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The Neglect of Prescreening Information in Two-Stage Decision Tasks

AMITAV CHAKRAVARTI CHRIS JANISZEWSKI GÜLDEN ÜLKÜMEN*

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* Amitav Chakravarti (achakrav@stern.nyu.edu) is Assistant Professor of Marketing, New York University, 44 West Fourth Street, Suite 9-83, New York, NY 10012-1126 (Phone: 212 998 0517; Fax: 212 995 4006). Chris Janiszewski (chris.janiszewski@cba.ufl.edu) is Faricy Professor of Marketing, University of Florida, P.O. Box 117155, Gainesville, FL 32611-7155 (Phone: 352-392-0161 Ext. 1240#; Fax: 352 846 0457). Gülden Ülkümen (mulkumen@stern.nyu.edu) is a doctoral student, New York University, 44 West Fourth Street, Suite 9-175, New York, NY 10012-1126 (Phone: 212-998-0524; Fax: 212 995 4006). This paper benefited from workshop participants at Duke University, HKUST, INSEAD, the National University of Singapore, the University of Chicago, the University of Colorado, and the University of Florida. All three authors contributed equally to this work.

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Abstract

Several studies show that information used to screen alternatives becomes less important

relative to information acquired latter in the search process simply because it was used to screen.

Experiment 1 shows that the tendency to deemphasize prescreening information leads to

systematically different choices for decision makers who screen alternatives, in comparison to

decision makers who do not screen alternatives. Additional studies show that screening

encourages decision makers to place an inordinate amount of emphasis on the postscreening

information (experiment 2). Prescreening information is deemphasized owing to the perceptual

categorization that occurs when people create a consideration set of retained alternatives

(experiments 3 and 4). Together, the results show that a brand’s strength of consideration (i.e.,

how highly an option ranks on screening criteria) may have little influence on the likelihood it is

chosen in a post-screening choice process.

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When consumers encounter a large number of options, they often use a two-stage

decision process (Beach 1993; Beach and Mitchell 1987; Bettman and Park 1980; Billings and

Marcus 1983; Crow, Olshavsky, and Summers 1980; Howard and Sheth 1969; Nedungadi 1990;

Payne 1976; Roberts and Lattin 1991). In stage one, options are screened to create a

consideration set. In stage two, the remaining options are compared to make a final choice. This

decision strategy is popular because it reduces the decision maker’s workload by paring down

the number of options to be examined at the final choice stage. A reduced number of options at

choice allows the decision maker to consider more information about each option, which should

lead to higher quality choices (e.g., Alba et al. 1997; Haubl and Trifts 1999; Lynch and Ariely

2000; Roberts and Nedungadi 1995). Thus, decision tools (e.g., web-based screening tools) that

allow consumers to structure and organize their decision environments have been strongly

endorsed by both the popular press and consumer decision researchers (e.g., Alba et al. 1997;

Diehl, Kornish, and Lynch 2003; Häubl and Trifts 2000; Lynch and Ariely 2000; Widing and

Talarzyk 1993).

In contrast to this prevailing sentiment, there is a small body of research suggesting that

there may be situations where screening choice sets leads to lower decision quality (Diehl et al.

2003; Van Zee, Paluchowski, and Beach 1992; Wright and Barbour 1977). For example, Diehl et

al. (2003) argue that sorting alternatives on one attribute (e.g., quality) encourages people to

make decisions using nonsorting attributes. If the nonsorting attribute is an important variable,

like price, then decision quality should improve, as Diehl et al. show. If the nonsorting attribute

is a less important variable, then decision quality may suffer. Placing more emphasis on the less

important attribute may change the decision outcome relative to a situation in which all

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information about a brand is considered concurrently (i.e., situations in which there is no

screening of alternatives).

In this paper, we show that the act of screening alternatives (e.g., during consideration set

formation) encourages a person to neglect the prescreening information, and emphasize the

postscreening information, in a subsequent choice (experiment 1). We argue that this tendency to

discount the prescreening information may be a consequence of two processes. First, the act of

screening is often a noncompensatory process in which alternatives that pass a cutoff are retained

(i.e., conjunctive rule) or alternatives that fail to meet a cutoff are rejected (i.e., elimination-by-

aspects rule). To the extent the noncompensatory choice encourages a person to view

prescreening information as already noted, the prescreening information may be ignored, even if

it is the most diagnostic information. As a consequence, there should be an increased emphasis

on the post-screening information (experiment 2). Second, the act of screening encourages a

person to create a category of retained alternatives. Categorization encourages a person to view

alternatives within a category as more similar (Goldstone, Lippa, and Shiffrin 2001). As a

consequence, the person may neglect the prescreening information owing to its perceived

homogeneity (experiments 3 and 4).

BACKGROUND

A large body of research has demonstrated the contingent nature of decision making

(Beach and Mitchell 1978; Einhorn and Hogarth 1981; Newell and Simon 1972; Payne 1976,

1982; Svenson 1979). The selection of information processing strategies is shown to be highly

contingent on the characteristics of the task, environment, and person characteristics (for an

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extensive review, see Ford et al. 1989). Among task characteristics, task complexity has been

most widely investigated with respect to its effect on information processing.

Task Complexity

The literature investigating the influence of task complexity on choice processes is

extensive. Studies show that the selection, evaluation, and integration of information are all

affected by task complexity. Of particular interest for this investigation is the complexity created

by having to consider a large number of alternatives. Process tracing studies find that large

choice sets are often reduced using noncompensatory screening strategies (Billings and Marcus

1983; Johnson and Meyer 1984; Olshavsky 1979; Onken, Hastie, and Revelle 1985; Payne 1976;

Payne and Braunstein 1978; Wright and Barbour 1977). For example, Payne (1976) finds that

when consumers encounter a two-alternative choice situation, they employ compensatory

decision strategies. However, in the context of a more complex, multi-alternative decision task,

consumers attempt to reduce the number of alternatives using a conjunctive or elimination by

aspects rule. Similarly, Timmermans (1993) finds that consumers become more likely to form a

consideration set as the number of available alternatives increases from three (21%) to six (31%)

to nine (77%).

Increases in task complexity also increase the variability of decision rules used within the

same decision task. Montgomery and Svenson (1976) suggest that different decision rules may

be applied in succession in the same decision situation. In support of this proposition, there is

general agreement that the proportion of intra-alternative processing increases with each

additional stage of a decision process (Payne, Bettman, and Johnson 1993). Correspondingly,

verbal protocols in Payne (1976, Experiment 1) illustrate that participants often began a task

using simplifying strategies. Once they had reduced the number of alternatives and dimensions

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using less cognitively demanding strategies, the participants switched to a more elaborate,

compensatory process in order to evaluate the remaining alternatives. Subsequent studies

reported similar findings regarding the phased nature of decision making (Bettman and Park

1980; Billings and Marcus 1983; Crow, Olshavsky, and Summers 1980).

