SUPPLEMENTARY ONLINE APPENDIX

Does Exposure to Dogs (Cows) Increase the Preference for Puma (the Color White)? Not

Always

Study 1: Replication of Conceptual Fluency (“Dog” and the Preference for Puma / Jaguar).

Study 1 was designed to replicate the effects of priming product-related concepts on

product evaluations (Berger & Fitzsimmons 2008; referred to as BF from hereon). Specifically,

we tested whether priming the concept of “dog” would activate the related concept “cat”, and

thereby increase preference for the Puma brand. The hypothesized differences in brand

preference should only occur for participants who are familiar with the Puma brand (BF).

Another purpose of this study was to test the fluency effects of the prime “dog” on another brand

– namely, Jaguar - that is also related to cats. In summary, we tested the effect of priming “dog”

on the preference for the brands Puma and Jaguar, which both belong to the cat family.

Methodology

Two-hundred and ninety-one students from a North American university (165 males, 126

females, Mage = 20.74; SD = 0.72) participated in an online study in exchange for extra course

credit. The study was a 2 (prime: dog, no prime) × 2 (target brand: Puma, Jaguar) between

subjects design, and participants were randomly assigned to one of the four conditions. Those in

the prime condition viewed 5 images of different breeds of dogs, and were asked to rank these

five breeds from the most favourite (1) to the least favourite (5). Subsequently, participants saw

one of two sponsorship scenarios in which either (1) a software developer of sports applications

for smartphones was seeking an athletic shoe brand as a sponsor or (2) a software developer of

traffic applications for smartphones was seeking an automobile brand sponsor. In the athletic

shoe brand sponsor scenario participants had to chose from Converse, New Balance, Puma, and

Basics. In the brand sponsor scenario they had to choose from LandRover, Volvo, Jaguar, and

Volkswagen. Both the names and logos of the brands were presented. Participants then indicated

whether they were familiar with the four brands in their respective sponsorship scenario and also

rated their liking for each of the brands (1= do not like at all, 7 = like it a lot). As a check for the

purported relation between the concepts of “dog” and “cat,” participants were asked to list five

words that come to mind when they thought of the word “dog”. We also used a funnelled

debriefing procedure (adapted from Bargh Gollwitzer, Lee-Chai, Barndollar & Troetschel 2001)

to examine whether participants suspected that there was a link between the prime “dog” and

their choice of the sponsor brand.

Results

None of the participants indicated any link between the prime and their brand choice. We

conducted a logistic regression on brand choice and an ANOVA on brand liking with the two

factors (prime: dog, no prime and target brand: Puma, Jaguar) and their interaction as predictors.

The interaction term was not significant in both the logistic regression (p > .70) and the ANOVA

(p > .50). Thus we conducted further analysis separately for each of the two target brands.

The Puma Brand Scenario

Association between “dog” and “cat.” One hundred and forty-two participants were

randomly assigned to receive the Puma brand sponsorship scenario (79 males, 63 females, Mage =

20.70; SD = 0.73). Only 12.7% of participants mentioned the word “cat” as one of the five words

that come to mind when they thought of “dog”. This indicates that the relation between “dog”

and “cat” is weaker than previously reported (BF). Priming “dog” was not a significant predictor

of whether the word “cat” was mentioned (B = -.76, Exp(B) = 0.47, Wald = 1.87, df = 1, p > .10),

although it was directionally higher for the prime versus no-prime condition. Note that the latter

is only a post-hoc measure of the purported strong relation between the concepts of “dog” and

“cat”, which is proposed to drive the conceptual fluency effects of priming “dog” on the

preference for brands related to the cat family. Also, note that once we ask participants to list the

words that came to mind when they thought of “dog” we have effectively primed this concept for

all participants, and thus any prior exposure to the prime “dog” is not expected to have a

significant effect.

In this condition, 93.7% of participants were familiar with the Puma brand. Since brand

familiarity is a pre-requisite for conceptual fluency effects (BF), in the subsequent analyses we

only focused on participants who were familiar with Puma (n = 133).

Brand Choice. We ran a logistic regression analysis with priming condition as predictor

variable and brand choice (1= Puma, 0 = other) as the outcome variable. The model was not

statistically better than the constant-only model (Chi-square = 0.03, p > .80), and the priming

condition did not predict brand choice (B = .06, Exp(B) = 1.06, Wald = .03, df = 1, p > .80).

