There’s No Such Thing as a Free Lunch: Consumers ... · demonstrates that the number zero can...

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There’s No Such Thing as a Free Lunch: Consumers’ Reactions to Pseudo Free Offers

STEVEN K. DALLAS

VICKI G. MORWITZ

New York University

Contact Information:

Steven K. Dallas

PhD Student

Stern School of Business, New York University

40 West 4th Street

Office 823

New York, NY 10012

T: (212) 998-0525

Email: [email protected]

Vicki G. Morwitz

Harvey Golub Professor of Business Leadership and Professor of Marketing

Stern School of Business, New York University

40 West 4th Street

Office 807

New York, NY 10012

T: (212) 998-0518

Email: [email protected]

Author’s Note and Acknowledgments: Steven K. Dallas is a Ph.D. Student in the Marketing

Department, Stern School of Business, New York University (email: [email protected]).

Vicki G. Morwitz is the Harvey Golub Professor of Business Leadership and Professor of

Marketing, Stern School of Business, New York University (email: [email protected]).

The authors would like to thank Elizabeth McCollom, Lisa Testa, and Maryann William for

research assistance, and Adam Alter for helpful comments on a previous draft.

The authors report no financial disclosures.

mailto:[email protected]



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There’s No Such Thing as a Free Lunch: Consumers’ Reactions to Pseudo Free Offers

ABSTRACT

We examine how consumers respond to pseudo free offers—offers that are framed to consumers

as free, but have real costs for them. Across seven studies, we find that consumers responded to

pseudo free offers as if they had no costs. When presented with an offer that is framed as “free”

but has a real cost—such as being forced to download an airport’s app, pay $3 for coffee shop

goods or complete a customer satisfaction survey, or expose oneself to commercial breaks and

mediocre sound quality—consumers are significantly more likely to accept the offer than a

comparable non-free offer, even when the cost of the pseudo free offer exceeds its benefit. We

provide evidence that this happens because consumers naturally tend to make positive

attributions for why the firm makes these offers. However, when consumers make negative

attributions regarding the pseudo free offer because they are dispositionally suspicious (study 4)

or induced to make negative attributions (study 6), the pseudo free effect disappears, and

consumers’ preference for pseudo free offers is reduced.

Keywords: Pseudo Free, Pricing, Attributions, Zero

Word Count: 174

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Free offers abound in the marketplace. Google offers free email to customers in the form

of Gmail, Twitter does not charge its users to post content and follow friends and celebrities, and

many apps for smartphones allow customers to play games, stream music, and follow the news at

no cost. These are just three examples of the many instances in which companies offer customers

free products and services. However, these offers are rarely truly “free” and in some cases are

even designed to defraud consumers. In 2009, the Better Business Bureau listed “Free” Trial

Offers as one of the top ten scams plaguing the country (Better Business Bureau 2010).

Although unscrupulous firms do use “free” offers to trick consumers, most “free” offers

are not outright scams, and many are offered by legitimate, well-respected companies. However,

many “free” offers come with very real costs, although they can be subtle and difficult for

consumers to detect. We call such “free” product and service offers “pseudo free,” since they

appear free on the surface, but there is nonetheless a cost involved for the consumer. Sometimes

the cost is monetary, but we have focused primarily on situations in which the cost of using the

product or service is non-monetary, and relatively difficult to quantify.

For example, as noted by CNN’s Heather Kelly, “Gmail doesn’t cost any money to use,

but it’s not free” (Kelly 2014, 1). In exchange for the service, consumers allow Google to scan

their emails. Google then uses these data to direct relevant advertisements to its users. Although

Gmail is free in the monetary sense, the service comes at the cost of loss of privacy (Kelly 2014),

which may be difficult to quantify in monetary terms, but is nonetheless a very real cost.

Similarly, Facebook does not charge users for its services, but this free service also

comes at a cost. In addition to the loss of privacy and the exposure to targeted advertisements

that accompany one’s use of Facebook, by creating an account, “Facebook users endorse an

online statement that gives the site permission to use their personal information for research”

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(Bower 2014, 1). Although they granted this permission, in one recent example, Facebook users

expressed outrage when they later learned that Facebook, without their awareness, had

manipulated their emotions (BBC 2014). Thus, ostensibly free services can come at great cost,

such as allowing one’s emotions to be manipulated\.

Similarly, consumers who download “free” apps may be placing their location and device

information in the hands of advertisers (Arthur 2013). For example, the free Android app

“Brightest Flashlight,” which has been installed between 50 and 100 million times, secretly

recorded users location and device ID information and shared those data with third parties,

including advertisers (Truong 2013). Although users of the app did not realize that the

information would be shared, they did agree to have the information collected. Accordingly,

many of the products and services that are labeled as “free” in the marketplace come with costs.

Given the prevalence of pseudo free offers, research is needed to better understand how

consumers respond to them. Previous research has demonstrated that consumers highly value

truly free offers (i.e., Chandran and Morwitz 2006; Shampanier, Mazar, and Ariely 2007), but no

research has examined whether they similarly value pseudo free offers. Moreover, pseudo free

offers raise many questions that are not applicable to free offers. For example, do consumers

even notice the costs of pseudo free offers and, if they do, do they rationally trade off these costs

against the benefits of the offer? Do consumers accept pseudo free offers even when their costs

outweigh their benefits? If consumers do accept costly pseudo free offers, why is this the case?

The current research examines these important practical and theoretical questions. We

propose that the attributions consumers make regarding why firms offer pseudo free deals

determine how they respond to them. Because free is highly salient (Chandran and Morwitz

2006) and elicits positive affect (Shampanier et al. 2007), consumers are inclined to make

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positive attributions regarding the pseudo free offer, ignoring the costs and believing that it is

intended to benefit the consumer, or that the cost of the pseudo free offer is fair given the benefit

of the offer. Accordingly, they are likely to accept the offer, essentially behaving as if the offer is

truly free. Moreover, because “free” is so attractive to consumers, they sometimes even respond

positively to pseudo free offers where the costs exceed the benefits. Only when consumers are

suspicious and make negative attributions—for example, that the pseudo free offer is an obvious

scam or is intended to harm the consumer—will they be unlikely to accept the offer.

We first briefly describe the existing literature regarding consumers’ responses to the

number zero and free products. Next, we briefly discuss the literature concerning the attributions

consumers make about products and services, and their impact on consumer behavior. We then

develop our hypotheses and detail seven studies that test our hypotheses. We conclude by

discussing the limitations, implications, and future directions for our research.

THEORETICAL DEVELOPMENT

Research has demonstrated that consumers treat the number zero differently than other

numbers (Kahneman and Tversky 1979; Palmeira 2011). For example, consumers are more

likely to choose a gamble that has a certain positive outcome (0% chance of losing) over a

gamble with a small probability of losing (1% chance of losing), even when the latter has a

higher expected value (Kahneman and Tversky 1979). Similarly, a zero-value on an attribute can

lead to preference reversals (Palmeira 2011). When making decisions, consumers tend to rely

and focus on relative differences between options (Tversky and Kahneman 1991; Wong and

Kwong 2005). Because any number is infinitely greater than zero, gauging the relative difference

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between two options when one of the options has an attribute value of zero is difficult (if not

impossible). As shown in Palmeira (2011), if CD-Changer A has an audio-signal distortion of

.003 and CD-Changer B has an audio-signal distortion of .01, it is clear that A is 3 times better

than B. Consistent with this, most participants select A. However, when A is made objectively

better by dropping its audio-signal distortion to 0 (while B was kept constant), A’s choice share

actually drops because participants are unable to judge the relative difference between the CD-

changers. Palmeira (2011) also showed that the choice share of a product can increase when it is

made objectively worse, if a positive characteristic (e.g., number of free coffee pods that come

with a coffee maker) is dropped from a low number to zero. Thus, this body of work

demonstrates that the number zero can influence consumer behavior in irrational ways.

Since a free price is a zero value on the price attribute, consumers may also react in

irrational ways to free offers. Indeed, research has shown in some cases that consumers’

reactions to zero prices are irrational. However, consumers’ reactions in terms of their future

willingness to pay are not always positive, and this work has shown that sometimes marketers

benefit from offering products and services for free but in other cases they do not. For example,

presenting a product as free may cause consumers to discount the quality and value of the item

(Kamins, Folkes, and Fedorikhin 2009; Raghubir 2004). When a product is offered as a free gift,

people are subsequently willing to pay less for it than when it was not previously offered as a

free gift (Raghubir 2004). Moreover, when products are bundled together and one of the products

is promoted as “free,” people subsequently are unwilling to pay as much for either the “free”

product or the “non-free” product as stand-alone products, compared to when both items in the

bundle are presented as non-free. However, people are willing to pay just as much for the entire

bundle when one of the items is promoted as “free” as when both items are presented as non-free

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(Kamins et al. 2009). On the other hand, Palmeira and Srivastava (2013) examined subsequent

willingness to pay for an item after it has been promoted as free or at a low, discounted price.

After the promotion ended or was retracted, people were willing to pay more for the item after it

was promoted as free, as opposed to after it was promoted at a low, discounted price.

The findings regarding the influence of free promotions on purchase are more consistent

and show a positive effect. For example, dropping the price of an item to free can increase the

choice share of the item, and can even result in preference reversals (Shampanier et al. 2007). In

one study, Shampanier et al. (2007) offered participants in one condition a choice between a less

preferred Hershey’s chocolate for $0.01and a more preferred Lindt truffle for $0.14. In the other

condition, the Hershey’s chocolate was offered for free ($0.00) and the Lindt truffle was offered

for $0.13. The authors discuss how the standard economic model would predict that the choice

shares should be similar across conditions, since the difference in price and the desirability

between the Hershey’s chocolate and the Lindt truffle were both held constant. However, instead

there was a significant preference reversal when the Hershey’s chocolates were offered for free.

Whereas 8% of participants chose the Hershey’s chocolate when it was offered for $0.01 (and

30% chose the Lindt truffle, while 62% chose nothing), 31% chose the Hershey’s chocolate

when it was offered for free (and 13% chose the Lindt truffle, while 56% chose nothing). The

researchers replicated this effect many times, and concluded that the preference reversal occurs

because consumers derive additional positive affect from free offers.