These results clearly indicate that when decision makers confront a complex choice task,

they often employ a two-stage choice process. In the first stage, a noncompensatory process is

used to reduce the number of alternatives to a set of acceptable candidates. Subsequently, a

compensatory process is used to select one alternative from among the remaining alternatives

(Beach and Mitchell 1987). The first stage of this process is called screening, and it can be

defined as the process that governs the admission of options to the consideration set (Beach

1993; Nedungadi 1987).

Screening

Historically, the screening stage of a choice process has been viewed as involving a

relatively simple act that merely reduces the amount of information to be considered in the more

complex and elaborate choice stage (e.g., Bettman 1979; Huber and Kline 1991; Johnson and

Payne 1985). Outside of the marketing literature, the emphasis is often on how certain screening

procedures (e.g., screen alternatives by inclusion / exclusion) influence the size of the resulting

consideration set (see Levin et al. 2001 for discussion). Within the marketing literature, research

has been largely limited to the descriptive issues of (1) verifying whether consumers screen

alternatives and create consideration sets (e.g., Roberts and Lattin 1991) and, (2) verifying the

size of these consideration sets (e.g., Hauser and Wernerfelt, 1990). This focus on descriptive

issues has come largely at the expense of process related issues. For example, does the act of

screening systematically affect information processing at the final choice stage? Consequently,

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do people who screen process information and make choices differently from those who make

their decisions without screening?

There are three pieces of evidence that suggest consideration set formation may alter the

manner in which prescreening information is considered at choice. First, Wright and Barbour

(1977) show that people are reluctant to reconsider information that has previously been used to

reduce the size of a choice set. Wright and Barbour (1977) asked respondents to use a

dimensional conjunctive rule to choose one alternative from among 24 alternatives described on

five attributes. Respondents were asked to apply the cutoffs conjunctively during the “screening

phase” and then choose one of the remaining options post-screening. Some respondents were

also provided with a suggested sequence in which the prescreening attributes should be

examined. Unbeknownst to the subjects at the initiation of the screening task, the initial

screening phase resulted in either all options being discarded or the sole remaining option being

out of stock. When respondents were asked to return to the original set of options, they tended to

restrict their analysis to alternatives they had retained in the last iteration of the sequential

screening process. In other words, participants were extremely reluctant to go back to the options

that were rejected in the earlier rounds of the screening process, even when it was made clear

that the criterion for screening (attribute sequence) had been fairly arbitrary. According to

Wright and Barbour (1977) arriving at the end of a decision making phase (i.e., a round of

screening involving a single attribute) “closes” the phase and, consequently, the details of the

information used in that phase become vague once the next phase is initiated.

Second, Diehl et al. (2003) argue screening may also influence the manner in which

consumers use attribute information to make a choice. Diehl et al. (2003) show that in a two

attribute environment, a screening tool that sorts exclusively on quality increases sensitivity to

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price (i.e., a postscreening attribute). The authors maintain that sorting products into an ordered

list results in over-sampling the more attractive options. To the extent these retained options are

more homogeneous on quality, relative to a random draw of retained options, consumers should

become more sensitive to price. The results of this study indicate that decision makers who

screen are willing to put more weight on post-screening information, although it is not clear how

much of this shift in attribute weight is a function of the act of screening and how much is a

function of the increase in the objective homogeneity of the retained alternatives on the

prescreening information.

Third, Van Zee et al. (1992) examined the effect of screening information on the

prechoice evaluations of the surviving options. Participants in Experiment 1 were presented with

information regarding five apartments, described on five different attributes. They were then

asked to find a suitable rental for a friend, based on the criteria that the friend had provided.

Participants were first given cutoffs for three of the five attributes. Participants in the control

group screened the options using the three criteria and then rated each remaining option’s

attractiveness. Participants in the four experimental groups screened alternatives using the three

criteria, then received additional postscreening information (i.e., attribute values on the

remaining two attributes) about their surviving options. These participants rated the

attractiveness of the surviving options prior to making a final choice (i.e., prechoice). The

authors tried to detect the differential impact of the prescreening and postscreening information

on the groups’ prechoice attractiveness ratings by varying the correlation of the prescreening

information relative to the postscreening information across the four experimental groups. The

results indicated that the treatment group’s prechoice ratings reflected the postscreening

information closely, while the prescreening information was ignored. Van Zee et al. (1992) refer

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to the observed phenomenon as the “screening effect,” and they indicated that this effect can lead

to suboptimal choices.

Together, the three studies suggest screening alternatives to create a consideration set can

encourage people to ignore prescreening information and emphasize postscreening information.

Van Zee et al. (1992) provide the closest test of this hypothesis, yet their demonstration does

have some problems that limit our confidence in their conclusions. First, their conclusions relied

on visual inspection of the data or on correlations of the ratings provided by the treatment and

control groups, rather than on deductions from a more formal experimental design. Second, their

prescreening and postscreening information presentation formats differed in that the prescreening

procedure simply displayed the information, whereas the postscreening procedure asked

participants to value each piece of postscreening information using an eight-point attractiveness

scale. Rating the attractiveness of each piece of postscreening information, but not each piece of

prescreening information, could have encouraged participants to place greater emphasis on the

postscreening information. Third, and perhaps most importantly, their stimuli were constructed

such that the retained alternatives often exhibited no variance on the prescreening attributes (e.g.,

prescreening rents of either $250/month or $300/month with a cutoff of $250/month). As Diehl

et al. (2003) show, if the retained alternatives exhibit objective homogeneity on prescreening

attribute values, then the prescreening attributes will be nondiagnostic and noninfluential in

choice.

Screening Effect

Although there are findings in the literature that hint at a screening effect, the only

explanation that supports the screening effect is that offered by Diehl et al. (2003), namely that

the prescreening attributes become nondiagnostic owing to homogeneous values. We expect

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there may be two additional reasons that prescreening information gets deemphasized when

additional information is encountered post-screening. First, the act of screening is commonly a

noncompensatory process. As argued by Wright and Barbour (1977), engaging in a

noncompensatory process encourages a person to treat prescreening information as used. If

information is viewed as used, it may have less influence when people subsequently shift to a

compensatory strategy post-screening.

A second reason for a screening effect is the process of screening may encourage people

to perceive the prescreening information as more homogeneous, hence less diagnostic

postscreening. Investigations into perceptual categorization suggest that alternatives that are

grouped within a category are perceived as more similar as a consequence of the categorization,

whereas alternatives that are grouped in different categories are perceived as less similar as a

consequence of categorization. For example, Goldstone (1994) shows that categorizing simple

figures (e.g., squares) as belonging to the same group decreases one’s ability to discriminate

among pairs of the figures using same/different judgments. Livingston, Andrews, and Harnard

(1998) find that learning to classifying objects (e.g., animal body parts, microorganisms) into

categories increases the perceived similarity of objects. Goldstone et al. (2001) show that

categorizing objects into the same category increases their shared similarity with an unclassified

object, a result that could only occur if the classed objects became perceptually more similar.

Finally, Werker and Tees (1984) show that infants lose their ability to discriminate between

speech sounds as a consequence of learning that the sounds come from the same phonetic

category. These results suggest screening may not only produce increases in the objective

similarity of the retained alternatives, as claimed by Diehl et al. (2003), but also increases in the

subjective similarity of the retained alternatives as well. These increases in the subjective

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similarity of the prescreening information may encourage people to deemphasize the

prescreening information when considered along with postscreening information.