Brand Liking. We also found that the degree to which participants liked the Puma brand

did not differ between those who received the dog prime (M = 5.05, SD = 1.60) and those who

did not receive the prime (M= 4.96, SD= 1.65; t (131) = 0.32, p > .70) (see Table 1).

We also compared the brand liking and choice for respondents that did mention an

association between “dog” and “cat” (12.7% of respondents, as reported earlier) with those that

did not do so. The results showed that Puma brand liking did not differ between these two groups

(M= 5.25 vs. 4.97; t(131) = 0.66, p > .50); and the Puma brand choice was also not different

between them (B = -.09, Exp(B) = 0.92, Wald = .03, df = 1, p > .80).

The Jaguar Brand Scenario

Association between “dog” and “cat.” One hundred and forty-nine participants (86

males, 63 females, Mage = 20.78; SD = 0.71) were randomly assigned to receive the Jaguar brand

sponsorship scenario. Only 18.1% of participants mentioned the word “cat” as one of the five

words that come to mind when they thought of “dog”. This further suggests that the purported

relation between “dog” and “cat” is not as strong as previously reported (BF). As before, priming

“dog” did not predict the likelihood of mentioning “cat” (B = -0.67, Exp(B) = 0.51, Wald = 2.42,

df = 1, p >.10), although it was directionally higher in the prime versus no-prime condition.

In this condition, 91.9% of participants were familiar with the Jaguar brand. As before, in

the subsequent analyses we only focused on these participants (n = 137).

Brand Choice. We ran a logistic regression on the choice of Jaguar as the dependent

variable and priming condition as a predictor variable. As before, priming was not a significant

predictor of brand choice (B= 0.33, Exp(B) = 1.39, Wald = 0.62, df = 1, p > .40).

Brand Liking. We also found that the degree to which participants liked the Jaguar brand

did not differ between those who received the dog prime (M = 5.62, SD = 1.28) and those who

did not receive the prime (M = 5.41, SD = 1.52; t (135) = 0.88, p > .30) (see Table 1).

We also compared the brand liking and choice for respondents that did mention an

association between “dog” and “cat” (18.1% of respondents) with those that did not do so. The

results showed that both Jaguar brand liking and choice were in the opposite direction to

expectations (i.e., it was lower for those mentioning the association vs. not; liking: M = 4.88 vs.

5.64; t(135) = -2.53, p = .01; choice: B = -1.33, Exp(B) = 0.26, Wald = 3.01, df = 1, p = .08).

Priming Stimuli used in Study 1

Think about the category of dog. Below are some pictures of different breeds of dogs. Please

rank the following breeds of dogs from 1 (most favorite) to 5 (least favorite), in terms of how

appealing they are to you.

Note that each dog has to get a different rank from 1 to 5.

Sponsorship Scenario (Target Brand: Puma)

Imagine that a new software company has approached you with an assignment. The software company is started by a group of students from your university and that from your neighboring university. They make a software application for smartphones, which can keep track of cultural and sporting events in the university and alert students of upcoming events in university. In addition it also allows students to buy tickets for these cultural and sporting events in their university.

The above software company is seeking an athletic sports brand as sponsor for their product. They have shortlisted the following four sporting brands as potential sponsors. Which one of these would you choose for being a sponsor for this software company:

Sponsorship Scenario (Target Brand: Jaguar)

Imagine that a new software company has approached you with an assignment. The software company is started by a group of students from your university and that from your neighboring university. They make a software application that keeps track of the traffic conditions and accidents in the Toronto region and all highways (including major highways such as 401, 407, 403, QEW, and smaller highways such as 85, 8 and 6). The software can be installed on Smartphones, existing GPS devices and navigation systems in automobiles.

The above software company is seeking an automobile brand as sponsor for their product. They have short-listed the following four automobile brands as potential sponsors. Which one of these would you recommend for being a sponsor for this software company:

Study 2A: Replication of Conceptual Fluency Using Different Stimuli (“Cow” and the

Preference for a White-Coloured Product).

The results from study 1 suggest that the association between the two concepts of “dog”

and “cat” may not be as strong as previously thought. The objective of studies 2A and 2B was to

examine the conceptual fluency effects of priming product-related concepts using a different set

of stimuli. Specifically, we examined whether priming the concept of “cow” would activate the

related concept “milk”, which would further activate “white,” and thereby increase the

preference for white-coloured products.