Research has also shown that in the face of free offers consumers are less sensitive to

other negative information about the product compared to economically equivalent monetary

promotions (Chandran and Morwitz 2006). The authors argued this occurs because whereas

monetary discounts tend to be integrated with the stated price of the product, free promotions

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tend to be processed independent of price (Diamond and Sanyal 1990; Nunes and Park 2003),

making free promotions more salient (Wilson et al. 2000) than monetary discounts when

consumers make decisions. The salience of the free promotion then lowers consumers’

sensitivity to negative information about the product, such as its quality (Chandran and Morwitz

2006). For example, Chandran and Morwitz (2006) demonstrated that when consumers were

offered a textbook for $23.00 with free shipping, their purchase intentions were unaffected by

whether the bookstore had positive or negative ratings. On the other hand, when consumers were

offered the same $23.00 textbook with a $2.99 discount but a charge of $2.99 for shipping, they

were highly sensitive to information about the quality of the bookstore.

Although free promotions have a positive impact on choice and purchase intentions, we

do not know how consumers respond to pseudo free offers. Pseudo free offers differ from free

offers in one very important way—they involve costs. The cost of a pseudo free offer may be in

the form of time (e.g., “free” internet if you watch a 5 minute promotional video), effort (e.g.,

receive a “free” t-shirt if you complete a 10 page survey), annoyance (e.g., receive a “free” music

streaming service if you accept periodic commercial breaks), personal information (e.g., use

Facebook for “free” if you hand over a wealth of personal information), privacy (e.g., “free”

email from Google if you allow Google to scan your emails), and even money (e.g., “free”

internet access with a purchase of $5 or more), to name a few. Although the costs of pseudo free

offers can vary widely in terms of magnitude and form, all pseudo free offers involve a cost

component. Because of this cost component, consumers should respond differently to pseudo

free offers than truly free offers, weighing the costs against the benefits of the offer.

However, we do not expect this to be the case. Because free offers are highly salient

(Wilson et al. 2000) and attractive (Chandran and Morwitz 2006; Shampanier et al. 2007), we

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expect consumers to respond to pseudo free offers in a way that is similar to their response to

free offers, preferring the pseudo free offer over an offer that is explicitly non-free. Thus:

H1: Consumers will be more likely to accept an offer when it is framed as “free,” but has

a clear cost, than when the offer is explicitly non-free.

Observation of everyday behavior seems to support this hypothesis. Even though

consumers are aware that using Facebook, Gmail, and other free services involve sacrificing

some measure of privacy and personal information (Diamond 2014; Kelly 2014), people

continue to use these “free” services despite the costs (Covert 2014; Protalinski 2014). This

behavior would be rational if the benefit of the “free” aspect of these offers (and other pseudo

free offers) was greater than the monetary or non-monetary cost of the offers. However, given

the attractiveness of free offers (Chandran and Morwitz 2006; Shampanier et al. 2007), we

hypothesize that consumers will respond to pseudo free offers in a way that is similar to free

offers even when such behavior is irrational. Stated formally, we expect that:

H2: Consumers will accept pseudo free offers even when the benefit of the “free” aspect

of the offer is less than the cost of the offer.

However, we do not expect that consumers will always respond positively to pseudo free

offers. Consumers may generate different attributions regarding why firms make pseudo free

offers. The attributions people make can have a large impact on their behavior (e.g., Heider

1958; Jones and Davis 1965; Kelley 1967; Weiner 1985), perceptions, (Ross 1977), motivations

and goals (Weiner 1985), and well being (Taylor and Brown 1988). Consumers’ attributions

regarding products similarly impact their behavior (e.g., Folkes 1984; Morales 2005; Tsiros,

Mittal, and Ross 2004; Weiner 2000). Since free offers are highly attractive (Chandran and

Morwitz 2006; Shampanier et al. 2007), we expect consumers to generally be inclined to

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spontaneously generate positive, trusting attributions regarding pseudo free offers, which leads

them to respond to the offers as if they are truly free. However, in cases when consumers do not

make these positive attributions, instead generating suspicious or negative attributions, we expect

them to behave differently, becoming less likely to accept the offer. Accordingly, we expect that

those who are dispositionally suspicious or who make negative attributions about the pseudo free

offer will not respond to it in a way that is similar to free offers. Thus:

H3: Consumers will significantly prefer the free offer to the pseudo free offer when they

make negative attributions about the pseudo free offer.

We next test these hypotheses in seven studies.

STUDY 1A

The goal of Study 1A was to test hypothesis 1 and determine whether consumers respond

more positively to pseudo free offers--that have a clear non-monetary cost--than to explicitly

non-free offers that have a monetary cost. If they do, it could be simply because the cost aspect

of pseudo-free offers is not salient to consumers. To rule this out we examined whether

consumers’ preference for the pseudo free offer (versus the non-free offer) would be reduced

when the cost of the offer was made salient.

Method

Participants and design. One hundred and fifty Amazon Mechanical Turk workers

(35.3% female, MAge = 32.54, SDAge = 10.04) participated in this study in exchange for $0.26.

Participants were randomly assigned to one of five conditions (free condition, pseudo free

condition, pseudo free with free salient condition, pseudo free with cost salient condition, or non-

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free condition). No outliers were removed from this (or any other) study.

Procedure. After consenting to participate, participants were asked to read and imagine

that they were at an airport waiting for a flight, that they had used up their data for this month,

but that they would love to check their Facebook and Twitter. In the free condition, participants

then read, “As you are waiting at your gate, you see a sign that says, ‘Free Wi-Fi.’ All you need

to do is click the standard accept terms statement.” In the pseudo free condition, the sign instead

said, “Free Wi-Fi if you download the airport’s app.” Thus, consistent with our definition of

pseudo free offers, the Wi-Fi was framed as free, but involved a clearly specified non-monetary

cost (i.e., being forced to download the airport’s app). In the pseudo free with free salient

condition, everything was the same as in the pseudo free condition, except that “Free Wi-Fi” was

bolded and increased to 16 point font (the rest of the sign, and the other signs (unless otherwise

indicated), were in 10 point font; see the web appendix for the signs). In the pseudo free with

cost salient condition, the “download the airport’s app” part of the sign was bolded and increased

to 16 point font. In the non-free condition, the sign said, “Wi-Fi for $3.50.” This price was based

on a pretest where MTurk workers indicated they expect, on average, to pay $3.50 for internet

access at an airport (see web appendix for details about the pretest). All participants were then

asked, “How likely are you to accept the terms and use the airport’s Wi-Fi?” on a seven point

scale with 1 = Not at all and 7 = Extremely. Participants then completed some demographic

questions, including gender and age (see the web appendix for all of the questions asked in this

study and all subsequent studies). They then were debriefed and thanked for their participation.

Results and Discussion

As hypothesized, a one-way ANOVA on participant’s likelihood to accept the terms and

use the internet revealed a significant effect of condition (F(4, 146) = 15.76, p < .001). Tukey’s

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HSD post-hoc tests revealed that participants in the free condition (M = 5.71, SD = 1.33), pseudo

free condition (M = 5.19, SD = 1.68), pseudo free with free salient condition (M = 5.23, SD =

1.94), and pseudo free with cost salient condition (M = 5.07, SD = 1.98) were significantly more

likely to accept the terms than participants in the non-free condition (M = 2.56, SD = 1.76; all

Tukey’s HSD q’s > 18.29, all ps < .001). The likelihood to accept did not significantly vary

across any of the other conditions (all Tukey’s HSD q’s < 3.33, ps > .638). Participants seemed

to respond to the pseudo free offer in a way that was very similar to how they responded to the

truly free offer. As hypothesized, participants were significantly more likely to accept the Wi-Fi

terms when the offer was presented as “free” but had a clear non-monetary cost (downloading

the airport’s app) than when it had a monetary cost. Participants significantly preferred the

pseudo free offer even when the cost component of the offer was highlighted. Thus, the effect

does not seem to be driven by a lack of salience of the cost component of pseudo free offers.

Although these findings suggest that participants do not evaluate non-monetary costs and

monetary costs in the same way, it is important to note that participants’ reactions to the pseudo

free offer in this study would be rational if they do not perceive downloading the airport’s app to

be costly. If the “cost” of downloading the airport’s app is less than $3.50, it would be logical for

consumers to be more likely to accept the pseudo free offer than the non-free offer. In fact, some

consumers may perceive there to be no cost associated with downloading the app, particularly if

the app is useful. In this case, it would be perfectly reasonable for consumers to respond to the

pseudo free offer in essentially the same way that they respond to the free offer. Alternatively,

consumers may realize there is a cost associated with the pseudo free offer, but they may find it

difficult to quantify a non-monetary cost and compare it to the value of the offering. We address

these possibilities in later studies, and demonstrate that consumers’ propensity to accept pseudo

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free offers is—at least in some cases—irrational.

STUDY 1B

Study 1B was conducted as a conceptual replication of Study 1A that again tests

hypothesis 1 but that rules out two other possible causes for the effect. It could be that

participants reacted positively to the pseudo free offer in Study 1A because the costs were non-

monetary and therefore difficult to assess. Participants may also have reacted positively because

they just did not think about the company’s motives for offering the deal. Study 1B is designed

to also test these two possibilities.

Method

Participants and design. Two hundred and forty-one Amazon Mechanical Turk workers


Participants were randomly assigned to one of eight conditions in a 4 (offer: free, pseudo free

monetary cost, pseudo free non-monetary cost, non-free) x 2 (thoughts about motives: absent,

present) between subjects design.

Procedure. After consenting to participate, participants read and were asked to imagine

that they were in a coffee shop where they planned to hang out for a while. They were told that

they really want to check their Facebook and Twitter, but that they had used up their data for the

month. Those in the free condition were told that they see a sign that says, “Free Wi-Fi. All you

need to do is click the standard accept terms.” In the pseudo free monetary cost condition the

sign said, “Free Wi-Fi with any purchase of $3 or more.” In the pseudo free non-monetary cost

condition the sign said, “Free Wi-Fi if you fill out a short customer satisfaction survey.” Finally,

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in the non-free condition the sign said, “Wi-Fi for $3.” Participants in the thoughts about motives

present conditions then read, “As you are deciding whether to accept or reject the offer, you

think about why the coffee shop is offering this Wi-Fi deal. Specifically, you think about the

coffee shop’s motives behind the offer. Please take a moment to think about the potential reasons

why the coffee shop has these Wi-Fi terms and is offering this Wi-Fi deal.” Participants in the

thoughts about motives absent conditions did not read these instructions. All participants were

then asked, “How likely are you to accept the terms and use the coffee shop’s Wi-Fi?” on a

seven point scale with 1 = Not at all and 7 = Extremely. Participants then completed

demographic questions, before being debriefed and thanked for their participation.