Summary

We hypothesize that screening will reduce the emphasis on prescreening information and

increase the emphasis on postscreening information. We further hypothesize that this screening

effect may occur for either of two reasons. First, the use of a noncompensatory choice strategy at

prescreening discourages a person from reconsidering the prescreening information once

additional postscreening information is encountered. Second, the act of screening encourages the

formation of a category of retained alternatives and categorization increases the perceived

similarity of alternatives within this category. If prescreening information is perceived to be

homogeneous, then there is no reason to reconsider the information postscreening.

In experiment 1, we investigate the screening hypothesis. If our hypothesis is correct, we

should be able to observe this influence in an option set where prescreening and postscreening

attributes are negatively correlated (cf. Widing and Talarczyk 1993). For example, suppose six

brands are described on more differentiating prescreening attributes and less differentiating

postscreening attributes. Brands A, C, and E perform best on the prescreening attributes and are

most likely to be retained in the event a decision maker is asked to screen the options. If the three

brands exhibit a preference pattern of A > C > E on the prescreening attributes, but E > C > A on

the postscreening attributes, then shifts in the relative amount of emphasis placed on

prescreening and postscreening attributes should influence choice shares. If decision makers

choose without screening, and rely primarily on the more differentiating prescreening attributes

to make this choice, they should prefer brand A. If decision makers initially screen, and

screening reduces the emphasis placed on prescreening attributes and increases the emphasis

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placed on postscreening attributes, they should prefer brand E. Thus, the act of screening should

systematically shift preferences from the brand that performs best on prescreening attributes to

the brand that performs best on postscreening attributes.

EXPERIMENT 1

The goal of experiment 1 was to demonstrate that systematic differences in choice shares

can arise when consumers make choices without screening versus with screening. Participants in

the control conditions were encouraged to choose a brand from among six brands described on

six attributes. Participants in the screening condition were encouraged to reduce the size of the

choice set using four prescreening attributes, then consider the complete set of attribute

information when choosing among the remaining brands. It was expected that screening

condition participants would neglect prescreening attributes during a subsequent choice task and

rely on the postscreening attributes to make their choice.

Method

Stimuli. The stimuli were six brands of microwave popcorn described on six attributes

adopted from Zhang and Markman (2001) (see appendix). The first two prescreening attributes

were cost per serving and level of sodium and had identical performance levels across the six

brands (i.e., they were common attributes). The next two prescreening attributes were alignable

attributes. A pretest had shown that crunchiness and calories per serving were the two most

desirable attributes among a large array of potential attributes (see Zhang and Markman 2001).

The brands’ performances on these attributes were graded so that the brands’ preference rank

was likely to be A, C, E, B, D, F. The final two postscreening attributes for any one brand were

nonalignable attributes, hence were unique to each brand (i.e., there were 12 additional

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attributes). These postscreening attributes were assigned to brands so as to negatively correlate

with the top three brands’ performance on the prescreening attributes (i.e., brand preference

based on only the postscreening attributes was expected to be E, C, A, B, D, F).

Design and Procedure. The design included two choice groups and a screening group.

The partitioned choice group and screening group followed a three-part procedure that varied

only in the screening activity (see appendix). At time one, participants were seated at a computer

and asked to rank the desirability of the best level of each prescreening attribute (e.g.,

crunchiness lasts 3.5 hours, calories equal to less than a slice of bread) plus the 12 postscreening

attributes. At time two, participants were told they needed to purchase some microwave popcorn

and that partial descriptions (i.e., the prescreening attributes) for the six available brands were

listed in the middle of the screen. Then participants in the partitioned choice group were told

“After you have gone through these descriptions to your satisfaction, we will provide you

with more information on these six brands on the next screen. Please do not make up

your mind about your final choice yet; simply take a careful look at the information

provided to you so far.

Participants in the screening group were told

After you have gone through these descriptions to your satisfaction, as a first step

towards picking a brand of your choice, please shortlist three brands that you would

consider more seriously for purchase. We will provide you with more information on

these three brands on the next screen. Please do not make up your mind about your final

choice yet; simply select (i.e., shortlist) three brands that you think warrant further

attention by clicking on the appropriate buttons below.”

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At time three, the postscreening attribute information was added to the stimulus array and

participants were asked to make a choice using all of the attribute information. Partitioned choice

group participants chose from among six brands. Screening group participants were also

presented with six brands, but were asked to choose only from among the three brands in their

consideration set (e.g., “Now, from the three brands that you had short listed earlier, please

choose one brand as your final choice by clicking the appropriate button below”). The

postscreening attributes were assigned so that a participant’s third and fourth most desired

attributes (i.e., the two most desired postscreening attributes) were paired with brand E (i.e., the

third most desirable brand on the prescreening attributes), fifth and sixth most desired

postscreening attributes were paired with brand C, and seventh and eighth most desired

postscreening attributes were paired with brand A. The remaining postscreening attributes were

assigned to the remaining brands as illustrated in the appendix. Thus, for each individual the

prescreening attributes were negatively correlated with the postscreening attributes for the target

brands of interest (i.e., A, C, E).

The experimental design also included a free choice group. Free choice group

participants were asked to rate the desirability of the attributes at time one, then were given all of

the information (i.e., all six attributes) about all of the brands and asked to make a choice. The

postscreening attribute information was assigned to the brands in the same manner as in the other

two conditions. Thus, free choice group participants simply skipped part two of the procedure,

where only the prescreening attribute information was presented. The free choice group

participants were meant to experience a more ecologically valid procedure in which consumers

review information and make a choice. We note that the free choice group and partitioned choice

group are conceptually equivalent control groups, but that the free choice group is a less

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internally valid control. The free choice group information presentation procedure (i.e., entire

stimulus array at once) varied from the procedure used in the screening condition (i.e., a time

two presentation of six brands described on four attributes followed by a time three presentation

of six brands described on six attributes). Thus, the free choice group was our ecologically valid

control group and the partitioned choice group was our internally valid control group.

Results

One hundred twenty participants from an undergraduate subject pool participated in the

experiment. It was expected that participants in the choice (i.e., control) groups would prefer

brand A, C, or E and that the majority would prefer brand A owing to its superior performance

on the most desirables attributes. The choice shares in the partitioned choice condition (π̂ A =

.610, π̂ C = .268, π̂ E = .049) and the free choice condition (π̂ A = .590, π̂ C = .231, π̂ E = .103)

were consistent with this expectation (see table 1). Second, it was expected that screening

condition participants would select brands A, C, and E as their consideration set. All of the

participants did so. Finally, if screening encourages participants to deemphasize prescreening

attributes during the choice task, then the choice share should shift from brand A to brand E

since brand E has better postscreening attribute performance than brand A. The choice shares in

the screening condition (π̂ A = .350, π̂ C = .275, π̂ E = .375) were consistent with this prediction.