Methodology

One-hundred and forty-two undergraduate students from a North American university (79

males, 63 females, Mage = 20.70, SD = 0.73) participated in this online study in exchange for

extra course credit. Participants were randomly assigned to a prime-condition or a no-prime

control condition. Those in the prime condition viewed 5 images of different breeds of cows, and

were asked to rank these five breeds from the most favourite (1) to the least favourite (5). Then,

participants read about the introduction of a new product, the Microsoft Surface Tablet, to the

market. This product was not available in the market at the time we ran the study, so it is

unlikely that participants had prior familiarity with the product. Within the scenario it was stated

that the new product would be available in two colors: black or white, and a picture was shown

for both the options. Participants answered some filler items including measures of their attitude

towards the product and their purchase intentions. Then – as a measure of the variable of interest

– participants indicated that if they were to purchase the product, which colour – white or black –

would they choose to buy. Participants also rated their liking for five different colors including

the color white (1= do not like at all, 7= like it a lot). Subsequently, participants were asked to

list five words that came to mind when they thought of the word “cow”. Finally, participants

reported how frequently they drank milk, and whether they lived near a farm with cows.

Results

Association between “cow” and “milk.” Results showed that 83.8% of participants

mentioned “milk” as one of the five words that come to mind when they think of “cow”. This

suggests that the relation between “cow” and “milk” is much stronger than that we found

between “dog” and “cat” in the prior study. As such, this provides for a stronger test for the

conceptual fluency effects of priming product-related concepts. Priming “cow” did not have an

effect on the likelihood of mentioning “milk” (p > .30). As mentioned in earlier in study 1, this is

not an issue since the above measure was only used to ascertain the strength of relation between

“cow” and “milk.” As such, asking participants to list the words that came to mind when they

thought of “cow” effectively primed this concept for all the participants (i.e., in both the prime

and non-prime conditions).

Choice of the white-coloured product. A logistic regression showed that priming “cow” did

not affect the choice of the white-coloured product (B= -.19, Exp(B)= .83, Wald= 0.28, df = 1, p

> .50) (see Table 1). We then used liking of the color white as a covariate, and found that while

this covariate was a significant predictor of the white-coloured product (B= .69, Exp(B) = 2.00,

Wald = 18.08, df = 1, p < .001), priming “cow” remained a non-significant predictor of the

choice of the white-coloured product (B= -.02, Exp(B) = .99, Wald = 0.001, df = 1, p > .90). We

ran the same analysis with milk drinking frequency and living near a farm with cows as

covariates. Both of the covariates did not predict the choice of the white-coloured product (B=

-.05, Exp(B) = 0.95, Wald = 0.24, df = 1, p > .60 and B= -.47, Exp(B) = 0.62, Wald = 0.72, df =

1, p > .30, respectively) and including each of these two covariates did not change the above-

mentioned results.

Study 2B: Conceptual Fluency Using Different Stimuli (“Cow” and the Evaluation of a

White-Coloured Product).

Methodology

One hundred and forty-nine undergraduate students from a North American university (86

males, 63 females, Mage = 20.78, SD = 0.71) participated in exchange for extra course credit. The

study was conducted online. Participants were randomly assigned to a prime-condition or a no-

prime control condition. The priming procedure was identical to that used in study 2A.

Participants then read about the introduction of the new Microsoft Surface tablet, and they were

presented with the product in the color white only. Participants responded to some filler

questions, which were similar to those used in study 2A, with the main dependent variables

embedded among these items. The main dependent variables were the degree to which

participants liked the product (1= do not like it at all, 7= like it very much) and the price that they

would be willing to pay for the product (WTP). Subsequently, participants listed five words that

came to mind when they thought of the word “cow”. Finally, participants reported how

frequently they drank milk, and they indicated whether they lived near a farm with cows.

Results

Association between “cow” and “milk.” The results showed that 84.6% of participants

mentioned “milk” as one of the five words that come to mind when they thought of “cow”.

Priming “cow” did not have an effect on the likelihood of mentioning “milk” (p > .80).

Evaluation of the white-coloured product. We ran a regression analysis to test the effect of

priming “cow” on product evaluation while controlling for the liking for the color white. Results

indicated that priming “cow” did not affect the evaluation of the white-coloured product (B = -

0.03, t (146) = -0.41, p > .60) (see Table 1). Liking for the color white did not significantly

predict the evaluation of the white-coloured product (B = 0.11, t (146) = 1.36, p > .10). We ran

the same analyses with milk drinking frequency and living near a farm with cows as covariates.