A 4 (condition) x 2 (thoughts about motives) ANOVA on likelihood to accept the terms

and use the coffee shop’s Wi-Fi revealed no main effect of thoughts about motives (F(1, 233) =

.07, p = .800) and no interaction between offer and thoughts about motives (F(3, 233) = 1.89, p =

.132). Thus, whether participants explicitly considered the coffee shop’s motives for the Wi-Fi

offer or not did not impact how consumers responded to the offer. As predicted, there was a

significant main effect of offer (F(3, 233) = 56.09, p < .001). Specifically, Tukey’s HSD post-

hoc analyses revealed that participants in the free condition (M = 5.98, SD = 1.35), pseudo free

monetary cost condition (M = 5.47, SD = 1.57), and pseudo free non-monetary cost condition (M

= 5.21, SD = 1.70) were significantly more likely to accept the coffee shop’s Wi-Fi terms than

those in the non-free condition (M = 2.59, SD = 1.61; all Tukey’s HSD q’s > 26.09, all ps <

.001). The only other significant difference was between the free and pseudo free non-monetary

cost condition (Tukey’s HSD q = 7.64, p = .037). Because this difference is not replicated in any

of our other studies, it should be interpreted with caution.

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Replicating study 1A, consumers presented with an offer framed as “free,” but with a

clear non-monetary cost (completing a customer satisfaction survey), were significantly more

likely to accept the offer than when it had a monetary cost. Moreover, they responded to the

pseudo free with monetary cost offer in way that was similar to how they responded to the free

offer. These results also show that the pseudo free effect can manifest when the costs are

monetary (a purchase of $3 or more). Participants were again more likely to accept the pseudo

free offer than the purely monetary cost offer, and responded to it as if it was completely free.

We also found that thinking about the coffee shop’s motives for the offer did not moderate the

effect, suggesting that the reason consumers prefer pseudo free to non-free offers is not because

they fail to consider the company’s motives behind the offer. The results also seem to provide

some initial evidence that consumers spontaneously make positive attributions in response to

pseudo free offers, since thinking about motives did not reduce the effect.

In this study we matched the costs of the pseudo free (a purchase of at least $3) and the

non-free monetary offers (the Wi-Fi cost was also $3). Despite this, participants were still more

likely to accept the pseudo free offer. These results are consistent with our conjecture that

consumers may behave irrationally when presented with pseudo free offers, being more likely to

accept it than an offer with an equal monetary cost. However, this study did not perfectly control

for the costs and benefits of the offers, because participants in the pseudo free monetary cost

condition received value from both the Wi-Fi and the goods that they purchased for $3, whereas

those in the non-free condition only received the Wi-Fi for $3. The next two studies are designed

to provide stronger evidence that consumers are irrational in the presence of pseudo free offers.

STUDY 2

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Study 2 was designed to test the robustness of the previous findings and to test hypothesis

1, using a different scenario and dependent variable. Specifically, study 2 tests whether

consumers will be more likely to accept a pseudo free offer than an offer with a monetary cost by

asking consumers to make a choice between two music streaming apps.

The paradigm used in this study was similar to that used by Shampanier et al. (2007). In

each condition, participants were presented with a choice between a lower cost, lower quality

music streaming app and a more expensive, higher quality music streaming app. The only

manipulated difference between the conditions was the price of the apps in the choice set. In

Condition 1, the lower quality app was priced at $0.50, and the higher quality app was priced at

$3.50. In Condition 2, the prices for both apps were dropped by $0.50. Thus the lower quality

app was framed as pseudo free and the higher quality app was $3.00. Because the difference in

cost between the two apps and the benefits (low quality vs. high quality) remain constant across

conditions, as Shampanier et al. (2007) argue, if pseudo-free promotions are not over-valued, the

choice shares for the two apps should be similar across the two conditions. In Condition 3, the

low quality app was again framed as pseudo free (a $0.50 decrease in price from Condition 1)

and the high quality app was offered for $2.00 (a $1.50 decrease in price from Condition 1).

Because the higher quality app is offered for a better price (which means the difference in cost

between the apps is decreased in this condition vs. Condition 1), and the benefits (low quality vs.

high quality) are constant across the conditions, standard economic theory would predict that the

choice share for the high quality app in Condition 3 should be greater than in Condition 1 (and

the choice share for the low quality app should be lower in Condition 3 than 1). However, we

predict that, similar to the findings in Shampanier et al. (2007), people will over-value pseudo

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free offers. Specifically, we posit that when the price of the low quality app is dropped to pseudo

free, its choice share (vs. the high quality app) will increase, even when such behavior is

inconsistent with common economic theory. In this study we will also test whether positive

affect explains consumers’ response to the pseudo free music streaming app.

Method

Participants and design. One hundred and five undergraduate business students at a large

northeastern U.S. university (44.8% female, MAge = 20.18, SDAge = 1.07) participated in this

study in exchange for partial course credit. Participants were randomly assigned to one of three

conditions (Condition 1: $0.50 & $3.50, Condition 2: pseudo free & $3.00, or Condition 3:

pseudo free & $2.00).

Procedure. After consenting to participate, participants were asked to imagine that they

were deciding between two versions of a music streaming app. They then were randomly

presented with one of three choice sets. Participants in Condition 1 ($0.50 & $3.50) were

presented with the following two options: “Version 1 is $0.50, has periodic commercial breaks,

and mediocre sound quality. Version 2 is $3.50, has no commercials, and high sound quality.”

Participants in Condition 2 (pseudo free & $3.00) were presented with the same options, except

that Version 1 was reduced to pseudo free and Version 2 was reduced to $3.00. We consider

Version 1 to be pseudo free because although the price of the app was framed as free, the benefit

of the “free” music streaming app came at the expense of commercial interruptions and low

sound quality. Participants in Condition 3 (pseudo free & $2.00) were presented with the same

options as those in Condition 2, except that the cost of Version 2 was further reduced to $2.00.

Participants then made a choice between the two versions of the music streaming app.

Participants were then asked, “Overall, how happy do you think you will be with your music app

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choice?” on a seven point scale with -3 = Very unhappy and +3 = Very happy. They were then

asked how important high sound quality and a commercial free experience are to them on seven

point scales with 1 = Not at all important and 7 = Very important. Participants then completed

some demographic questions, were debriefed, and thanked for their participation.


When the low quality app costs $0.50 and the high quality app costs $3.50 (Condition 1),

most participants (90.9%) chose the high quality app. However, in Condition 2, when the price

of the low quality app dropped to pseudo free and the cost of the high quality app was dropped to

$3.00, there was a significant preference reversal. The majority (54.3%) of participants now

selected the low quality app (Version 1). Remarkably, even in Condition 3, when the price of the

high quality app was dropped to $2.00, the choice share for the low quality app remained high

(43.2%) and was significantly higher than in Condition 1 (9.1%). The effect of condition on

choice of the app was significant (X2(2, N = 105) = 16.25, p < .001). As shown in Figure 1, post-

hoc Chi-Square tests revealed that significantly more participants selected the lower quality

version (Version 1) of the music streaming app in Condition 2 (pseudo free & $3.00; 54.3%) and

Condition 3 (pseudo free & $2.00; 43.2%) than in Condition 1 ($0.50 & $3.50; 9.1%; p < .001

and p = .001, respectively). The difference between Condition 2 (pseudo free & $3.00) and

Condition 3 (pseudo free & $2.00) was not significant (X2(1, N = 72) = .88, p = .349).

We next conducted the same set of analyses only on those participants who indicated that

high sound quality and a commercial free experience were very important to them, to see if the

same pattern emerges for this group. First, for those who responded with a 6 or 7 to the question

regarding the importance of high sound quality when selecting a music app (N = 66), a Chi-

Square test revealed a significant effect of condition on choice (X2(1, N = 66) = 8.38, p = .015).

19

Post-hoc Chi-Square tests revealed that significantly more participants selected the lower quality

version (Version 1) of the music streaming app in Condition 2 (pseudo free & $3.00; 38.1%) and

Condition 3 (pseudo free & $2.00; 29.6%) than in Condition 1 ($0.50 & $3.50; 0.0%; p = .003

and p = .011, respectively). The difference between Conditions 2 and 3 was not significant (X2(1,

N = 48) = .38, p = .537). Next, for those who responded with a 6 or 7 to the question regarding

the importance of a commercial free experience (N = 44), a Chi-Square test revealed a significant

effect of condition on choice (X2(1, N = 44) = 9.66, p = .008). Post-hoc Chi-Square tests revealed

that significantly more participants selected the lower quality version (Version 1) of the music

streaming app in Condition 2 (pseudo free & $3.00; 46.2%) and Condition 3 (pseudo free &

$2.00; 21.4%) than in Condition 1 ($0.50 & $3.50; 0.0%; p = .002 and p = .045, respectively).

The difference between Conditions 2 and 3was not significant (X2(1, N = 27) = 1.85, p = .173).

This suggests that pseudo free offers are attractive even for those participants for whom low

sound quality and commercials are significant costs.

We next tested whether participants who selected the pseudo free music streaming app

were significantly happier with their selection than participants who selected the non-free, higher

quality app, since Shampanier et al. (2007) found that positive affect explains the overvaluation

of free offers. However, we instead found that participants were significantly happier with their

choice when they selected the higher quality, non-free app (M = 2.15, SD = .86) than the lower

quality app (M = .82, SD = .80; t(103) = 7.84, p < .001). When the analysis was restricted to only

the conditions in which the lower quality app was pseudo free, the same results were found

(MNon-Free = 2.05, SDNon-Free = .82 vs. MPseudoFree = .83, SDPseudoFree = .82; t(70) = 6.35, p < .001).

Thus, positive affect does not seem to explain consumers’ preference for pseudo free offers.