The choice share of brand A was lower in the screening group ( π̂ A = .350) than in the

partitioned choice group (π̂ A = .610; z = 2.43, p < .05) or the free choice group (π̂ A = .590; z =

2.20, p < .05).1 The choice share of brand E was higher in the screening group (π̂ E = .375) than

in the partitioned choice group ( π̂ E = .049; z = 3.90, p < .05) or the free choice group ( π̂ E =

1 All hypotheses involving choice shares are directional. One-tail z-tests were used for all comparisons of choice shares.

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.103; z = 3.00, p < .05). Choice shares among the partitioned choice and free choice conditions

did not differ for brand A (z = 0.18, p > .05) or brand E (z = 0.91, p > .05).

************************* Place Table 1 about here

*************************

Discussion

Experiment 1 shows that screening options using a subset of attribute information makes

the prescreening attributes less important in a subsequent choice. Participants preferred the brand

that performed best on the prescreening attributes when they did not engage in screening, but

were less likely to prefer this brand if they initially screened using these desirable attributes.

Preference shifted to a brand that performed better on attributes that were introduced post-

screening, even though these postscreening attributes were not as desirable. In general, people

seem to ignore the magnitude of differences between the three surviving brands on the

prescreening attributes.

It is important to recognize that the design and stimuli used in experiment 1 rule out

processes that might masquerade as a screening effect. First, it is not the case that the

prescreening information was unimportant, and hence, ignored by the subjects in the final choice

stage. Subjects rated the prescreening attributes as more attractive than the postscreening

attributes and yet, deemphasized the prescreening information during final choice. Second, it is

not the case that a small amount of brand differentiation on the prescreening attributes was

overwhelmed by a large amount of brand differentiation on the postscreening attributes.

Participants in the choice conditions also saw the postscreening information. These participants

preferred the brand that performed best on the prescreening attributes, suggesting that the

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prescreening attributes had more influence on choice than the postscreening attributes when

screening was not required. Third, the shift in choice shares in the screening condition was not a

function of an increased salience of the postscreening attributes owing to their introduction just

before choice. The partitioned choice group also had postscreening attributes introduced just

prior to choice, yet exhibited a pattern of choice shares that differed from the screening group.

Thus, it appears that the act of screening itself, rather than the partitioned nature of the task,

initiated a decision process that was responsible for the observed shift in choice shares.

Thus far, we have argued that the screening effect occurs because people screen on

attributes that contribute the most utility to the product, and then deemphasize these attributes at

choice, relying on differences in less desirable, less attractive postscreening attributes. If people

are assigning more weight to the postscreening attributes as a consequence of screening, then we

should be able to show that people are sensitive to changes in the relative performance of the

brands on the postscreening attributes. Moreover, people should be most sensitive to changes in

the relative performance of the brands on the postscreening attributes when they engage in

screening.

EXPERIMENT 2

The goal of experiment 2 was to demonstrate that people become more sensitive to

postscreening attribute information as a consequence of screening. Two stimulus sets were used

to test this hypothesis. The first stimulus set had the same structure as the stimulus set used in

experiment 1. Recall the postscreening attributes were assigned so that a participant’s third and

fourth most desired attributes were paired with brand E, fifth and sixth most desired attributes

were paired with brand C, and seventh and eighth most desired attributes were paired with brand

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A. The second stimulus set assigned a more hedonically dispersed set of postscreening attributes

to brands E, C, and A. Postscreening attributes were assigned so that a participant’s third and

fourth most desired attribute were paired with brand E, seventh and eighth most desired attribute

were paired with brand C, and eleventh and twelfth most desired attribute were paired with brand

A. It was expected that participants would be sensitive to the change in postscreening attribute

hedonic dispersion, especially in the screening condition. In other words, the screening effect

should become larger when the desirability of the postscreening attributes assigned to the

consideration set brands becomes more dispersed.

Method

The experiment was a three (task format: partitioned choice / free choice / screening

group) by two (hedonic dispersion of postscreening attributes: low / high) between-subject

design. Except for the changes noted above, the stimuli were same as in experiment 1. The

procedure was identical to the procedure used in experiment 1.

Results

Two hundred thirty-nine participants from an undergraduate subject pool participated in

the experiment. Collapsing across the preference dispersion factor, the results replicate the

results of experiment 1 (see table 2). Participants in the partitioned choice condition (π̂ A = .530,

π̂ C = .217, π̂ E = .181) and the free choice condition ( π̂ A = .577, π̂ C = .211, π̂ E = .141) were

more likely to prefer brand A to C to E. Participants in the screening condition exhibited a shift

in preference from brand A to brand E ( π̂ A = .294, π̂ C = .224, π̂ E = .447). The choice share of

brand A was lower in the screening group ( π̂ A = .294) than in the partitioned choice group ( π̂ A

= .530; z = 3.20, p < .05) or the free choice group ( π̂ A = .577; z = 3.69, p < .05). The choice

share of brand E was higher in the screening group (π̂ E = .447) than in the partitioned choice

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group ( π̂ E = .181; z = 3.88, p < .05) or the free choice group ( π̂ E = .141; z = 4.50, p < .05).

Choice shares among the partitioned choice and free choice conditions did not differ for brand A

(z = 0.59, p > .05) or brand E (z = 0.68, p > .05).

Owing to the lack of difference between the partitioned choice and free choice

conditions, the groups were collapsed for the hedonic dispersion analysis. The first key

prediction of the experiment was that participants would be sensitive to the change in the

hedonic dispersion of the postscreening attributes. This means that there should be a greater shift

in market share in the high hedonic dispersion condition than in the low hedonic dispersion

condition. To test this prediction, we did three calculations using the market shares for brands A

and E. First, we confirmed there was a shift in choice shares in the low hedonic dispersion

condition (�2(2) = 13.03, p < .05). In the choice groups, the share for Brand A was 58.3% and

the share for brand E was 13.1%. In the screening group, the share for Brand A was 36.7% and

the share for brand E was 34.7%. Thus, the shift in market share was 43.2% ([58.3 - 13.1] - [36.7

- 34.7] = 43.2) for the low hedonic dispersion condition. Then, we confirmed there was a shift in

market shares in the high hedonic dispersion condition (�2(2) = 15.78, p < .05). In the choice

groups, the share for Brand A was 51.4% and the share for brand E was 20.0%. In the screening

condition, the share for Brand A was 19.4% and the share for brand E was 58.3%. Thus, the shift

in market share was 70.2% ([51.4 - 20.0] - [19.4 - 58.3] = 70.2) for the high hedonic dispersion

condition. Finally, we confirmed that the shifts in market share in the low and high hedonic

dispersion conditions were different. A log-linear test for the three-way interaction was

significant (G2(7) = 34.96, p < .01). This means the shift in choice shares in the high hedonic

dispersion condition (70.2%) was greater than the shift in choice shares in the low hedonic

dispersion condition (43.2%).

