1

THE RULES OF ATTRACTION

Shane Frederick, Leonard Lee, & Ernest Baskin

Abstract

Consumer research has documented dozens of instances in which the introduction of an

“irrelevant” third option affects preferences between the remaining two. In nearly all such cases,

the least attractive option enhances the attractiveness of the option it most resembles – a

phenomenon known as the "attraction effect." In the studies presented here, however, we

contend that this phenomenon is restricted to highly stylized product representations in which

every product dimension is represented by a number (e.g., a toaster oven that has a durability of

7.2 and ease of cleaning of 5.5). We find no such effect when consumers experience the product

(e.g., taste a drink) or when even one of the product attributes is represented perceptually (e.g.,

different priced hotel rooms whose quality is depicted with a photo). We posit that this occurs

because the comparisons underlying that contextual effect are not readily accessible in perceptual

representations.

Keywords: Attraction effect, Context effects, Attribute representation, Consumer choice

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INTRODUCTION

The "attraction effect" or “asymmetric dominance effect” (Huber, Payne, and Puto 1982;

Huber and Puto 1983) refers to instances in which adding an unattractive option to a choice set

increases the choice share of the option it most closely resembles. The effect is often upheld to

illustrate the deficiency of “rational” choice models, and the corresponding necessity of

developing psychologically richer ones. Moreover, since managers can readily manipulate the

composition of choice sets, the effect’s managerial significance is manifest.

For these reasons, the attraction effect is among the most discussed and documented

phenomena in the consumer behavior literature (see Appendix A). However, its apparent

robustness is misleading, because most demonstrations involve stimuli with attributes levels

represented by numeric indices. For such stimuli, the psychological processes respondents use

may be similar, whether those numbers happen to refer to “quality rating of TVs,” "durability of

digital cameras," "attractiveness of romantic partners," "honesty of politicians," or "capacitance

of widgets."

Such highly stylized stimuli may be sufficient to capture essential tradeoffs consumers

routinely make (e.g., between price and quality). Moreover, many of the dimensions consumers

consider in their purchasing decisions are numeric. Nevertheless, in ordinary purchase settings,

it would be rare that every attribute would be represented solely by numeric indices, and there is

no strong reason to expect that the psychological processes evoked by stylized stimuli are similar

to those evoked by more realistic stimuli. For instance, when consumers enter an electronics

store intent on purchasing a flat screen television, they do not choose between two slips of paper

depicting the picture quality and price of two unspecified brands (e.g., [7.3, $390] vs. [8.8,

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$610]). Instead, they typically stroll around the store examining various models and actually

experiencing the quality of images displayed (usually of brightly colored fish swimming around

a coral reef).

Recognizing that most research on the attraction effect involves stimuli unlike those

consumers typically encounter, Simonson (1989) encouraged future researchers to “test whether

the attraction and compromise effects and their explanation still hold in more natural consumer

environments.” However, despite this entreaty, such studies remain rare. In most demonstrations

of the attraction effect, attributes are represented solely by numeric indices (see Appendix A).

Only Sen (1998) and Simonson and Tversky (1992) report an attraction effect using perceptual

stimuli, and we have been unable to replicate those results.

This present work focuses on the existence or prevalence of attraction effects when the

relevant attributes can be directly experienced. This includes situations in which the stimuli are

actually consumed (e.g., beverages with different flavors and concentrations) or represented in

ways not involving numeric indices – either because the attributes are inherently qualitative (e.g.,

a popcorn’s brand and flavor) or because we elected to represent attribute levels perceptually

(e.g., by depicting apartment views with actual photographs, rather than ratings). Our first study

is, effectively, a meta-analysis involving a dozen studies we conducted using such stimuli.

Studies 2 – 4 hold the stimuli constant and manipulate how product attributes are represented.

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STUDY 1: TESTING FOR ATTRACTION EFFECTS WITH NON-NUMERIC STIMULI

Method

In several studies involving a total of 1,192 students from universities in the U.S. and

Asia, participants indicated their preference among various products. The stimuli are listed in

table 1.1 Our stimuli differed from those used in most prior studies, because one or more of the

product attributes could be experienced, directly perceived or somehow communicated without

the use of numbers. However, excepting this difference, our studies preserved the fundamental

structure of all studies on the attraction effect: respondents were randomly assigned to choose

either from two core options or from an expanded set that also included a “decoy” option that

was similar, but inferior to, one of the core options.