[Insert Figure 1 About Here]

20

Study 2 again showed that consumers are significantly more likely to accept (in this case

choose) an offer when it is presented as “free” with a non-monetary cost (commercial

interruptions and low sound quality) than when it has a monetary cost. Importantly, the findings

from this study provide evidence that consumers’ responses to pseudo free offers can be

irrational. Standard economic theory suggests that consumers make choices based on the costs

and benefits of an option. When Version 1 was offered for $0.50 (and Version 2 offered for

$3.50), only 9.1% of consumers chose Version 1. However, when the cost of Version 1 was

dropped by $0.50 to pseudo free, the choice share of that version increased to 54.3%, even

though the cost of Version 2 also was reduced by $0.50. If participants were rational, their choice

shares for the options should have been roughly equivalent in Conditions 1 and 2. Even more

surprising, 43.2% of participants selected Version 1 when its price had been reduced by only

$0.50 to pseudo free and the price of Version 2 had been reduced by $1.50 to $2.00. In this case,

standard economic theory would argue that consumers’ choice share of Version 1 should have

decreased even below the 9.1% share found in Condition 1, and that Version 2’s share should

have increased above 90.1%. Instead Version 2’s share dropped to 56.8%. Thus, consumers

appear to respond irrationally to pseudo free offers.

However, one limitation of this study is that we do not know for certain whether the

participants considered commercial breaks and low sound quality to be costs. Thus, these

“pseudo free” options may have been perceived as truly free to our participants. If so, this study

would be a replication of the studies reported in Shampanier et al. (2007), rather than a novel

contribution to the literature. This seems unlikely, though, given the extremely small choice

share of Version 1 in Condition 1 ($0.50 & $3.50). If commercial breaks were not costly, one

would expect more than 9% of the participants to select the music app with the lower cost.

21

Further, those who rate high sound quality and a commercial free experience as important should

have been more likely to consider these to be real costs, and the same pattern emerged for these

groups too. Regardless, we continue to address this concern in the next study, where we again

test whether consumers will accept pseudo free offers that are costly and whether they will

irrationally accept pseudo free offers even when their costs exceed their benefits.

STUDY 3

The goal of study 3 was to test hypothesis 2—that consumers will behave irrationally and

will accept pseudo free offers even when the cost of the offer outweighs the benefit.

Method

Participants and design. One hundred and twenty-six Amazon Mechanical Turk workers


Participants were randomly assigned to one of five conditions (free, pseudo free, non-free,

internet willingness to pay (WTP), or app need to pay (NTP)). The free, pseudo free, and non-

free conditions were very similar to those used in earlier studies (studies 1A and B). The internet

willingness to pay (WTP) condition was included to determine the value (the benefit) of the

internet in the scenario, whereas the app need to pay (NTP) condition was included to quantify

the cost of being forced to download the airport’s app (i.e., the cost of the pseudo free offer).

Procedure. After consenting to participate, participants were told to read and imagine

that they were actually in a scenario. The scenario and manipulations used for the free, pseudo

free, and non-free conditions were identical to those used in Study 1A. Participants in these three

conditions were then asked, “How likely are you to accept the terms and use the airport’s Wi-

22

Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely.

Participants in the other two conditions were presented with the same scenario (i.e., that

they were at an airport, had used up their data for the month, but wanted to check their Facebook

and Twitter), but had different dependent variables. In the internet willingness to pay (WTP)

condition, participants were asked, “How much would you be willing to pay to use the airport’s

Wi-Fi?” Participants slid a bar between $0 and $20 to indicate their willingness to pay.

Participants in the app need to pay (NTP) condition were told, “As you are waiting at your gate,

you see a sign that says the airport is trying to get people to download its app.” They were then

asked, “How much would the airport have to pay you in cash or credit toward airport services

(e.g. Wi-Fi) in order for you to be willing to download the airport’s app?”Participants responded

by sliding a bar between $0 and $20 to indicate how much the airport would have to pay them in

order for them to download the app. Participants then completed some demographic questions

and were debriefed and thanked for their participation.


As hypothesized, a one-way ANOVA revealed a significant effect of condition (free,

pseudo free, and non-free) on likelihood to accept the airport’s Wi-Fi terms (F(2, 70) = 25.23, p

< .001). Tukey’s HSD post-hoc analyses revealed that participants in the free (M = 5.88, SD =

1.57) and pseudo free conditions (M = 5.12, SD = 2.28) were significantly more likely to accept

the airport’s Wi-Fi terms and use the internet than participants in the non-free condition (M =

2.29, SD = 1.57; Tukey’s HSD q’s > 13.11, both ps < .001). The difference between the free and

pseudo free conditions was not significant (Tukey’s HSD q = 3.52, p = .330). The results again

indicated that participants were significantly more likely to accept an offer when it is presented

as “free” with a non-monetary cost than when it has a monetary cost.

23

However, more importantly, the goal of study 3 was to demonstrate that even when the

non-monetary cost of the pseudo free offer is costly, consumers may still respond positively to

pseudo free offers. The results demonstrate just that. Participants in the internet WTP condition

said that they would be willing, on average, to pay $2.07 (SD = 1.91) to use the internet.

Participants in the app NTP condition said that they would need to be paid, on average, $5.52

(SD = 3.18) to be willing to download the airport’s app. The amounts participants would be

willing to pay for the internet and need to be paid to download the app were significantly

different (t(51) = 4.80, p < .001) and demonstrate that the non-monetary cost of the pseudo free

offer is costly (valued on average at $5.52). Moreover, these results also demonstrate that

consumers can be irrational when accepting pseudo free offers, since the non-monetary cost of

the offer exceeds the benefit of the “free” aspect of the offer ($2.07benefit of internet – $5.52cost of

downloading app = -$3.45). Furthermore, consumers were more likely to accept the pseudo free offer

than the non-free offer even though the (negative) difference between the benefit and the cost of

the offer was greater for the pseudo free offer ($2.07 benefit of internet -$5.52cost of downloading app = -

$3.45) than for the non-free offer ($2.07 benefit of internet - $3.50price of intent = -$1.43). Given this,

those in the pseudo free condition should have been less likely to accept the offer than those in

the non-free condition. Instead, they responded to the pseudo free offer in a way that was almost

indistinguishable from how participants responded to the truly free offer. This suggests that

participants in the pseudo free condition either simply ignored the costs, or in the face of a free

promotion placed different valuations on the free product and the cost to obtain it, than they

would have without the pseudo free promotion.

Study 3 provided further evidence for the robustness of the pseudo free effect, and

demonstrated that consumers can respond to pseudo free offers in an irrational way. Thus,

24

similar to free offers, there appears to be something special about pseudo free offers that

transcends standard economic thought. Although previous research would suggest that this

“specialness” may be a result of the positive affect that consumers derive from free offers

(Shampanier et al. 2007) or the salience of free offers (Chandran and Morwitz 2006), such that

consumers hardly consider the costs of pseudo free offers, we did not find support for these

explanations in Studies 1A-2. Instead, we propose that consumers’ attributions regarding why

firms provide pseudo free offers drives consumers’ responses to them. We posit that consumers’

initial, spontaneous attributions regarding the pseudo free offers are positive, which leads them

to respond to these offers as if they were truly free, even when that response is irrational (e.g.,

the costs of the offer exceed its benefits). We test this hypothesis, as well as what happens when

consumers make negative attributions regarding pseudo free offers, in the following studies.

STUDY 4

The goal of study 4 was to begin to investigate the process behind the pseudo free effect

and its boundary conditions. Specifically, will this effect always manifest and, if not, what

factors would lead the pseudo free effect to disappear? When are people less likely to accept a

pseudo free offer than a truly free offer? Accordingly, study 4 was designed to test hypothesis

3—that people will be less likely to accept a pseudo free offer than a truly free offer when they

make negative attributions about the pseudo free offer. Thus far, we have argued that free offers

are highly attractive to consumers (Chandran and Morwitz 2006; Shampanier et al. 2007), which

leads consumers to spontaneously generate positive attributions about the offers. As a result, they

respond to the pseudo free offer as if it is truly free. We suggest that only when consumers make

25

negative attributions about the pseudo free offer might the effect be attenuated or reverse. We

first test this by examining whether people who as a trait tend to be suspicious react less

positively to pseudo free offers. Because people high in dispositional suspicion should be likely

to make suspicious, negative attributions regarding the pseudo free offer, we hypothesized that

they would have a lower likelihood of accepting the pseudo free offer than the free offer. On the

other hand, we hypothesized that people low in dispositional suspicion would respond to the

pseudo free offer in a way similar to the free offer, since they should make positive, non-

suspicious attributions regarding the pseudo free offer.

Method

Participants and design. One hundred and forty-eight Amazon Mechanical Turk workers


Participants were randomly assigned to one of six conditions in a 3 (offer: free, pseudo free, non-

free) x 2 (thoughts about motives: absent, present) x measured dispositional suspicion between-

subjects design. Similar to study 1B, the thoughts about motives factor was included to see

whether asking participants to think about the airport’s motives behind the pseudo free offer

would induce participants to make negative attributions regarding the pseudo free offer. If

thinking about the airport’s motives is enough to induce people to make negative attributions,

participants in the thoughts about motives present condition should respond less positively to

pseudo free offers (vs. thought absent). If thinking about the airport’s motives does not induce

people to make negative attributions, then thoughts should not change participants’ responses to

pseudo free offers.

Procedure. After consenting to participate, participants were told to read and imagine

that they were actually in the same scenario as in Study 3 (that involved obtaining Wi-Fi in an

26

airport). Recall that participants in the free condition were told that the sign says, ‘Free Wi-Fi,’

those in the pseudo free condition were told the sign said “Free Wi-Fi if you download the

airport’s app,” and those in the non-free condition were told the sign said, “Wi-Fi for $3.50.”

Participants in the thoughts about motives present conditions were then told, “As you are

deciding whether to accept or reject the offer, you think about why the airport is offering this Wi-

Fi deal. Specifically, you think about the airport’s motives behind the offer. Please take a

moment to think about the potential reasons why the airport has these Wi-Fi terms and is offering

this Wi-Fi deal.” Participants in the thoughts about motives absent conditions did not receive

these instructions. All participants then responded to the dependent variable, “How likely are you

to accept the terms and use the airport’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7

= Extremely.