These two covariates did not predict evaluation (ps > .10) and did not change the above results.

WTP for the white-coloured product. We ran a regression analysis to test the effect of

exposure to the prime “cow” on the amount of money that participants are willing to pay for this

product, while controlling for the liking of the color white. Results indicated that priming “cow”

did increase the WTP for the white-coloured product as compared to the no-prime control

($356.71 vs. $312.58; B = 0.19, t (146) = 2.28, p < .05) (see Table 1). Note that there were no

outliers in the WTP data (no values above or below 3 SDs), and the Levine’s test for

homogeneity of variance across conditions was satisfied (p > .40). Liking for the color white did

not significantly predict WTP for the white-coloured product (B = 0.10, t (146) = 1.17, p > .20).

We ran the same analysis with milk drinking frequency and with living near a farm as covariates.

The results indicated that neither of the two covariates had an effect on WTP (ps > .10), and that

the above pattern of results did not change after controlling for these variables.

Priming Stimuli used in Study 2A and Study 2B

Think about the category of cow. Below are some pictures of different breeds of cows.

Please RANK the following breeds of cows from 1 (most favorite) to 5 (least favorite), in terms

of how appealing they are to you. Note that each cow has to get a different rank from 1 to 5.

New Product Description used in Study 2A

Study 3: Repeated Exposure to Dogs (Apple iPhone) and the Preference for Puma (the

Fruit Orange)

Study 3 was designed to test whether repeated exposure to a prime (e.g., to dogs) has a

significant effect on the preference for conceptually related brands (e.g., to Puma) and products.

BF found that repeated exposure to a prime enhances the conceptual fluency effects on related

concepts. For instance, they found that exposure to ten versus five images of dogs significantly

increased the evaluation of the Puma brand shoes (BF; Study 4). We wanted to replicate this

effect using a proxy measure for repeat exposure; namely, the ownership of dogs on the

preference (ownership) of Puma branded sports shoes. Furthermore, we wanted to test the effect

using a different set of related concepts; namely, the ownership of Apple iPhone on the

preference for the (conceptually related) fruit orange.

Methodology

We recruited 1180 members of the U.S. general population (603 males, 577 females,

96.1% of participants were between 18 and 54 years, 95.0% of participants reported English as

their native language) through an online panel provided by a professional marketing research

firm. The participants received monetary compensation in exchange for their participation. The

study was a part of a larger study and was purported to assess the liking and preference for

different brands and products. In the part relevant to our context, participants were first asked to

mention the first word that came to their mind after hearing the word “dog” and “apple” (these

two words were embedded within a total of eight words). In our sample 15.6% of respondents

mentioned “cat” in response to “dog,” and 7.0% mentioned “orange” in response to “apple.”

Participants were then presented with a list of eight brands of sports shoes (namely, Nike,

Adidas, Reebok, Puma, New Balance, Converse, Basics, Timberland) and were asked to indicate

all the brands that they were familiar with (including the “none of these” option), how much they

liked each of the brands (1= do not like at all, 7 = like it a lot), and which of these brands’ sports

shoes they owned (including “none of these”). In the next part, participants were presented with

eight kinds of fruit (orange, apple, strawberry, pineapple, blueberry, banana, raspberry, and

mango) and they were asked to select their favourite fruit (including “none of these”).

Participants were also asked to select their favourite kind of fruit juice from among six kinds

(orange juice, apple juice, strawberry juice, pineapple juice, mango juice, and blueberry juice; or

“none of these”). Subsequently, participants read a list of eight different brands of cell phones

(Apple iPhone, Blackberry, Samsung, Sony, Motorola, LG, Nokia, and HTC). They were asked

to mark the brands that they are familiar with and to select the brands that they owned (including

“none of these”). Finally, participants were presented with a list of six kinds of pets (cat, dog,

parrot, rabbit, lizard, snake) and were asked to select the ones that they owned (including “none

of these”). The study concluded with demographic questions (pertaining to their gender, age,

income, education and native language).