-------------------------------------- Insert tables 1 & 2 about here.

-------------------------------------- Results and Discussion

As shown in table 2, we found no evidence for an attraction effect: the decoy did not

increase the relative preference for the core option it most resembled. Indeed, averaging across

product categories, the decoy reduced the choice share of that option, sometimes significantly.

For example, in one study, respondents sampled normal strength cherry Kool-Aid, normal

strength grape Kool-Aid, and a third drink that was either dilute grape or dilute cherry (mixed to

½ the recommended concentration). Rather than finding an attraction effect, we found a

“repulsion effect”: adding dilute grape drink reduced the choice share of regular grape, and

adding dilute cherry reduced the choice share of regular cherry.

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We will revisit repulsion effects in the general discussion, but turn first to the most notable

result from Study 1: the conspicuous absence of an attraction effect. We next explore two

possible accounts for the lack of an effect.

EXPLAINING THE ABSENCE OF AN ATTRACTION EFFECT WHEN ATTRIBUTE

VALUES ARE NON-NUMERIC

Comparing tradeoff rates requires numeric specification

As one possible account of the attraction effect, Simonson and Tversky (1992) discuss

the notion of "tradeoff contrast." To illustrate this account, consider three cars that vary in fuel

efficiency and price: A= (25 mpg; $25K), B= (35 mpg; $35K), and C= (36 mpg; $42K). Fuel

efficiency is cheaper moving from A to B ($1,000 per unit) than from B to C ($7000 per unit),

and this comparison may favor B. Of course, computing tradeoff rates requires that both

dimensions are numeric, and this might help explain why we did not observe attraction effects

with our stimuli in Study 1.

Range & Number of Levels Effects

In some cases, a decoy increases the considered range for the attribute on which the target

is inferior, “shrinking” the perceived significance of that difference (see Parducci 1974)2 and

thereby enhancing its attractiveness relative to the other core option. Depending on its location

in attribute space, the decoy may also more finely partition the dimension on which the target is

superior, which usually increases the weight this dimension receives (Currim, Weinberg, and

Wittink 1981). Such effects are less applicable when attribute values are not represented by

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numbers; when the decoy is inferior to the target in a qualitative rather than quantitative sense.

For example, it is not clear how adding dilute grape Kool-aid to the choice set (regular cherry,

regular grape) either shrinks the perceived significance of grape’s lack of cherry flavor or serves

to partition the distance, in n-dimensional space, between grape and cherry.

In light of the (non) results from study 1 and the aforementioned theoretical reasons, we

propose that attraction effects could be attenuated, eliminated, or possibly even reversed if

product attributes were represented as percepts that could be directly experienced rather than as

concepts (in the form of numeric indices of attribute levels). We test this conjecture in the next

three studies by manipulating how attribute levels of a particular stimulus are represented.

STUDY 2: NUMERIC VS. PERCEPTUAL REPRESENTATIONS OF PROBABILITY IN

CHOICES AMONG GAMBLES

In this study, our stimuli were gambles that vary in the probability of winning and the

winning amount. Based on prior research involving gambles (Huber, Payne, and Puto 1982;

Wedell 1991), we expected to find an attraction effect when the probability of winning is

represented numerically. However, we conjectured that if probability was presented visually (in

the form of the shaded area of a probability wheel), these effects would be attenuated.

Method

A total of 507 participants (276 picnickers in a large northeastern city and 231 participants

from an online survey site) chose between two (or three) gambles which varied in terms of

wining amount and probability of winning. The core set included a low-risk, low-reward gamble

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(73% of chance of winning $197) and a high-risk, high reward gamble (28% chance of winning

$516). The three-option choice included a third, “decoy” gamble (23% chance of winning $507)

that was dominated by the high- risk, high-reward gamble. Using a 2 x 2 design, we manipulated

the presence or absence of the decoy gamble and the mode by which winning probability was

represented: either numerically (as it typically is) or perceptually (as the shaded region of a

probability wheel – see upper panel of figure 1).

-------------------------------------- Insert figure 1 about here.