Participants then completed a dispositional suspicion scale (McKnight, Kacmar, and

Choudhury 2004). The nine item scale contained statements such as, “People are usually out for

their own good,” “Most people would tell a lie if they could gain by it,” and “I usually trust

people until they give me a reason not to trust them” (reverse-coded), all measured on seven

point scales with 1 = Strongly disagree and 7 = Strongly agree. The complete set of items is

included in the web appendix, and the scale had acceptable reliability (α = .86). After answering

these questions, participants completed some demographic questions and were then debriefed

and thanked for their participation.


To analyze the data, hierarchical regressions were conducted with likelihood to accept the

airport’s Wi-Fi offer as the dependent variable (Aiken and West 1991). The main effects of

dispositional suspicion (mean-centered), offer (represented by two dummy variables: first offer

27

dummy (1 = free, 0 = pseudo free, 0 = non-free) and second offer dummy (0 = free, 1 = pseudo

free, 0 = non-free), and thoughts about motives (1 = present, 0 = absent) were entered

simultaneously in step 1. The five two-way interactions between dispositional suspicion (mean-

centered), the two dummy variables representing the offer conditions, and the dummy variable

representing the thoughts about motives conditions were entered simultaneously in step 2. The

two three-way interactions between dispositional suspicion (mean-centered), the two dummy

variables representing the offer conditions, and the dummy variable representing the thoughts

about motives conditions were entered simultaneously in step 3. There was no significant three-

way interaction, ∆R2 = .003, F(2, 136) = .29, p = .746.

We next tested the two-way interactions. The two-way interactions between thoughts

about motives and dispositional suspicion (∆R2 = .014, F(1, 144) = 2.07, p = .152) and thoughts

about motives and offer (F(2, 142) = .02, p = .976) were not significant. However, as predicted,

the two-way interaction between dispositional suspicion and offer was significant (∆R2 = .031,

F(2, 142) = 3.32, p = .039). This analysis revealed a significant offer by dispositional suspicion

interaction when the pseudo free and free (0 = pseudo free, 1 = free) conditions were analyzed (B

= .86, SE = .34, t = 2.58, p = .011), but no significant offer by dispositional suspicion interaction

when the pseudo free and non-free (0 = pseudo free, 1 = non-free) conditions (B = .44, SE = .36,

t = 1.24, p = .219) and non-free and free (non-free = 0, free = 1) conditions (B = .419, SE = .34, t

= 1.22, p = .223) were analyzed. Accordingly, regardless of level of dispositional suspicion,

people respond to free and non-free offers in the same way. Only when pseudo free offers are

involved do people with different levels of dispositional suspicion respond to the offer

differently.

To further decompose the significant omnibus offer by dispositional suspicion

28

interaction, we tested our main hypothesis that people low in dispositional suspicion will respond

to the pseudo free offer as if it is truly free, whereas people high in dispositional suspicion will

be more suspicious of the pseudo free offer and therefore react to it more negatively than they do

for the free offer. Thus, we conducted spotlight analyses comparing likelihood to accept the

airport’s free, pseudo free, and non-free Wi-Fi offers for individuals low in dispositional

suspicion (1 SD below the mean (3.93) of dispositional suspicion) and individuals high in

dispositional suspicion (1 SD above the mean (3.93) of dispositional suspicion) (Spiller,

Fitzsimons, Lynch, and McClelland 2013). As shown in Figure 2, these spotlight analyses

revealed that people low in dispositional suspicion respond to the pseudo free offer in a way

similar to the free offer. Individuals low in dispositional suspicion were significantly more likely

to accept the free offer (B = 2.20, SE = .50, t = 4.36, p < .001) and the pseudo free offer (B =

2.71, SE = .54, t = 5.04, p < .001) than the non-free offer. However, there was no significant

difference between their likelihood to accept the free and pseudo free offers (B = -.52, SE = .52, t

= -.99, p = .324). On the other hand, individuals high in dispositional suspicion did not respond

to the pseudo free offer as if it was truly free. Although individuals high in dispositional

suspicion were significantly more likely to accept the free (B = 3.09, SE = .52, t = 5.93, p < .001)

and pseudo free (B = 1.76, SE = .53, t = 3.36, p = .001) offers than the non-free offer, they were

also significantly more likely to accept the free offer than the pseudo free offer (B = 1.33, SE =

.51, t = 2.60, p = .010).

[Insert Figure 2 about Here]

Study 4 demonstrated that individuals high in dispositional suspicion react less positively

to pseudo free offers. Whereas those low in dispositional suspicion likely make positive, non-

suspicious attributions regarding pseudo free offers—leading them to respond positively to

29

them—those high in dispositional suspicion likely make less positive, more suspicions

attributions—leading them to respond less positively.

Interestingly, replicating study 1B, explicitly asking participants to consider the airport’s

motives for offering the Wi-Fi deal had no significant effect on their likelihood to accept the Wi-

Fi offer, and the thoughts about motives manipulation did not interact with dispositional

suspicion. This suggests that consumers’ propensity to generate positive attributions in response

to pseudo free offers (if they are low in dispositional suspicion) continues even in the face of

such subtle inducements to consider the costs of the offer. In addition, highly suspicious

consumers’ propensity to make negative attributions regarding pseudo free offers is not affected

by prompting them to consider the airport’s motives. However, to this point, we have not

measured the attributions consumers make in response to pseudo free offers. We do just that in

study 5, as well as formally test whether the attributions consumers make regarding pseudo free

offers mediate their response to them.

STUDY 5

The goal of study 5 was to more directly test hypothesis 3—that consumers will respond

more negatively to pseudo free offers if they make negative attributions about the pseudo free

offer. Specifically, we wanted to test whether the attributions consumers make about offers

mediate their response to them. As the findings from study 4 suggest, consumers likely make

different attributions regarding pseudo free offers. Our previous findings suggest that the

majority of participants make positive attributions about the pseudo free offer—for example, that

it is intended to benefit consumers—which leads them to respond as positively to the offer as if it

30

is truly free. However, some participants, such as those who are naturally suspicious, may make

negative attributions about the pseudo free offer—for example, that it is intended to take

advantage of the consumer. We predict that those who make more negative attributions regarding

the pseudo free offer will be less likely to accept the pseudo free offer than the free offer.

Method

Participants and design. One hundred and sixty-four Amazon Mechanical Turk workers


Participants were randomly assigned to one of three conditions (free, pseudo free, or non-free).

Procedure. After consenting to participate, participants were told that they were about to

read a scenario, and were asked to imagine that they were actually in the scenario. The scenario

used was identical to that used in Studies 3 and 4. After reading the scenario and learning about

the Wi-Fi sign for their condition and responding to the dependent variable, participants were

asked five questions about the attributions they made about the airport’s Wi-Fi offer. They were

asked to indicate their level of agreement (on a seven point scale with -3 = Strongly disagree and

+3 = Strongly agree, which was rescaled to a 1 to 7 scale for analyses) with the following

statements: “The airport had this Wi-Fi deal so that travelers have a pleasant experience at the

airport,” “The airport has this Wi-Fi deal to help travelers get a good deal,” “The airport had this

Wi-Fi deal to increase its profits” (reverse coded), and “The airport has this Wi-Fi deal to

monitor the activity of travelers” (reverse coded). Participants were also asked, “Is this Wi-Fi

deal better for the consumer or the airport?” on a seven point scale with 1 = Definitely better for

the airport and 7 = Definitely better for the consumer. These items were combined to form an

attributions scale (α = .66), with higher scores indicating more positive attributions.

After answering these questions, participants completed some demographic questions and

31

were then debriefed and thanked for their participation.


Replicating our previous findings, a one-way ANOVA revealed a significant effect of

condition (F(2, 161) = 38.40, p < .001). Tukey’s HSD post-hoc analyses revealed that

participants in the free (M = 5.78, SD = 1.44) and pseudo free (M = 5.50, SD = 1.76) conditions

were significantly more likely to accept the airport’s Wi-Fi terms and use the internet than

participants in the non-free (M = 3.17, SD = 1.91; Tukey’s HSD q’s > 18.56, both ps < .001)

condition. The difference between the free and pseudo free conditions was not significant

(Tukey’s HSD q = 2.20, p = .664).

We next conducted a one-way ANOVA that revealed a significant effect of condition on

attributions (F(2, 161) = 21.61, p < .001). Participants in the free condition (M = 4.50, SD =

1.23) made significantly more positive attributions regarding the Wi-Fi offer than participants in

the pseudo free (M = 3.90, SD = .93; Tukey’s HSD q = 7.55, p = .007) and non-free (M = 3.22,

SD = .87; Tukey’s HSD q= 16.01, p < .001) conditions. Participants in the pseudo free condition

also made significantly more positive attributions than those in the non-free condition (Tukey’s

HSD q = 8.47, p = .003).

Next, to test whether the attributions consumers make regarding the offer mediate their

response to the offer, we conducted regression analyses and a test of mediation (Hayes and

Preacher 2014). We first conducted regression analyses to determine the paths between the

independent variable (condition), mediator (attributions), and dependent variable (likelihood to

accept the airport’s Wi-Fi offer). Specifically, we did this twice, once to determine the paths

when comparing the free and pseudo free conditions, and a second time to determine the paths

when comparing the pseudo free and non-free conditions. Comparing free to pseudo free, as

32

shown in Figure 3A, the path from condition (free = 0, pseudo free = 1) to likelihood to accept

the airport’s Wi-Fi offer is nonsignificant (B = -.28, SE = .30, t = -.92, p = .359). However, the

paths from condition (free = 0, pseudo free = 1) to attributions (B = -.61, SE = .21, t = -2.90, p =

.005) and from attributions to likelihood to accept the airport’s Wi-Fi offer (B = .53, SE = .13, t

= 4.23, p < .001) are both significant. When attributions are included in the model, the

significance of the path from condition (free = 0, pseudo free = 1) to likelihood to accept the

airport’s Wi-Fi offer drops (though it was nonsignificant in both cases) (B = .04, SE = .29, t =

.15, p = .885), whereas the effect of attributions on likelihood to use the airport’s Wi-Fi remains

significant (B = .53, SE = .13, t = 4.10, p < .001).

