggests that consumers are less likely to prefer large assortments over smaller ones if they assume that the options in both assortments are mostly attractive and of high quality (Chernev 2008). If instead the options are variable but on average of low quality, a large assortment increases the chance that at least one somewhat attractive option will be found. While the options used in the choice experiments in the meta-analysis were certainly not similar, it could still be that participants differed in the degree to which they perceived them as similar or as having high or low mean quality, which could moderate the presence of choice overload. This could be tested in future experiments by manipulating the variance (real or perceived) independent of the assortment size. Similarly, the perception of the assortment size can be very different from the actual number of options (Broniarczyk, Hoyer, and McAlister 1998; Hoch et al. 1999). As it is perception that ultimately guides behavior, this is another aspect that could explain some of the variance.

### Choice and Satisfaction as Dependent Variables

In a related vein, Anderson (2003) pointed out that making no choice is not a homogeneous concept either but rather embraces different phenomena, including procrastination, an explicit preference for the status quo, or a trade-off between the effort to make a choice and its possible benefits. Because the presence of an extensive choice assortment could also indicate the availability of good or even better options in the future, what looks like choice omission might sometimes be an adaptive deferral strategy for the time being (Hutchinson 2005), which current short-term experimental designs do not capture. Little is known about the specific reasons why participants in choice overload experiments sometimes refrain from making a choice, but further exploring these reasons, including via longer-term studies that allow deferral and future choice, is another pathway to a better understanding of when and why choice overload can reliably be expected to occur.

## Final Conclusions

Although strong instances of choice overload have been reported in the past, direct replications and the results of our meta-analysis indicated that adverse effects due to an increase in the number of choice options are not very robust: The overall effect size in the meta-analysis was virtually zero. While the distribution of effect sizes could not be explained solely by chance, presumably much of the variance between studies was due to a few experiments reporting large positive and large negative effect sizes. The meta-analysis further confirmed that “more choice is better” with regard to consumption quantity and if decision makers had well-defined preferences prior to choice. There was also a slight publication bias such that unpublished and more recent experiments were somewhat less likely to support the choice overload hypothesis. Effect sizes did not depend on whether the choice was hypothetical or real or whether satisfaction or choice was the dependent variable. Likewise, there was no evidence for cultural differences. At least within the analyzed set of experiments, there was also no linear or curvilinear relationship between the effect size and the number of options in the large set.

In summary, we could identify a number of potentially important preconditions for choice overload to occur, but on the basis of the data on hand, we could not reliably identify sufficient conditions that explain when and why an increase in assortment size will decrease satisfaction, preference strength, or the motivation to choose. This might account for why some researchers have repeatedly failed to replicate the results of earlier studies that reported such effects.

It is certainly possible, however, that choice overload does reliably occur depending on particular moderator variables, and researchers may profitably continue to search for such moderators. Our review of this literature identified a number of promising directions worth exploring in future research. To understand the effect that assortment size can have on choice, it will be essential to consider the interaction between the broader context of the structure of assortments—beyond the mere number of options available—and the decision processes that people adopt.

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