-------------------------------------- Results and Discussion

When probability was represented numerically, we found a significant attraction effect, as

the decoy increased the choice share of the risky gamble from 14% to 28% (χ2 (1, N = 254) =

7.59; p < .01). However, when probability was represented as the shaded region of a probability

wheel, the decoy had no effect on the choice share of the riskier target gamble (24% to 26%).

We replicated these results using a different set of gambles (see lower panel of Figure 1) in a

subsequent study involving 791 respondents recruited from a private northeastern university and

a national online panel. Once again, when probability was represented numerically, we found a

significant attraction effect, as the presence of the decoy doubled the choice share of the risky

gamble (21% to 37%; χ2 (1, N = 396) = 11.78, p = .01). However, when probability was

represented as the shaded area of a probability wheel, the attraction effect was negligible (34% to

35%). See table 3 for the full results from both studies.

-------------------------------------- Insert table 3 about here.

--------------------------------------

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It is important to note that the dominance relation between the target and the decoy must

have been salient regardless of how probability was represented, since the decoy was rarely

chosen in either the numeric or visual condition. Moreover, a follow-up study revealed that

respondents could accurately estimate the fraction of the circles that were shaded.3 The visual

representation reduced the ease with which probabilities could be compared. This could help

explain why the high payoff (low probability) gambles were more popular when probability was

presented visually, since attributes receive less weight when they are harder to compare (Hsee

1996).4 The visual representations presumably also inhibited the computation of expected value

(though we suspect that few respondents attempted this computation even in the numeric

condition).

STUDY 3A: NUMERIC VERSUS VISUAL REPRESENTATIONS OF PICTURE QUALITY

Method

A total of 240 respondents from two large universities in the U.S. and in Asia chose

between flat-screen televisions that varied in price and picture quality. Using a 2 x 2 between-

subjects design, we manipulated whether the choice set contained a decoy option and the mode

by which image quality was represented (with a numeric rating or a photo).

When image quality was represented visually, we created a high-, a medium-, and a low-

quality image by using graphics software to manipulate color, sharpness, contrast, and resolution

(see figure 2). To create a corresponding numeric condition, a separate group of eighty

respondents rated the picture quality of each of these three images on a 10-point scale ranging

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from 1 (low quality) to 10 (high quality), and we used these ratings to create the stimuli: {($503,

8.0), ($350, 5.5), ($339, 3.5)}.

-------------------------------------- Insert figure 2 about here.

--------------------------------------

Results and Discussion

As shown below, when image quality was represented numerically, adding the low-

quality decoy TV caused a significant attraction effect, increasing the choice share of the target

TV from 33% to 57% (χ2(1, N = 120) = 7.08, p = .01). However, when picture quality was

represented with an image, the decoy decreased the choice share of the target from 53% to 35%

(χ2(1, N = 120) = 3.79, p = .05). A logistic regression with dummy variables for decoy presence

and mode of quality representation yielded the expected significant interaction term (β = -1.71, p

< .01). There were no significant main effects.

The implications of these results are straightforward. For example, a manager at Best

Buy who had read all published results on the attraction effect (see appendix A) might conclude

that adding a blurry, low-quality TV to the product line might increase the choice share of the

medium-quality TV. However, our results suggest instead that the manager might instead

discover that this increases the choice share of the high-quality, expensive TV.

-------------------------------------- Insert table 4 about here.

--------------------------------------

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STUDY 3B: ADDING NUMERIC RATINGS TO PERCEPTUAL STIMULI

In the previous study, we used the judgments of an external group in an attempt to create

numeric stimuli that roughly matched the visual stimuli. In this study, we attempted to assure

such correspondence by having each participant rate the picture quality of each TV.

Method

364 participants from two northeastern universities were randomly assigned to one of

four conditions using a 2 x 2 between-subjects design, which manipulated whether the ratings

followed choice (as in the prior study) or preceded it (i.e., they saw images and rated their quality

before choosing).

Results and Discussion

The table below shows the quality ratings in each of the four conditions, as well as the

proportion choosing each TV. Among participants who made their TV choice before rating

image quality, the decoy reduced the choice share of the target (46% to 36%; χ2(1, N = 165) =

1.74, p = .19). However, if participants first rendered a numeric rating of picture quality before

choosing, the attraction effect returned, as the decoy’s presence increased the choice share of the

medium TV (32% to 43%; χ2(1, N = 147) = 2.69, p = .10).