Comparing pseudo free to non-free, as shown in Figure 3B, the path from condition

(pseudo free = 0, non-free = 1) to likelihood to accept the airport’s Wi-Fi offer is significant (B =

-2.33, SE = .36, t = -6.51, p < .001). Similarly, the paths from condition (pseudo free = 0, non-

free = 1) to attributions (B = -.68, SE = .18, t = -3.86, p < .001) and from attributions to

likelihood to accept the airport’s Wi-Fi offer (B = 1.25, SE = .19, t = 6.69, p < .001) are both

significant. When attributions are included in the model, the path from condition (pseudo free =

0, non-free = 1) to likelihood to accept the airport’s Wi-Fi offer is weakened but remains

significant (B = -1.70, SE = .34, t = -4.97, p < .001), and the effect of attributions on likelihood

to use the airport’s Wi-Fi remains significant (B = .93, SE = .18, t = 5.18, p < .001).

[Insert Figure 3A and Figure 3B About Here]

We then conducted a test of multilevel categorical variable indirect effects (Hayes and

Preacher 2014). Using the MEDIATE macro for SPSS (Hayes and Preacher 2014), we conducted

the test based on dummy coding (comparing the free to pseudo free and pseudo free to non-free

conditions) using a bootstrap sample n = 10,000. Mediation analysis revealed an omnibus effect

33

of condition on likelihood to use the airport’s Wi-Fi through attributions made about the offer, B

= .14, CI (95%) = [.06, .27]. When a bootstrap confidence interval for the omnibus test does not

contain zero, one has evidence of mediation (Hayes and Preacher 2014). However, omnibus

indirect effects can also be broken down by comparing specific levels of the independent

variable. These tests revealed that the differences between the free and pseudo free conditions (B

= .42, CI (95%) = [.13, .84]) and pseudo free and non-free conditions (B = -.47, CI (95%) = [-

.80, -.23]) on the likelihood to use the airport’s Wi-Fi were both mediated by the attributions

consumers made regarding the airport’s Wi-Fi offer.

This study revealed that how consumers respond to offers is driven by the attributions

they make regarding the offer. Specifically, although the differences between free and pseudo

free in our studies have been small, consumers do not treat these offers as exactly the same.

Because consumers make slightly more negative attributions regarding pseudo free than free

offers, they are slightly less willing to accept pseudo free than free offers. However, because the

difference between willingness to accept the free and the pseudo free offer has been

nonsignificant—in the aggregate—in almost all of our studies, it is likely that the majority of

participants make positive attributions regarding pseudo free offers, which leads them to respond

to them as if they are truly free. Thus, a relatively small percent of participants are responsible

for the difference, such as those who are high in dispositional suspicion (study 4). However, one

limitation of this study is that we did not use participants’ own, self-generated attributions as our

measure of their attributions. We therefore ran another study that did just that, and the results

replicated (see Study 5B in the web appendix for all of the study details).

In our final study, we more directly test whether attributions explain the difference

between how consumers respond to free and pseudo free offers by manipulating the attributions

34

they make. Accordingly, we now test process through moderation of the mediator, and even

more directly test H3—that consumers will not respond to pseudo free offers as if they are truly

free when they make negative attributions about the pseudo free offer.

STUDY 6

Study 5 provided evidence that the attributions consumers make regarding offers mediate

their response to them. To further test this, in this study we manipulate the proposed mediator,

that is, the attributions consumers made regarding the pseudo free offer. We hypothesized that

when consumers make positive attributions regarding the pseudo free offer—for example, that it

is intended to benefit the consumer—they respond to it as if it is truly free. However, when

consumers make negative attributions regarding the pseudo free offer—for example, that it is

intended to take advantage of the consumer—they do not respond to the pseudo free offer as if it

is truly free, and they are less likely to accept the pseudo free offer (hypothesis 3). We also tested

whether participants’ initial, spontaneous attributions regarding the pseudo free offer are

positive, which would help explain the results from the previous studies.

Method

Participants and design. One hundred and fifty-two Amazon Mechanical Turk workers


Participants were randomly assigned to one of five conditions (free, pseudo free, pseudo free

with positive attributions, pseudo free with negative attributions, or non-free).


that they were actually in the scenario. The scenario used for the free, pseudo free, and non-free

35

conditions was identical to those used in Studies 1A and 3-5 (i.e., Wi-Fi in an airport).

Participants in the pseudo free with positive attributions and pseudo free with negative

attributions conditions read the same scenario as those in the pseudo free condition, except for

the addition of the attributions manipulation. Participants in the pseudo free with positive

attributions condition read, “As you are looking at the sign, an airport employee tells you that the

airport wants travelers to download the app so that it can easily communicate important

information to them. In addition, he tells you that it allows the airport’s restaurants and shops to

inform travelers about special deals.” Participants in the pseudo free with negative attributions

condition read, “As you are looking at the sign, an airport employee tells you that the airport

wants travelers to download the app so that it can easily track them and monitor their behavior.

In addition, he tells you that it allows the airport to gather data about the travelers that can be

sold to advertisers so they can target advertisements to specific consumers.”

All participants were then asked, “How likely are you to accept the terms and use the

airport’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely. As a

manipulation check for the pseudo free conditions, participants were asked, “When thinking

about your likelihood of accepting the airport’s offer, how suspicious were you of the airport’s

motives behind the Wi-Fi offer?” with 1 = Not at all suspicious and 7 = Extremely suspicious.

Participants then responded to demographic questions before being debriefed and thanked.


First, a one-way ANOVA revealed the attributions manipulation was successful (F(4,

147) = 15.56, p < .001). Planned contrasts with a Bonferroni correction revealed that participants

in the pseudo free with negative attributions condition (M = 5.69, SD = 1.67) made significantly

more negative attributions regarding the pseudo free offer than those in the pseudo free with

36

positive attributions condition (M = 4.17, SD = 2.04; F(1, 147) = 11.17, p = .004). Participants in

the pseudo free with positive attributions and pseudo free (M = 3.38, SD = 1.70) conditions were

similarly suspicious (F(1, 147) = 3.03, p = .263), whereas participants in the pseudo free with

negative attributions condition were significantly more suspicious than those in the pseudo free

condition (F(1, 147) = 26.68, p < .001).

Second, as shown in Figure 4, a one-way ANOVA revealed a significant effect of

condition on likelihood to accept the airport’s Wi-Fi offer (F(4, 147) = 11.56, p < .001).

Replicating the previous findings, Tukey’s HSD post-hoc tests revealed that participants in the

free (M = 5.45, SD = 1.74) and pseudo free conditions (M = 4.88, SD = 1.83) were significantly

more likely to accept the airport’s Wi-Fi offer than participants in the non-free (M = 3.14, SD =

2.05; Tukey’s HSD q’s > 11.13, both ps < .006) condition. The difference between the free and

pseudo free conditions was not significant (Tukey’s HSD q = 3.65, p = .774).

Importantly, participants in the pseudo free with positive attributions condition (M =

4.83, SD = 1.95) were just as likely to accept the airport’s Wi-Fi offer as those in the free

(Tukey’s HSD q = 3.97, p = .737) and pseudo free (Tukey’s HSD q = .32, p = 1.00) conditions,

but were significantly more likely to accept it than those in the non-free condition (Tukey’s HSD

q = 10.81, p = .008). In contrast, participants in the pseudo free with negative attributions

condition (M = 2.75, SD = 2.05) were significantly less likely to accept the airport’s Wi-Fi offer

than those in the free, pseudo free, and pseudo free with positive attributions conditions (Tukey’s

HSD q’s > 13.30, all ps < .001). There was no significant difference between the pseudo free

with negative attributions and non-free conditions (Tukey’s HSD q = 2.49, p = .935).

[Insert Figure 4 About Here]

The results of Study 6 demonstrate that consumers tend to naturally make positive

37

attributions regarding pseudo free offers, which leads them to respond to them as if they are truly

free. However, in support of hypothesis 3, if consumers do not make positive attributions, and

instead make negative attributions regarding the pseudo free offer, their response to the offer

becomes much more negative, and they are significantly less likely to accept the pseudo free

offer than the free offer. Consequently, this study provides compelling evidence that the

attributions consumers make regarding pseudo free offers determine how they respond to them.

GENERAL DISCUSSION

To our knowledge, this is the first paper to examine how consumers respond to pseudo

free offers. Such research is important, given the increasing prevalence of pseudo free offers

(Anderson 2008). We find that consumers respond to pseudo free offers in a way that is similar

to how they respond to truly free offers (Chandran and Morwitz 2006; Shampanier et al. 2007),

and they seem to largely ignore the costs of the offer, unless they are suspicious about why the

firm made the offer and they make negative attributions regarding the pseudo free offer.

Specifically, when presented with an offer that is framed as “free” but has a clear cost—such as

being forced download an airport’s app (studies 1A, 3-6), pay $3 for coffee shop goods or

complete a customer satisfaction survey (study 1B), or expose oneself to commercial breaks and

mediocre sound quality (study 2)—consumers are significantly more likely to accept the offer

than when it is presented as non-free, with a monetary cost (hypothesis 1).

Moreover, consumers can be irrational in the presence of pseudo free offers. Even when

the cost of the pseudo free offer exceeds the value of the offer, consumers indicate that they are

willing to accept the offer (hypothesis 2). In fact, even when the (negative) difference between

38

the costs and benefits of the pseudo free offer exceed the costs and benefits of a non-free offer,

consumers are significantly more likely to accept the pseudo free than the non-free offer (study

3). This may help explain why people continue to use services such as Facebook even when the

benefits of the “free” service may be less than the costs—such as loss of privacy, exposure to

targeted advertisements, and emotional manipulation (BBC 2014)—for some consumers.

However, consumers only respond to pseudo free offers as if they are truly free if they

make positive attributions regarding the pseudo free offer (studies 4-6). Although most

consumers’ default, spontaneous attributions regarding pseudo free offers appear to be positive,

consumers who are dispositionally suspicious (study 4) or induced to make negative attributions

regarding the pseudo free offer (study 6) do not make positive attributions about pseudo free

offers. When consumers make negative attributions regarding the pseudo free offer, they do not

respond to it as if it is truly free. Instead, they are significantly more likely to accept the free

offer than the pseudo free offer.

Theoretical and Practical Implications

There are many theoretical and practical implications of the present research. First, our

research contributes to the literature regarding free as a special price. Consistent with Chandran

and Morwitz (2006) and Shampanier et al. (2007), we find that consumers are overly willing to

accept “free” offers, even to the point of being irrational. Moreover, we contribute to this

literature by demonstrating that free as a special price can be expanded. Even when the offer

clearly has costs, simply presenting the offer as “free” causes consumers to respond to the offer

as if it were truly free, as long as they make positive attributions about the pseudo free offer.