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The second key prediction of the experiment was that hedonic dispersion should exert a

greater influence in the screening conditions. For screening condition participants, brand A was

less preferred in the high hedonic dispersion condition (π̂ A = .194) than in the low hedonic

dispersion condition (π̂ A = .367; z = 1.82, p < .05). For the choice participants, brand A was

equally preferred in the high hedonic dispersion condition (π̂ A = .514) and the low hedonic

dispersion condition (π̂ A = .583; z = 0.86, p > .05). For screening condition participants, brand E

was more preferred in the high hedonic dispersion condition (π̂ A = .583) than in the low hedonic

dispersion condition (π̂ A = .347; z = 2.21, p < .05). For the choice participants, brand E was

equally preferred in the high hedonic dispersion condition (π̂ A = .200) and the low hedonic

dispersion condition (π̂ A = .131; z = 1.14, p > .05). Thus, the hedonic dispersion manipulation

exerted its influence primarily in the screening conditions.

*************************

Place Table 2 about here *************************

Discussion

We have argued that screening prior to choice encourages decision makers to put less

emphasis on the attributes they used to screen, and more emphasis on new attribute information,

at choice. Experiment 2 provides evidence for the shift in the relative importance of prescreening

and postscreening attributes during the choice task by showing that decision makers are sensitive

to the hedonic dispersion of the postscreening attribute information. In addition, the hedonic

dispersion of postscreening attributes exerts its influence primarily in the screening conditions.

This finding provides direct support for the hypothesis that the act of screening encourages a

decision maker to discount prescreening attribute information and increase the emphasis on

22

postscreening attribute information. The findings also suggest that the act of screening does not

simply encourage people to use a noncompensatory choice process post-screening (c.f., Johnson

and Meyer 1984).

STUDY 3

The results of the first two studies suggest that screening alternatives encourages people

to neglect prescreening information in the subsequent choice. Earlier, two processes were

proposed to account for this effect. First, the process of screening may encourage the use of a

noncompensatory process that subsequently discourages the consideration of the prescreening

information at choice, assuming additional postscreening information is encountered. Second,

the process of screening may encourage people to create a category of retained alternatives (i.e.,

the consideration set). Categorization encourages people to perceive within-group alternatives as

more similar, hence put less emphasis on this information once postscreening information is

encountered. In study 3, we tried to differentiate between these two potential sources of the

screening effect.

The challenge of study 3 was to develop a manipulation that allowed participants to

screen the alternatives using a noncompensatory process, but to not think of the retained

alternatives as a group. To this end, a screen-by-rejection condition was developed. In the

screen-by-rejection condition, participants were asked to remove the alternatives that they did

not want in the consideration set. Thus, similar to the screening condition, the screen-by-

rejection condition encouraged the use of a noncompensatory screening process, but was

exclusionary instead of inclusionary. We expected that a consideration set created by rejecting

undesirable alternatives should discourage participants from perceiving the remaining

23

alternatives as a group, and hence any screening effect attributable to categorical perception

should be mitigated. This is in keeping with findings from past research that show that choice

sets formed under exclusionary instructions tend to (a) be larger, (b) contain more ambiguous

items, (c) require less effort, and (d) create more loosely held group perceptions (e.g., see Levin

et al. 2001). In contrast, if the screening effect observed in experiments 1 and 2 is a consequence

of a noncompensatory screening process, we should observe a screening effect in both the

screening and screen-by-rejection groups.

Method

The experiment was a four cell (partitioned choice, free choice, screen-by-inclusion,

screen-by-rejection) between-subject design. The stimuli were same as in experiment 1. The

procedure for the partitioned choice, free choice, and screen-by-inclusion groups was identical to

the procedure used in experiment 1. The procedure for the screen-by-rejection group mimicked

the procedure of the other groups for the attribute ranking task performed at time one, and was

then modified as follows.

At time two, the screen-by-rejection group was told,

“After you have gone through these descriptions to your satisfaction, as a first step

towards picking a brand of your choice, please reject three brands that you would not

consider more seriously for purchase. We will provide you with more information on the

three surviving brands on the next screen. Please do not make up your mind about your

final choice yet; simply reject (i.e., throw away) three brands that you think do not

warrant further attention by clicking on the appropriate buttons below.”

The screen-by-rejection group then followed a procedure identical to the screen-by-inclusion

group.

24

Results

Two hundred fifty participants from an undergraduate subject pool participated in the

experiment. Choice shares for the four experimental groups are shown in table 3. The results for

the partitioned choice, free choice, and screen-by-inclusion groups replicated the results of

experiment 1. Participants in the partitioned choice condition (π̂ A = .594, π̂ C = .219, π̂ E = .125)

and the free choice condition (π̂ A = .644, π̂ C = .237, π̂ E = .102) were more likely to prefer

brand A to C or E. Participants in the screen-by-inclusion condition exhibited a shift in

preference from brand A to brand E ( π̂ A = .385, π̂ C = .215, π̂ E = .338). The choice share of

brand A was lower in the screen-by-inclusion group (π̂ A = .385) than in the partitioned choice

group ( π̂ A = .594; z = 2.43, p < .05) or the free choice group ( π̂ A = .644; z = 2.99, p < .05). The

choice share of brand E was higher in the screen-by-inclusion group ( π̂ E = .338) than in the

partitioned choice group (π̂ E = .125; z = 2.97, p < .05) or the free choice group (π̂ E = .102; z =

3.34, p < .05). Choice shares among the partitioned choice and free choice conditions did not

differ for brand A (z = 0.57, p > .05) or brand E (z = 0.40, p > .05).

The critical analysis involved the screen-by-rejection group. Participants in the screen-

by-rejection group exhibited choice shares ( π̂ A = .581, π̂ C = .194, π̂ E = .194) that were similar

to the partitioned choice and free choice groups (i.e., showed a lack of a screening effect). The

choice share of brand A in the screen-by-rejection group ( π̂ A = .581) did not differ from the

partitioned choice group (π̂ A = .594; z = 0.15, p > .05) or the free choice group (π̂ A = .644; z =

0.71, p > .05). The choice share of brand E in the screen-by-rejection group (π̂ E = .194) did not

differ from the partitioned choice group ( π̂ E = .125; z = 1.06, p > .05) or the free choice group

(π̂ E = .102; z = 1.44, p > .05). The choice share of brand A in the screen-by-rejection group ( π̂ A

= .581) was higher than the choice share of brand A in the screen-by-inclusion group ( π̂ A =

25

.385; z = 2.25, p < .05). The choice share of brand E in the screen-by-rejection group ( π̂ E = .194)

was lower than the choice share of brand E in the screen-by-inclusion group (π̂ E = .338; z =

1.86, p < .05).

One additional analysis was performed. It was possible that the failure to find a screening

effect in the screen-by-rejection group was a consequence of fewer members of this group

retaining brand E in their consideration set, as compared to members in the screen-by-inclusion

group. If this were so, the choice share of brand E would be artificially suppressed in the screen-

by-rejection group. The percentage of participants including brand E in their consideration set

was equivalent in the screen-by-rejection (73%) and the screen-by-inclusion (71%) groups (z =

0.02, p > .05).