Since the decoy was chosen by only three respondents, we ran a logistic regression in

which the dependent variable is choice between the medium and high quality TV, with dummy

variables for decoy presence (or absence) and whether image quality was rated before (vs. after)

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indicating preferences. The regression results revealed a significant interaction effect (β = -.888,

p = .04) between these variables.

-------------------------------------- Insert table 5 about here

--------------------------------------

These results provide further evidence that numeric representations (even numbers that

respondents generate themselves) are an important, if not essential, ingredient in the emergence

of the attraction effect.

GENERAL DISCUSSION

In several studies, we find that when products are displayed in ways that more closely

resemble how consumers would actually experience them, the attraction effect is not observed.

We suspect that perceptual representations eliminate the attraction effect by depriving processes

that numeric representations permit and evoke (such as tradeoff contrasts). Indeed, the only

significant effects we find with non-numeric representations are “repulsion effects,” in which the

decoy hurts the option it most resembles (see Appendix A and Study 3A). In contrast with the

large body of work on the attraction effect, there has been essentially no work on the repulsion

effect. We found no prior published studies and only one reference in a microeconomics

textbook (Kreps 1990, pg. 28) who proposes, as an example, that the consideration of mediocre

French food might diminish the attractiveness of excellent French food. Though Kreps’ example

seems plausible, it stands as a notable exception to the large body of work on the attraction

effect. This is somewhat surprising, considering: (1) that his intuition has been formalized as the

law of similarity, whereby the bad properties of one object are transferred to other objects in that

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category (Rozin, Haidt, and McCauley 2000; Rozin, Millman, and Nemeroff 1986; Rozin and

Nemeroff 2002); (2) that there is widespread evidence for both contrast and assimilation effects

within the large literature on context effects in psychology (Bless and Schwarz 1998;

Mussweiler, Rüter, and Epstude 2004); and (3) that the repulsion effect is broadly consistent

with unsuccessful brand extensions, in which unattractive products such as Bic pantyhose and

Heinz pet food taint more successful products sharing the same brand name (Kotler and Keller

2005; Dimofte and Yalch 2011).

In conclusion, we reprise Simonson’s (1989) call for more research on context effects

using more ecologically realistic stimuli. Our studies certainly suggest that perceptual stimuli

behave very differently from numeric stimuli. The status of the attraction effect as a stylized fact

may even need to be reconsidered, as we find that moving outside the domain of abstract

numeric stimuli, repulsion effects may be more common than attraction effects. Indeed, we hope

that the current work will not only spur further investigation on the boundaries of the attraction

effect, but also exploration into the scope and limits of the repulsion effect.

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1 As revealed by the discrepancy between the listed sample sizes and the total number of participants in the study (1,192), respondents typically evaluated only a subset of all the stimuli tested. 2 For a perceptual example, someone who estimates the temperatures of a tepid and a warm bucket of water will regard them as differing more than someone who first experiences a hot bucket of water before estimating the respective temperatures of the two cooler buckets (i.e., the hot bucket will make the two cooler buckets seem more similar). 3 In a separate study, 187 participants simply estimated the shaded area of the probability wheels for the two core gambles. The mean judgments were fairly accurate: the wheel with a 73% shaded area was estimated to be 69% shaded, while the one with a 28% shared area was estimated to be 30% shaded. 4 In the core set, the risky gamble was chosen more often when probability was displayed pictorially than numerically. The effects were significant in both the first Study 2 (24% vs. 14%; χ2(1, N = 260) = 4.01; p = .05) and the follow up replication study (34% vs. 21%; χ2(1, N = 388) = 8.20; p < .01)

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Table 1: List of Stimuli and Attributes Used in Study 1

Product Class Attributes Attribute Levels of Competitor Target Decoy

Apartments

View (with picture of window view),

Floor space

Sea view, 530 sq. feet

Residential Apartment view,

910 sq. feet

Residential Apartment view (with

cloudy sky), 905 sq. feet

Fruit

Type, Freshness (implied in

picture)

Apple Orange Orange (with black spots on skin)

Orange Apple Apple (with dented skin)