Accordingly, pseudo free (with positive attributions) appears to be as special a price as truly free.

Second, our finding that the attributions consumers make regarding pseudo free offers

39

determine their response to them may also apply to free offers. If consumers believe that a

company is using a truly free offer in order to persuade them to spend more money or buy more

goods, consumers are unlikely to respond as positively to the offer. Future research should

determine whether negative attributions regarding truly free offers eliminate the positive affect

that is usually derived from them and can perhaps help explain the mixed results in the literature

concerning consumers’ reactions to (truly) free offerings.

Third, our research has clear marketing implications for firms who use or who are

considering using such promotions. Our results clearly demonstrate that marketers would be

well-advised to find a way to frame their products and service offers to consumers as “free,”

since consumers will be significantly more likely to accept such pseudo free offers, despite their

having a clear non-monetary cost, than when it is presented as non-free—as long as they make

positive attributions regarding the offer. In fact, consumers are more likely to accept the pseudo

free offer even when the non-monetary cost of the pseudo free offer exceeds the monetary cost of

the non-free offer. However, in all of our studies, the consumers were explicitly informed of the

costs of the pseudo free offers. It is unlikely that consumers will be amenable to pseudo free

offers with costs that become apparent only after they have agreed to the offer.

Furthermore, if a company is planning a free promotion, they may get the same positive

results through a pseudo free promotion instead. For example, if Starbucks is planning a free

coffee day, they should ask consumers to join a mailing list, like them on Facebook, or follow

them on Twitter to receive the free coffee. Such a promotion is likely to be just as successful as a

truly free promotion, while also allowing Starbucks to gain valuable data about their customers.

Similarly, companies often try to persuade consumers to do something for them—such as

like them on Facebook, follow them on Twitter, or write a review on Yelp (Vega 2012). Our

40

results suggest that companies should offer consumers “free” perks in exchange for carrying out

these tasks. Even when the benefit of the “free” offer is significantly less than the cost of the

offer, consumers may be willing to accept the offer. Thus companies are unlikely to have to offer

consumers a substantial benefit, provided they present the offer as “free.”

It is important to note that our studies are a conservative test of the pseudo free effect.

Generally, the costs of pseudo free offers are hidden in fine print and are easy to ignore. In our

studies, on the other hand, the costs of the pseudo free offers were made explicit to the

participants. Thus, our research clearly demonstrates that consumers should be attentive to the

costs—whether hidden or explicit—of “free” offers, and think hard about whether the benefits of

the offer outweigh the costs. However, our results also highlight the need for regulators and

public policy officials to protect consumers from manipulative, malevolent pseudo free offers.

The majority of consumers are eager to accept pseudo free offers, which means protections must

be put in place to prevent marketers from taking advantage of them. This may be especially the

case for the poor and elderly, who may be even more susceptible to these offers.

Limitations and Future Research Directions

Although our results are robust across studies, there are many limitations to the current

research. First, all of the costs of the pseudo free offers in our studies were relatively small. For

example, the cost of being forced to download an app or complete a survey is unlikely to be very

significant, and some consumers may not even consider these to be costs. Future research should

examine whether consumers respond to pseudo free offers with large costs in the same way that

they respond to pseudo free offers with smaller costs, and whether the attributions that they make

about pseudo free offers with small or large costs differ. It does seem possible that consumers

respond positively to pseudo free offers even when the costs are large, given the willingness of

41

consumers to give up their privacy and personal information in exchange for services such as

Gmail and Facebook (Covert 2014; Protalinski 2014). However, consumers are likely to accept

such a large cost only when the perceived benefit of the offer is also great, when they are

unaware of the cost, or when they have a lot of trust in the company. For example, people may

only be willing to give up valuable vacation time to attend a day-long timeshare information

session if they are given a valuable “free” vacation in return.

Second, all of our studies were scenario studies. Although previous research has

demonstrated that participants respond similarly in scenario and “real world” studies (e.g.,

Savary, Goldsmith, and Dhar 2015; Shah et al. 2014), future research on how consumers respond

to pseudo free offers should examine such responses in the field.

Third, all of the “free” products and services in our studies were relatively low value, and

frequently are “free” to consumers. For example, consumers likely are used to receiving internet

access and music streaming apps for low or no cost. Accordingly, future research should

examine whether similar effects are found for more valuable products and services, such as a

“free” vacation or “free” cell phone. One may expect that consumers would be even more likely

to accept such valuable products when they are presented as pseudo free, but consumers may

also be more skeptical when the offer appears to be “too good to be true.” This may lead them to

generate negative attributions regarding the offer—believing it is a scam—which will make them

less likely to accept the offer. One reason we may have found that participants’ default,

spontaneous attributions regarding the pseudo free offers we examined were positive may be

because the benefits of the offer were of such low value that they did not lead to suspicion.

Future research should determine the situations under which consumers’ initial, spontaneous

attributions regarding a pseudo free offer are negative. Future research should also examine if for

42

an offer that appears to be “too good to be true,” whether consumers scrutinize the offer more

closely, and realize that the non-monetary costs of the offer exceed the benefits of the offer, and

become less likely to accept the pseudo free offer.

Pseudo free offers appear to be the wave of the future (Anderson 2008). We hope that

this work is only a first step in trying to understand the way consumers respond to them.

43

REFERENCES

Aiken, Leona S. and Stephen G. West (1991), Multiple Regression: Testing and Interpreting

Interactions. Thousand Oaks, CA: Sage Publications.

Anderson, Chris (2008), “Free! Why $0.00 is the Future of Business,” Wired, February 25,

[available at http://archive.wired.com/techbiz/it/magazine/16-03/ff_free?currentPage=all]

Arthur, Charles (2013), “Android Torch App with Over 50M Downloads Silently Sent User

Location and Device Data to Advertisers,” The Guardian, December 6, [available at

http://www.theguardian.com/technology/2013/dec/06/android-app-50m-downloads-sent-

data-advertisers]

BBC (2014), “Official Complaint Filed Over Facebook Emotion Study,” July 4, [available at

http://www.bbc.com/news/technology-28157889]

Better Business Bureau (2010), BBB Lists Top 10 Scams and Rip-Offs of 2009, January 6,

[available at http://www.bbb.org/us/article/bbb-lists-top-10-scams-and-rip-offs-of-2009-

14436]

Bower, Bruce (2014), “Main Result of Facebook Emotion Study: Less Trust in Facebook,”

Science News, July 3, [available at https://www.sciencenews.org/blog/scicurious/main-

result-facebook-emotion-study-less-trust-facebook]

Chandran, Sucharita and Vicki G. Morwitz (2006), “The Price of ‘Free’‐dom: Consumer

Sensitivity to Promotions with Negative Contextual Influences,” Journal of Consumer

Research, 33 (3), 384-392.

Covert, Adrian (2014), “Gmail at 10: How Google Dominated E-mail,” CNN Money, April 1,

[available at: http://money.cnn.com/2014/04/01/technology/gmail/]

http://archive.wired.com/techbiz/it/magazine/16-03/ff_free?currentPage=all

http://www.theguardian.com/technology/2013/dec/06/android-app-50m-downloads-sent-data-advertisers

http://www.theguardian.com/technology/2013/dec/06/android-app-50m-downloads-sent-data-advertisers

http://www.bbc.com/news/technology-28157889

http://www.bbb.org/us/article/bbb-lists-top-10-scams-and-rip-offs-of-2009-14436

http://www.bbb.org/us/article/bbb-lists-top-10-scams-and-rip-offs-of-2009-14436

https://www.sciencenews.org/blog/scicurious/main-result-facebook-emotion-study-less-trust-facebook

https://www.sciencenews.org/blog/scicurious/main-result-facebook-emotion-study-less-trust-facebook

http://money.cnn.com/2014/04/01/technology/gmail/

44

Diamond, Dan (2014), “The Outrage Over Facebook’s ‘Creepy’ Experiment is Out-of-Bounds—

And This Study Proves It,” Forbes, July 1, [available at

http://www.forbes.com/sites/dandiamond/2014/07/01/the-outrage-over-facebooks-

creepy-experiment-is-out-of-bounds-and-this-study-proves-it/]

Diamond, William D. and Abhijit Sanyal (1990), “The Effect of Framing on the Choice of

Supermarket Coupons,” in Advances in Consumer Research, Vol. 17, eds. Marvin E.

Goldberg, Gerald Gorn, and Richard W. Pollay, Provo, UT: Association for Consumer

Research, 488–93.

Folkes, Valerie S. (1984), “Consumer Reactions to Product Failure: An Attributional Approach,”

Journal of Consumer Research, 10 (4), 398-409.

Hayes, Andrew F. and Kristopher J. Preacher (2014), “Statistical Mediation Analysis with a

Multicategorical Independent Variable,” British Journal of Mathematical and Statistical

Psychology, 67 (3), 451-470.

Heider, Fritz (1958), The Psychology of Interpersonal Relations. New York: John Wiley and

Sons, Inc.

Jones, Edward E. and Keith E. Davis (1965), “From Acts to Dispositions: The Attribution

Process in Person Perception,” Advances in Experimental Social Psychology, 2, 219-266.

Kahneman, Daniel and Amos Tversky (1979), “Prospect Theory: An Analysis of Decision Under

Risk,” Econometrica: Journal of the Econometric Society, 47 (2), 263-291.

Kamins, A. Michael, Valerie S. Folkes, and Alexander Fedorikhin (2009), “Promotional Bundles

and Consumers’ Price Judgments: When the Best Things in Life Are Not Free,” Journal

of Consumer Research, 36 (4), 660-70.

Kelley, Harold H. (1967), “Attribution Theory in Social Psychology,” Nebraska Symposium on

http://www.forbes.com/sites/dandiamond/2014/07/01/the-outrage-over-facebooks-creepy-experiment-is-out-of-bounds-and-this-study-proves-it/

http://www.forbes.com/sites/dandiamond/2014/07/01/the-outrage-over-facebooks-creepy-experiment-is-out-of-bounds-and-this-study-proves-it/

45

Motivation, 15, 192-238.

Kelly, Heather (2014), “Why Gmail and Other E-mail Services Aren’t Really Free,” CNN, April

1, [available at http://www.cnn.com/2014/03/31/tech/web/gmail-privacy-problems/]

Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock (2014), “Experimental Evidence

of Massive-Scale Emotional Contagion Through Social Networks,” Proceedings of the

National Academy of Sciences, 111 (29), 8788-8790.