************************* Place Table 3 about here

*************************

Discussion

Experiment 3 provides evidence that perceptual categorization contributes to the

screening effect. When people screened alternatives by selecting brands to retain in their

consideration set, a screening effect occurred. When people screened alternatives by rejecting

brands from retention in their consideration set, a screening effect did not occur. It appears as if

the active creation of a consideration set, with an emphasis on the prescreening information of

the retained alternatives, encourages people to view the prescreening information as less relevant

in a subsequent choice. Forming consideration sets in a less active manner, with an emphasis on

rejecting alternatives, does not lead to a reduced emphasis on the prescreening information.

Thus, using information to actively categorize reduces its importance when additional

postscreening information is encountered prior to a choice.

26

The results of experiment 3 suggest that actively categorizing retained alternatives into a

group (a consideration set) is a necessary precondition for the screening effect. However, the

results do not provide insight into what aspects of this active categorization process is

responsible for the screening effect. For example, it may be that inclusionary screening

encourages more intensive consideration of the prescreening information that describes the

retained alternatives and exclusionary screening encourages more intensive consideration of the

prescreening information that describes the rejected alternatives. In other words, the neglect of

prescreening information that we observe may simply be a function of the amount of effort put

into its initial consideration, and that the act of forming a category is not critical to obtaining the

screening effect. We refer to this as the greater-consideration-of-information hypothesis.

Alternatively, it may be the case that inclusionary screening leads to the screening effect

precisely because it encourages people to create a category using the prescreening information,

and not simply because it encourages greater consideration of the prescreening information. We

refer to this as the perceptual categorization hypothesis. Experiment 4 addresses these two

explanations.

EXPERIMENT 4

The goal of experiment 4 was to differentiate between the perceptual categorization and

the greater-degree-of-consideration accounts of the screening effect. To investigate this issue, we

used the partitioned choice, free choice, and screening groups from experiment 1 plus two

additional groups – the screen & rate group and the rate & screen group. In the screen & rate

group, participants first went through the screening step, then they rated the brands on the

prescreening information, and finally they chose a brand. In the rate & screen group, participants

27

first rated the brands on the prescreening information, then they went through the screening step,

and finally they chose a brand. These two new groups allowed us to directly test two predictions

that could differentiate the perceptual categorization account from the greater-consideration-of-

information account of the screening effect.

First, recall that the act of categorization tends to increase the perceived similarity of

within-group members (Goldstone, Lippa, and Shiffrin 2001). If screening is encouraging

categorization, then the ratings of the retained brands should be less dispersed after screening (as

in the screen & rate group), as compared to before screening (as in the rate & screen group).

Second, if screening encourages the perception of retained alternatives as a group, and this

grouping is responsible for the screening effect, then anything that negates the perception of this

grouping should remove the screening effect. For example, if screening encourages a person to

view retained alternatives as a group and rating encourages a person to perceive alternatives

individually, then rating followed by screening should create a screening effect, whereas

screening followed by rating should not. In other words, inserting a rating task between the

screening stage and the final choice stage, as in the screen & rate group, should weaken or

switch off the screening effect. In contrast, the greater-consideration-of-information hypothesis

predicts there should be a similar screening effect in a screen & rate group and rate & screen

group because the amount of consideration of the prescreening information on the retained

alternatives should be equivalent in each group.

Method

The experiment was a five cell (partitioned choice, free choice, screening, screen & rate,

rate & screen groups) between-subject design.2 The stimuli were same as in experiment 1. The

2 We also ran a rating group to confirm our assumption that rating encourages people to view alternatives individually and, thus, negates the screening effect. Participants first rated the brands on the prescreening

28

procedure for the partitioned choice, free choice, and screening groups was identical to the

procedure used in experiment 1. The procedure for the screen & rate and rate & screen groups

mimicked the procedure of the other groups with respect to the attribute ranking task performed

at time one, and was then modified as follows. In the screen & rate group, respondents were

asked to screen using a procedure identical to the screening group and then were told,

“You just shortlisted three brands (i.e., {list of brands}). However, before you make your

final choice, we want to understand your evaluation of these three brands relative to the

three brands that you did not shortlist. Please rate each of the six brands using the

following scales. We will provide you with more information on these brands on the next

screen. Please do not make up your mind about your final choice yet; simply rate the

brands by clicking on the appropriate buttons below.”

After rating the six brands on a nine-point scale, the screen & rate group moved to the choice

step and was told, “Now, from the three brands that you had short listed earlier, please choose

one brand as your final choice by clicking the appropriate button below.” This instruction was

identical to the instruction in the screening group.

The rate & screen group followed a similar procedure. The rating task instructions were

“Before you begin choosing a microwave popcorn, first we want to understand your

evaluation of these six brands. Please rate each of the six brands using the following

scales. Please do not make up your mind about your final choice yet; simply rate the

brands by clicking on the appropriate buttons below.”

After rating the brands on a nine-point scale with endpoints “extremely unattractive” and

“extremely attractive,” the rate & screen group was asked to screen the alternatives using a

information and then chose a brand from among their top three rated brands. Rating brand shares ( π̂ A = .673, π̂ C = .231, π̂ E = .096) did not differ from either control group (all p > .25) suggesting there was no screening effect.

29

procedure similar to the screening group. Respondents then received the same choice screen as in

the screening and screen & rate groups.

Results

Two hundred seventy-two participants from an undergraduate subject pool participated in

the experiment. Choice shares for the five experimental groups are shown in table 4. The results

for the partitioned choice, free choice, and screening groups replicated the results of experiment

1. Participants in the partitioned choice condition (π̂ A = .585, π̂ C = .208, π̂ E = .132) and the

free choice condition (π̂ A = .621, π̂ C = .207, π̂ E = .121) were more likely to prefer brand A to

C or E. Participants in the screening condition exhibited a shift in preference from brand A to

brand E ( π̂ A = .415, π̂ C = .170, π̂ E = .340). The choice share of brand A was lower in the

screening group ( π̂ A = .415) than in the partitioned choice group ( π̂ A = .585; z = 1.77, p < .05)

or the free choice group ( π̂ A = .621; z = 2.21, p < .05). The choice share of brand E was higher

in the screening group ( π̂ E = .340) than in the partitioned choice group ( π̂ E = .132; z = 2.60, p <

.05) or the free choice group (π̂ E = .121; z = 2.81, p < .05). Choice shares among the partitioned

choice and free choice conditions did not differ for brand A (z = 0.39, p > .05) or brand E (z =

0.17, p > .05).

The perceptual categorization hypothesis predicts that the act of screening would make

the retained brands seem more similar on the screened attributes, an indicator that people were

categorizing the retained alternatives. To assess whether screening altered perceptions of brand

differentiation, we compared the dispersion of the ratings for brands in the consideration set in

the screen & rate group to the dispersion of the ratings for brands in the consideration set in the

rate & screen group. The variance in ratings was computed for each participant. The mean

variance was lower for the screen & rate group (M = 1.69) than the rate & screen group (M =

30

2.85; F (1, 106) = 6.23, p < .05). The mean rating of the screen & rate group (M = 7.27) and the

rate & screen group (M = 6.96) were equivalent (F (1, 106) = 1.14, p > .05), suggesting the

reduction in variance in the screen & rate group was not a function of participants assigning all

retained brands the highest scale value (i.e., “9” on the 9-point scale).