Hotel Rooms

Décor (with picture of hotel room interior),

Rental rate

Average décor with no adjacent bath,

$120/night

Very nice décor with adjacent heart-shaped

bath, $180/night

Nice décor with adjacent polygon bath, $180/night

Jellybeans Flavor and color

Cherry (Red) Plum (Grey) Pepper (Grey) Apricot (Orange) Chocolate (Brown) Dirt (Brown) Banana (Yellow) Apple (Green) Grass (Green) Blueberry (Blue) Marshmallow (Beige) Ear Wax (Beige)

Kool-Aid Flavor and

concentration of Kool-Aid mixture

Grape Cherry Dilute Cherry

Cherry Grape Dilute Grape Mints Brand, Flavor Certs, Spearmint Altoids, Spearmint Altoids, Ginger

Movie Actors Actor, Movie Title

(with verbal description of movie)

Stallone, Rocky

Schwarzenegger, The Terminator

Schwarzenegger, Hercules in New

York

Schwarzenegger, The Terminator

Stallone, Rocky

Stallone, Stop! Or My Mom

Will Shoot

Movie Sequels Movie Title (with

verbal description of movie)

Speed Grease Grease 2

Grease Speed Speed 2

Popcorn Brand, Flavor Popz, Butter Act-II, Butter Act-II, Jalapeno Act-II, Butter Popz, Butter Popz, Jalapeno

Bottled Water Brand, Type (with picture of bottle)

Penta Water

Volvic Spring Water

Duck Fart Spring Water

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Table 2: Results of Study 1

a The first number was the sample size when the decoy was not added to the choice set, and the second number was the sample size when the decoy was added to the choice set. b This experiment involved the real tasting of food or beverage options. c This experiment was done by comparing a trinary set to another trinary set with each having a different decoy. d p < .05 using χ2-test. e p < .01 using χ2-test.

Product

Category No decoy

Decoy present

% Change in target’s

share due to decoy Target Target Decoy

Apartments a (n=134/122) 43% à 48% 2% +5%

Fruit (n=94/93) (n=94/90)

62% à 63% 0% +1%

38% à 38% 1% 0%

Hotel Rooms (n=69/60) 70% à 67% 13% -3%

Jellybeans b (n=162/165) (n=174/174) (n=200/204) (n=152/153)

52% à 46% 6% -6%

35% à 32% 2% -3%

64% à 56% 10% -8%

55% à 52% 4% -3%

Kool-Aid bc (Cherry target)

(n= 37) 58% à 35% 5% -23% d

Kool-Aid bc (Grape target)

(n=43) 59% à 40% 5% -19% d

Mints (n=123/128) 55% à 49% 6% -6%

Movie Actors (n=85/85) (n=85/80)

55% à 55% 10% 0%

45% à 40% 7% -5%

Movie Sequels (n=77/89) (n=77/84)

44% à 36% 6% -8%

56% à 48% 10% -8%

Popcorn (n=38/36) (n=38/30)

39% à 31% 5% -8%

61% à 33% 7% -28% e

Bottled Water (n=120/121) 70% à 52% 2% -18%d

Aggregated 53% à 46% 5% -7%

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Table 3: Results of Study 2

Original Study

Replication

Note:--The numbers in subscript indicate the actual participant count corresponding to the respective proportions.

Probability representation

23% chance to win $507

28% chance to win $516

73% chance to win $197

Numeric ----- 14% 18 86% 111 2% 2 28% 35 71% 90

Visual

-----

24% 31

76% 100 0% 0 26% 31 74% 89

Probability representation

28% chance to win $30

28% chance to win $33

73% chance to win $12

Numeric ----- 21% 42 79% 156 0% 1 37% 73 63% 125

Visual

-----

34% 65

66% 125 0% 1 35% 71 65% 132

21

Table 4: Results of Study 3A

Note:--The numbers in subscript indicate the actual participant count corresponding to the respective proportions.

Representation of picture quality

Low quality $339

Medium quality $350

High quality $503

Numeric ----- 33% 20 67% 40 2% 1 57% 34 42% 25

Visual

-----

53% 32

47% 28 2% 1 35% 21 64% 38

22

Table 5: Results of Study 3B Quality Ratings Percentage Choosing

Note:--The numbers in subscript indicate the actual participant count corresponding to the respective proportions.