Results

Dog Ownership and Puma Ownership. Nine-hundred and twenty-six participants were

familiar with the Puma brand. A Chi-square test within this subset of participants indicated that

there was a significant relationship between dog ownership and Puma ownership among those

who are familiar with the Puma brand (Chi-square = 8.51, df = 1, p < .01). Those who owned

dogs were significantly more likely to own Puma sports shoes (18.5%) compared to those who

did not own dogs (11.7%) (see Table 2). We also ran a logistic regression analysis with dog

ownership as the predictor variable and Puma ownership as the outcome variable, controlling for

the effects of sex and income. The results indicated that sex and income were both significant

predictors of Puma ownership (B = .63, Exp(B) = 1.88, Wald = 10.25, df = 1, p < .01 and B = .22,

Exp(B) = 1.25, Wald = 9.47, df = 1, p < .01, respectively). More importantly, dog ownership

significantly predicted Puma ownership, after controlling for these two demographic variables in

the model (B = .45, Exp(B) = 1.58, Wald = 5.60, df = 1, p < .05).

Apple iPhone Ownership and Favourite Fruit. Seven-hundred and eighty-seven

participants reported that they were familiar with Apple iPhones. Within this subset of

participants, those who owned an Apple iPhone were more likely than those who did not own an

iPhone to select orange as their favourite fruit (14.4% vs. 7.5%; Chi-square = 8.56, df = 1, p

< .01; See Table 3). Apple iPhone owners were also more likely (than non-owners) to select

orange juice as their favourite juice (51.9% vs. 44.0%; Chi-square= 3.93, df = 1, p < .05). We

also ran logistic regressions controlling for the effects of gender and income. The association

between Apple iPhone ownership and orange as favourite fruit remained significant (B = .65,

Exp(B) = 1.92, Wald = 6.14, df = 1, p < .05), while that for orange juice was directionally so (B =

.22, Exp(B) = 1.25, Wald = 1.81, df = 1, p > .10).

We also examined the relationship between Apple iPhone ownership and the preference

for the fruit apple (as well as apple juice). The analysis revealed that owners of Apple iPhone

were only directionally higher (than non-owners) in their choice of apple as their favourite fruit

(17.6% vs. 13.8%; Chi-square = 1.75, df = 1, p > .10); and the two groups did not differ in their

choice of apple as their favourite juice (18.5% vs. 16.8%; Chi-square = 0.32, df = 1, p > .50).

Table 1: The Effect of Priming on Brand Evaluations and Choice.

Prime No Prime n

Study 1 Puma brand choice 49.4%a 50.8%a 133

Puma brand liking 4.84a

(1.76)

4.91a

(1.65)

133

Jaguar brand choice 23.5% a 19.1% a 137

Jaguar brand liking 5.60a

(1.27)

5.46a

(1.52)

137

Study 2A Choice of the white product 37.0% a 32.8% a 142

Study 2B White product evaluation 4.51a

(1.43)

4.63a

(1.29)

149

Willingness to pay for white $356.71a

(112.69)

$312.58b

(128.73)

149

Note. Standard deviations are in parentheses. In each row, values with a different

superscript are statistically different from one another (p < .05). The findings reported for

Study 1 reflect the subset of participants in each study that were familiar with the target

brand (n =133 for the Puma brand and n =137 for the Jaguar brand).

Table 2: The Relationship between Dog Ownership and Puma Brand Ownership (Study 3).

Dog Owners

n = 454

Dog Non-owners

n = 472

Puma sports shoes ownership 84 (18.5% a) 55 (11.7% b)

Note. These results reflect the subset of participants that were familiar with the Puma

brand (N =926). Values with different superscripts are statistically different from one

another (p < .05).

Table 3: The Relationship between Apple iPhone Ownership and Preference for the Fruit Orange

(Study 3).

iPhone Owners

n = 216

iPhone Non-owners

n = 571

Orange as favourite fruit

Orange as favourite juice

31 (14.4% a)

112 (51.9% a)

43 (7.5% b)

251 (44.0% b)

Note. These results reflect the subset of participants that were familiar with the Apple

iPhone (N =787). In each row, values with different superscripts are statistically different

from one another (p < .05).

REFERENCES

Bargh, John A., Peter M. Gollwitzer, Annette Lee-Chai, Kim Barndollar, and Roman Troetschel

(2001), “The Automated Will: Nonconscious Activation and Pursuit of Behavioral

Goals,” Journal of Personality and Social Psychology, 81 (December), 1014-1027.

Berger, Jonah and Grainne Fitzsimmons (2008), “Dogs on the Street, Puma on the Feet: How

Cues in the Environment Influence Product Evaluation and Choice,” Journal of Marketing

Research, 45 (February), 1-14.