The perceptual categorization hypothesis also predicts that the rate & screen group should

exhibit a screening effect, whereas the screen & rate group should not. The rate & screen group

(π̂ A = .455, π̂ C = .200, π̂ E = .309) did exhibit a screening effect. The choice share of brand A

was lower in the rate & screen group ( π̂ A = .455) than in the partitioned choice group ( π̂ A =

.585; z = 1.36, p < .10) or free choice group ( π̂ A = .621; z = 1.79, p < .05). The choice share of

brand E was higher in the rate & screen group ( π̂ E = .309) than in the partitioned choice group

(π̂ E = .132; z = 2.28, p < .05) or the free choice group (π̂ E = .121; z = 2.49, p < .05). The screen

& rate group ( π̂ A = .566, π̂ C = .208, π̂ E = .170) did not exhibit a screening effect. The choice

share of brand A was equivalent in the screen & rate group ( π̂ A = .566) and the partitioned

choice group ( π̂ A = .585; z = 0.20, p > .05) or the free choice group ( π̂ A = .621; z = 0.49, p >

.05). The choice share of brand E was equivalent in the screen & rate group (π̂ E = .170) and the

partitioned choice group (π̂ E = .132; z = 0.55, p > .05) or the free choice group (π̂ E = .121; z =

0.73, p > .05).

************************* Place Table 4 about here

*************************

Discussion

There are three critical findings in experiment 4. First, the perceptual categorization

account of the screening effect predicts that people should view retained alternatives as more

similar after screening (i.e., in the screen & rate condition) than before screening (i.e., in the rate

31

& screen condition). The data are consistent with the prediction. Second, the perceptual

categorization account predicts that the rate & screen procedure should result in a screening

effect, whereas the screen & rate procedure should not result in a screening effect. Again, this

prediction is confirmed. When the screening task follows the rating task, people view the

retained alternatives as a group and a screening effect is observed. When the rating task is

inserted in between the screening task and the choice task, people view the retained alternatives

as individual items and a screening effect is not observed. Finally, the data are inconsistent with

the hypothesis that the screening effect is a function of the amount of attention paid to the

prescreening information. There should have been equivalent attention to the prescreening

information in the screen & rate and rate & screen conditions, yet the former group does not

show a screening effect and the later group does show a screening effect.

GENERAL DISCUSSION

Experiment 1 shows that screening options using a subset of attribute information makes

the prescreening attribute information less important in a subsequent choice. As a consequence,

people rely on postscreening information to make their judgment. To the extent prescreening and

postscreening information are negatively correlated, people will make different choices than if

they had not screened at all. Experiment 2 confirms that screening encourages a person to put

more emphasis on the postscreening information. Experiment 3 shows that people must actively

retain alternatives for a screening effect to occur. When people are asked to reject alternatives as

unworthy of further consideration, the screening effect is mitigated. The implication is that

screening depends on the active categorization of retained alternatives into a consideration set.

Experiment 4 provides further evidence that perceptual categorization is necessary for the

32

screening effect. Screening encourages decision makers to perceive screened alternatives as more

similar on prescreening information. In addition, the screening effect is only obtained when a

rating task precedes the screening task and the choice task, but not when a rating task is inserted

in between the screening task and the choice task.

Implications

The results have a number of implications. First, consider the finding that an inclusionary

process of perceptual categorization (i.e., selecting alternatives to include in the consideration

set) leads to a greater degree of perceived similarity among retained alternatives. Increases in the

perceived similarity of retained alternatives can lead to a number of outcomes. First, people

become more price sensitive when brands become less differentiated (Boulding, Lee, and Staelin

1995; Diehl et al. 2003). If prechoice screening leads to less perceived differentiation among

retained alternatives, people may be less willing to pay a premium price for their preferred brand

relative to the second most preferred brand. Second, people become more reluctant to purchase

as the perceived difference in the attractiveness of alternatives becomes smaller (Dhar 1997). If

prechoice screening leads to less perceived differentiation among retained alternatives, then

people may delay making a purchase decision. Third, people are more likely to search for

additional information when current information does not allow for differentiation (Moorthy,

Ratchford, and Talukdar 1997). This implies that the act of forming a consideration set may

encourage perceptions of brand homogeneity and motivate people to acquire additional

information, even though this additional information is less relevant than the initial screening

information. Thus, from a managerial perspective, inclusionary screening encourages a number

of consumer behaviors that may discourage sales and limit the extraction of profits by top

performing brands.

33

A second issue concerns why categorization increases the perceived similarity of the

alternatives in a category. Goldstone et al. (2001) argue there are two sources of the increased

similarity of objects that are categorized together. First, there may be a strategic judgment bias

that is associated with the classification task (i.e., a task demand). For example, if a person learns

one set of objects belongs to category one and a second set of objects belongs to category two,

then it seems logical that items within a category should be rated as more similar. The learning

task encourages the inference that within-group objects should be similar. Second, there may be

a change in the perception of the objects as a consequence of classification. For example, if an

attribute is used to classify objects into two groups, then the act of classification may increase the

importance of the attribute for similarity judgments. If within-group members are homogeneous

on the classification attribute, then increasing the importance of this attribute will increase

similarity judgments.

Our data show that another consequence of the increased importance of attribute

information (i.e., prescreening information) at the time of classification may be the decreased

importance of that same information in a subsequent judgment (i.e., at choice). Categorization

encourages a person to view the within-group alternatives as more similar (experiment 4).

According to Goldstone et al. (2001), this change in the perception of the alternatives depends on

the increased importance of the prescreening information (i.e., the information that allowed

participants to group objects as similar becomes more important). Yet, our results show that the

act of classification also encourages a decision maker to put less weight on the classification

attribute information, and more weight on the subsequently encountered non-classification

attribute information, in a subsequent choice. Thus, the importance of classification information

changes at various stages in the decision process.

34

A third issue concerns the natural process of screening and whether it is exclusionary or

inclusionary. Beach and colleagues have long argued that screening ensures the quality of a

choice by excluding options that are of unacceptable or unknown quality (Beach 1993; Potter

and Beach 1994). In this sense, screening is a conservative process that operates solely through a

rejection mechanism (Ordóñez, Benson, and Beach 1999). In contrast, Levin et al. (2001) argue

that consideration set formation is primarily an inclusionary process. Levin et al. (2001)

comment that inclusionary screening results in a smaller consideration set than exclusionary

screening. If the goal of screening is to simplify a choice, then smaller consideration sets are

desirable. Levin et al. (2001) find people naturally engage in inclusionary screening when

attempting to hire a job candidate, but exclusionary screening when people attempt to fire an

employee. Levin et al. (2001) also find that people use inclusionary screening to add and delete

stocks from a portfolio. Thus, it may not be the case that it is easy to anticipate when

inclusionary screening, and an accompanying screening effect, may occur.