Conditions

Low quality $339

(decoy)

Medium quality $350

(target)

High quality $503

(competitor) Choices precede

ratings ----- 5.4 7.8 3.0 6.0 8.8

Ratings precede

choices

-----

5.3

7.3

3.8 6.2 7.7

Choices precede ratings

----- 46% 37 54% 44 1% 1 36% 36 63% 64

Ratings precede

choices

-----

32% 28

68% 59

2% 2 43% 41 67% 52

23

Figure 1: Gambling Study Visual Stimuli (Study 2)

Suppose that for the gambles below, you get to spin the pointer, and if it lands anywhere in the black area, you win the amount shown. Which of the gambles below would you choose?

$197 $516 $507 Suppose that for the gambles below, you get to spin the pointer, and if it lands anywhere in the shaded area, you win the amount shown. Which of the gambles below would you choose?

$12 $33 $30

24

Figure 2: TV Study Pictorial Stimuli (Studies 3A and 3B)

Suppose you are buying a second television. Assuming that all the ones below have the same screen size, which would you choose? (Please select one.)

A (Price: $503)

B

(Price: $350)

C

(Price: $339)

25

Appendix A: List of Stimuli and Attributes Used in Articles on the Attraction Effect

Paper Stimuli Attributes Attribute Type

Ariely and Wallsten 1995 Microwaves Running shoes Computers TVs Bicycles

Price, capacity (ft.), wattage (W) Comfort, durability, price Speed (Hz), memory (MB), price Screen size (in), price, wattage (W) Price, weight (LB), wheel base (in)

Numeric Numeric Numeric Numeric Numeric

Bargava, Kim and Srivastava 2000

Cars Flights

Quality of ride, fuel Price, penalty

Numeric Numeric

Branstrom 1998 Apartments Monthly rent, distance from campus (min) Numeric Burton and Zinkhan 1987 Beer

Restaurants Price, taste quality Food quality, driving time

Numeric Numeric

Choplin and Hummel 2002 Airplane ticket Studio apartment

Cost, layover (min) Rent, commute (min)

Numeric Numeric

Colman, Pulford, and Bolger 2007

Game strategies Payout Numeric

Dhar and Glazer 1996 Automobile Stereo Apartment Manager MBA Beer Battery Restaurant VCR

Comfort rating, gas mileage Sound rating, reliability Distance (miles), condition rating Technical rating, human skill rating GMAT, GPA Quality, price/6-pack Life (# hours), price/pair Food quality, driving time (min) Picture rating, reliability rating

Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric

Doyle et. al. 1999 Audiocassette tapes Batteries Orange juice

Price, quality Price, quality Price, quality

Numeric Numeric Numeric

Ha, Park and Ahn 2009 Vacation tour packages Laptop computers Camera phone

Vacation site, Hotel Service Quality, Hotel Location Brand, Weight, Memory Capacity Phone Type, Screen Size, Resolution

Numeric Numeric Numeric

Heath and Chatterjee 1991, 1995 Beer Cars

Price, quality rating Car mileage, ride quality

Numeric Numeric

Hedgcock, Rao, and Chen 2009 Beer Health plans Cruises Housing Automobiles Presidential candidates

Price, quality Max coverage, copay, % donor participation Price, incidence of disease Crime rate, number of bedrooms Safety, lease terms Economic policy, international policy

Numeric Numeric Numeric Numeric Numeric Numeric

Highhouse 1996 Job candidates Interview rating, promotability rating Numeric Huber, Payne, and Puto 1982; Huber and Puto 1983

Beer Cars Restaurants Lotteries Film TV sets Calculator batteries

Price/six pack, quality Ride quality, gas mileage (mpg) Driving time (min), food quality Chance of winning, amount of win Developing time (min), color fidelity Percent distortion, reliability Estimated life (# hours), price/pair

Numeric Numeric Numeric Numeric Numeric Numeric Numeric

Kim and Hasher 2005 Grocery discounts Extra credit

Discount offered (%), minimum purchase required ($) Extra credit offered (points), min amount of time required (min)

Numeric Numeric

Mishra, Umesh and Stem 1993 Beer Cars TV sets

Price/six pack, quality Ride quality, gas mileage Percent distortion, reliability