Limitations

The two major limitations relate to issues of robustness and issues of causation. With

respect to robustness, it is hard to estimate the percentage of choices that involve a screening

stage, the percentage of choice processes that involve postscreening information gathering, or the

percentage of multi-part choice processes that use a compensatory process in the final stage. We

do know that there are examples of personal (e.g., buying a home) and civic (e.g., selecting an

Olympic host city) choices that appear to follow a process that is consistent with the procedure

used in our studies. With respect to causality, the screening effect is likely to be multi-

determinant. For example, we know that a screening effect can occur because of the

categorization processes we discuss above or because people engage in a noncompensatory

35

choice process postscreening. We expect there may be other causes of the neglect of

prescreening information. We leave the documentation of these other causes to future research.

36

APPENDIX

STIMULUS SET USED IN EXPERIMENTS 1, 3, and 4 Attribute Brand A Brand C Brand E Brand B Brand D Brand F

#1 Common Low cost per serving

Low cost per serving

Low cost per serving

Low cost per serving

Low cost per serving

Low cost per serving

#2 Common Low level of sodium

Low level of sodium

Low level of sodium

Low level of sodium

Low level of sodium

Low level of sodium

1 Rank #3 Alignable Sample

Crunchiness lasts 3.5 hours

Crunchiness lasts 3 hours

Crunchiness lasts 2.5 hours

Crunchiness lasts 2 hours

Crunchiness lasts 1.5 hours

Crunchiness lasts 1 hour

2 Rank #4 Alignable Sample

Calories equal to less than a slice of bread

Calories equal to a slice of bread

Calories equal to two slices of bread

Calories equal to a pinch of sugar

Calories equal to a spoon of sugar

Calories equal to two spoons of sugar

7 5 3 9 11 13 Rank #5 Nonalignable Sample

Not tough Very crispy and easy to swallow

Few kernels left unpopped

Tastes a bit sweet

Slightly low in corn and grain flavor

Has some citric acid

8 6 4 10 12 14 Rank #6 Nonalignable Sample

With waterproof wrapping

Not likely to burn

Does not stick in teeth

Comes in a colorful wrapping

Requires a microwave bowl

Medium size kernels

NOTE. – There are 12 nonalignable attributes in the stimulus set. The nonalignable attributes were assigned to brands based on an individual’s ranking of attribute desirability. The experimental stimulus matrix had columns arranged A, B, C, D, E, F.

EXPERIMENTAL CONDITIONS AND PROCEDURES

Condition Time 1 Time 2 Time 3 Partitioned Choice

Rank Desirability of Attribute Levels

Review 6 brands @ 4 attributes/brand See 6 brands @ 6 attributes/brand Choose 1

Free Choice Rank Desirability of Attribute Levels

See 6 brands @ 6 attributes/brand Choose 1

Screening Rank Desirability of Attribute Levels

Review 6 brands @ 4 attributes/brand Select 3 for further consideration

See 6 brands @ 6 attributes/brand Choose 1 of 3 selected

Screen & Rate

Rank Desirability of Attribute Levels

Review 6 brands @ 4 attributes/brand Select 3 for further consideration

Rate all 6 brands

See 6 brands @ 6 attributes/brand Choose 1 of 3 selected

Rate & Screen

Rank Desirability of Attribute Levels

Review 6 brands @ 4 attributes/brand Rate all 6 brands

Select 3 for further consideration

See 6 brands @ 6 attributes/brand Choose 1 of 3 selected

37

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TABLE 1

CHOICE SHARES IN EXPERIMENT 1

Choice Condition N Brand A Brand C Brand E Brand B Brand D Brand F Partitioned Choice 41 61.0% 26.8% 4.9% 7.3% 0.0% 0.0% (n=25) (n=11) (n=2) (n=3) (n=0) (n=0) Free Choice 39 59.0% 23.1% 10.3% 7.7% 0.0% 0.0% (n=23) (n=9) (n=4) (n=3) (n=0) (n=0) Screening 40 35.0% 27.5% 37.5% 0.0% 0.0% 0.0% (n=14) (n=11) (n=15) (n=0) (n=0) (n=0)

44

TABLE 2

CHOICE SHARES IN EXPERIMENT 2

Choice Condition N Brand A Brand C Brand E Brand B Brand D Brand F

All Data Partitioned Choice 83 53.0% 21.7% 18.1% 6.0% 1.2% 0.0% (n=44) (n=18) (n=15) (n=5) (n=1) (n=0) Free Choice 71 57.7% 21.1% 14.1% 5.6% 1.4% 0.0% (n=41) (n=15) (n=10) (n=4) (n=1) (n=0) Screening 85 29.4% 22.4% 44.7% 2.4% 1.2% 0.0% (n=25) (n=19) (n=38) (n=2) (n=1) (n=0)

Low Hedonic Dispersion Partitioned Choice 84 58.3% 19.0% 13.1% 7.1% 2.4% 0.0% & Free Choice (n=49) (n=16) (n=11) (n=6) (n=2) (n=0) Screening 49 36.7% 24.5% 34.7% 4.1% 0.0% 0.0% (n=18) (n=12) (n=17) (n=2) (n=0) (n=0)

High Hedonic Dispersion Partitioned Choice 70 51.4% 24.3% 20.0% 4.3% 0.0% 0.0% & Free Choice (n=36) (n=17) (n=14) (n=3) (n=0) (n=0) Screening 36 19.4% 19.4% 58.3% 0.0% 2.8% 0.0% (n=7) (n=7) (n=21) (n=0) (n=1) (n=0)

45

TABLE 3

CHOICE SHARES IN EXPERIMENT 3

Choice Condition N Brand A Brand C Brand E Brand B Brand D Brand F Partitioned Choice 64 59.4% 21.9% 12.5% 1.6% 4.7% 0.0% (n=38) (n=14) (n=8) (n=1) (n=3) (n=0) Free Choice 59 64.4% 23.7% 10.2% 1.7% 0.0% 0.0% (n=38) (n=14) (n=6) (n=1) (n=0) (n=0) Screen-by-Inclusion 65 38.5% 21.5% 33.8% 0.0% 4.6% 1.5% (n=25) (n=14) (n=22) (n=0) (n=3) (n=1) Screen-by-Rejection 62 58.1% 19.4% 19.4% 3.2% 0.0% 0.0% (n=36) (n=12) (n=12) (n=2) (n=0) (n=0)

46

TABLE 4

CHOICE SHARES IN EXPERIMENT 4

Choice Condition N Brand A Brand C Brand E Brand B Brand D Brand F Partitioned Choice 53 58.5% 20.8% 13.2% 5.7% 0.0% 1.9% (n=31) (n=11) (n=7) (n=3) (n=0) (n=1) Free Choice 58 62.1% 20.7% 12.1% 1.7% 1.7% 1.7% (n=36) (n=12) (n=7) (n=1) (n=1) (n=1) Screening 53 41.5% 17.0% 34.0% 3.8% 1.9% 1.9% (n=22) (n=9) (n=18) (n=2) (n=1) (n=1) Screen & Rate 53 56.6% 20.8% 17.0% 5.7% 0.0% 0.0% (n=30) (n=11) (n=9) (n=3) (n=0) (n=0) Rate & Screen 55 45.5% 20.0% 30.9% 1.8% 0.0% 1.8% (n=25) (n=11) (n=17) (n=1) (n=0) (n=1)