Numeric Numeric Numeric

Moran and Meyer 2006 Vacation deals Price, hotel quality Duration, hotel quality

Numeric Numeric

Olsen and Burton 2000 Cars Gas mileage, reliability rating Numeric Pan and Lehmann 1993 TV sets

Apartments Batteries Compact sedan Light bulb

Resolution (lines), durability (months) Size (sq. feet), closeness to campus (secs to walk) Expected life (hours), price Fuel efficiency (mpg), acceleration Expected life (hours), light output (output)

Numeric Numeric Numeric Numeric Numeric

Pan, O’Curry and Pitts 1995 Political candidates Education, crime control, tax policy Numeric

26

Prelec, Wernerfelt and Zettelmeyer 1997

Air conditioners Binoculars Auto-focus cameras Coffeemakers Rain boots Running shoes Vacuum cleaners VCRs

Operating noise rating, price Magnifying power, price Number of features, price Quality rating, price Durability rating, price Cushioning ability rating, price Suction power rating, price Durability rating, price

Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric

Ratneshwar, Shocker and Stewart 1987

TV sets Orange juice Beer Cars Light bulbs Gas barbeque grills

Percent distortion, reliability (years) Price/64oz, quality rating Price/six pack, quality rating City mileage (mpg), ride quality Light output (lumens), expected life hours Cooking area (sq. ins) fuel tank capacity (hours)

Numeric* Numeric* Numeric* Numeric Numeric Numeric

Scarpi 2008 MP3 players Price, data capacity Numeric Schwartz and Chapman 1999 Medication Treatment effectiveness, probability of side effects Numeric Sedikides, Ariely and Olsen 1999

Partner attributes Attractiveness, honesty, sense of humor, dependability, intelligence

Numeric

Sen 1998 Restaurants Food, atmosphere Numeric & Qualitative

Simonson 1989 Beer Cars Color TV Apartment Calculator Mouthwash Calculator battery

Price of a six-pack, quality Ride quality/gas mileage Price, picture quality Distance, general condition No of functions, Probability of repair in first 2 years Fresh breath effectiveness, germ killing effectiveness Expected life (hours), probability of corrosion

Numeric Numeric Numeric Numeric Numeric Numeric Numeric

Simonson and Tversky 1992 Microwave ovens Paper towels vs. facial tissues Cash vs. pens Gasoline Personal Computers

Capacity, price, discount Quality (of paper towels vs. facial tissues) Quality (of pens) Quality (amount of octane), price/gallon Memory (K), price

Numeric Perceptual Perceptual Numeric Numeric

Tentori, et. al. 2001 Supermarket discounts Discount offered (%), minimum purchase required ($) Numeric Wedell 1991 Gambles

Automobiles Restaurants TV sets

Probability to win, amount to win Ride quality, gas mileage (mpg) Quality rating, driving time (min) Percent distortion, reliability

Numeric Numeric Numeric Numeric

Wedell and Pettibone 1996; Pettibone and Wedell 2000

Computers Restaurants Plane tickets Mechanics CD players Apartments Cars Boats Job offers Houses Electric keyboards Mini-LCD TVs Preschools Microwaves Parking spaces Video cameras Beer (24 packs) Cars Restaurants TV sets

Processing speed (MH), size of hard drive (MB) Price of meal for two, wait to be served (minutes) Cost of ticket ($), Length of layover (minutes) Warranty length (days), experience (years) Price, number of disks Rent, distance (minutes) Miles per gallon, number of safety features Number of passengers, speed (knots per hour) Number of days of sick leave, number of paid holidays Price (thousands of $), square footage Tone quality (1-100), number of features Price, percent distortion Children per classroom, teacher’s experience (years) Warranty (months), cooking power (watts) Price per month, distance from work (blocks) Weight (pounds), number of features Price, Quality (1-100) Ride quality (1-100), miles per gallon Distance from home (minutes), quality (1-5 stars) Percent distortion, average life span (years)

Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric

Zhou, Kim and Laroche 1996 Cars Orange juice Calculators

City mileage (mpg), ride quality rating Price, quality rating Number of functions, probability of need for repair in first 2 years

Numeric Numeric Numeric

* The qualitative ratings were supplemented with elaborate verbal descriptions of their meanings.