McKnight, D. Harrison, Charles J. Kacmar, and Vivek Choudhury (2004), “Dispositional Trust

and Distrust Distinctions in Predicting High- and Low-Risk Internet Expert Advice Site

Perceptions,” E-Service, 3 (2), 35-58.

Morales, Andrea C. (2005), “Giving Firms an ‘E’ for Effort: Consumer Responses to High-Effort

Firms,” Journal of Consumer Research, 31 (4), 806-812.

Nunes, Joseph C. and C. Whan Park (2003), “Incommensurate Resources: Not Just More of the

Same,” Journal of Marketing Research, 40 (February), 26–38.

Palmeira, Mauricio M. (2011), “The Zero-Comparison Effect,” Journal of Consumer Research,

38 (1), 16-26.

Palmeira, Mauricio M. and Joydeep Srivastava (2013), “Free Offer ≠ Cheap Product: A Selective

Accessibility Account on the Valuation of Free Offers,” Journal of Consumer Research,

40 (4), 644-656.

Protalinski, Emil (2014), “Facebook Passes 1.23 Billion Monthly Active Users, 945 Million

Mobile Users, and 757 Million Daily Users,” The Next Web, January 29, [available at:

http://thenextweb.com/facebook/2014/01/29/facebook-passes-1-23-billion-monthly-

active-users-945-million-mobile-users-757-million-daily-users/]

Raghubir, Priya (2004), “Free Gift with Purchase: Promoting or Discounting the Brand?”

http://www.cnn.com/2014/03/31/tech/web/gmail-privacy-problems/

http://thenextweb.com/facebook/2014/01/29/facebook-passes-1-23-billion-monthly-active-users-945-million-mobile-users-757-million-daily-users/

http://thenextweb.com/facebook/2014/01/29/facebook-passes-1-23-billion-monthly-active-users-945-million-mobile-users-757-million-daily-users/

46

Journal of Consumer Psychology, 14 (1), 181-186.

Ross, Lee (1977), “The Intuitive Psychologist and his Shortcomings: Distortions in the

Attribution Process,” Advances in Experimental Social Psychology, 10, 173-220.

Savary, Jennifer, Kelly Goldsmith, and Ravi Dhar (2015), “Giving Against the Odds: When

Tempting Alternatives Increase Willingness to Donate,” Journal of Marketing Research,

52 (1), 27-38.

Shah, Avni M., James R. Bettman, Peter A. Ubel, Punam Anand Keller, and Julie A. Edell

(2014), “Surcharges Plus Unhealthy Labels Reduce Demand for Unhealthy Menu Items,”

Journal of Marketing Research, 51 (6), 773-89.

Shampanier, Kristina, Nina Mazar, and Dan Ariely (2007), “Zero as a Special Price: The True

Value of Free Products,” Marketing Science, 26 (6), 742-57.

Spiller, Stephen A., Gavan J. Fitzsimons, John G. Lynch Jr., and Gary H. McClelland (2013),

“Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated

Regression,” Journal of Marketing Research, 50 (2), 277-88.

Taylor, Shelley E. and Jonathon D. Brown (1988), “Illusion and Well-Being: A Social

Psychological Perspective on Mental Health,” Psychological Bulletin, 103 (2), 193-210.

Truong, Alice (2013), “This Popular Flashlight App has been Secretly Sharing Your Location

and Device ID,” Fast Company, December 5, [available at:

http://www.fastcompany.com/3023042/fast-feed/this-popular-flashlight-app-has-been-

secretly-your-sharing-location-and-device-id]

Tsiros, Michael, Vikas Mittal, and William T. Ross, Jr. (2004), “The Role of Attributions in

Customer Satisfaction: A Reexamination,” Journal of Consumer Research, 31 (2), 476-

483.

47

Tversky, Amos and Daniel Kahneman (1991), “Loss Aversion and Riskless Choice: A

Reference-Dependent Model,” Quarterly Journal of Economics, 106 (November), 1039–

62.

Vega, Cecilia (2012), “Yelp Outs Companies That Pay for Positive Reviews,” ABC News,

November 19, [available at: http://abcnews.go.com/blogs/business/2012/11/yelp-outs-

companies-that-pay-for-positive-reviews/]

Weiner, Bernard (1985), “An Attributional Theory of Achievement Motivation and Emotion,”

Psychological Review, 92 (4), 548-573.

Weiner, Bernard (2000), “Attributional Thoughts about Consumer Behavior,” Journal of

Consumer Research, 27 (3), 382-387.

Wilson, Timothy D., Thalia Wheatley, Jonathan M. Meyers, Daniel T. Gilbert, and Danny

Axsom (2000), “Focalism: A Source of Durability Bias in Affective Forecasting,”

Journal of Personality and Social Psychology, 78 (May), 821–36

Wong, Kin Fai Ellick and Jessica Y. Y. Kwong (2005), “Comparing Two Tiny Giants or Two

Huge Dwarfs? Preference Reversals Owing to Number Size Framing,” Organizational

Behavior and Human Decision Processes, 98 (September), 54–65.

http://abcnews.go.com/blogs/business/2012/11/yelp-outs-companies-that-pay-for-positive-reviews/

http://abcnews.go.com/blogs/business/2012/11/yelp-outs-companies-that-pay-for-positive-reviews/

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FIGURE 1

STUDY 2: PERCENTAGE CHOOSING VERSION A AND VERSION B AS A FUNCTION

OF CONDITIO\

0

10

20

30

40

50

60

70

80

90

100

Condition 1 ($0.50 &$3.50)

Condition 2 (PseudoFree & $3.00)

Condition 3 (PseudoFree & $2.00)P

erc

en

tag

e C

ho

osin

g V

ers

ion

A

an

d V

ers

ion

B

Condition

Version 1 (Lower Quality App) Version 2 (Higher Quality App)

49

FIGURE 2

STUDY 4: LIKELIHOOD TO ACCEPT THE AIRPORT’S WI-FI TERMS AS A FUNCTION

OF DISPOSITIONAL SUSPICION AND WHETHER THE WI-FI IS FREE, PSEUDO FREE,

OR NON-FREE

1

2

3

4

5

6

7

Low Suspicion High Suspicion

Lik

elih

oo

d t

o A

ccep

t th

e A

irp

ort

's W

i-F

i T

erm

s

Condition

Free Pseudo Free Non-Free

50

FIGURE 3A

STUDY 5: PATHS DEMONSTRATING EFFECT OF CONDITION (FREE VS. PSEUDO

FREE) ON LIKELIHOOD TO ACCEPT THE AIRPORT’S WI-FI TERMS THROUGH

ATTRIBUTIONS

FIGURE 3B

STUDY 5: PATHS DEMONSTRATING EFFECT OF CONDITION (PSEUDO FREE VS.

NON-FREE) ON LIKELIHOOD TO ACCEPT THE AIRPORT’S WI-FI TERMS THROUGH

ATTRIBUTIONS

Attributions

Likelihood to Accept the Airport’s Wi-

Fi Terms

Condition

(Free = 0, Pseudo Free = 1)

B = -.61 (p = .005) B = .53 (p < .001)

B = -2.33 (p = .359)

B = .04 (p = .885)

Attributions

Likelihood to Accept the Airport’s Wi-

Fi Terms

Condition

(Pseudo Free = 0, Non-Free = 1)

B = -.68 (p < .001) B = 1.25 (p < .001)

B = -2.33 (p < .001)

B = -1.70 (p < .001)

51

FIGURE 4

STUDY 6: LIKELIHOOD TO ACCEPT THE AIRPORT’S WI-FI TERMS AS A FUNCTION

OF WHETHER THE WI-FI IS FREE, PSEUDO FREE, PSEUDO FREE WITH POSITIVE

ATTRIBUTIONS, PSEUDO FREE WITH NEGATIVE ATTRIBUTIONS, OR NON-FREE

1

2

3

4

5

6

7

Free Pseudo Free Pseudo Freewith PositiveAttributions

Pseudo Freewith NegativeAttributions

Non-Free

Lik

elih

oo

d t

o A

ccep

t th

e A

irp

ort

's

Wi-

Fi T

erm

s

Condition

52

Web Appendix

This web appendix has been provided by the authors to give readers additional information about

their work.

Supplement to: There’s No Such Thing as a Free Lunch: Consumers’ Reactions to Pseudo Free

Offers

53

PRETEST FOR STUDY 1A

Method

Participants and design. Seventy-eight Amazon Mechanical Turk workers (41.0%

female, MAge = 31.41, SDAge = 11.15) participated in this study in exchange for $0.25.

Participants were randomly assigned to one of three conditions (expect to pay, willingness to

pay, or need to pay).

Procedure. After consenting to participate, participants completed the following

questionnaire. The questionnaire was identical for all of the conditions except where it is noted

otherwise.

Participants read, “You are about to read a scenario. Please imagine that you are actually

in the scenario, and answer the following questions as accurately as possible.”

Participants in the expect to pay condition read, “Imagine that you are at an airport

waiting for a flight. You have used up your data for this month, but you would love to check

your Facebook and Twitter.” They were then asked, “How much would you expect to have to

pay to use the airport’s Wi-Fi?” “What is the least that you would expect to have to pay to use

the airport’s Wi-Fi?” and “What is the most that you would expect to have to pay to use the

airport’s Wi-Fi?” These questions were answered on a sliding scale with $0.00 and $20.00 as the

endpoints.

Participants in the willingness to pay condition read the same scenario, but were asked,

“How much would you be willing to pay to use the airport’s Wi-Fi?” on a sliding scale with

$0.00 and $20.00 as the endpoints.

54

Participants in the need to pay condition read the same scenario, except that an additional

sentence was tacked. The sentence said, “As you are waiting at your gate, you see a sign that

says the airport is trying to get people to download its app.” They were then asked, “How much

would the airport have to pay you in cash or credit toward airport services (e.g. Wi-Fi) in order

for you to be willing to download the airport’s app?” on a sliding scale with $0.00 and $20.00 as

the endpoints.

All participants were then asked the following demographic questions:

“Do you have a Facebook account?” Yes, No

“Do you have a Twitter account?” Yes, No

“How often do you use your Facebook account?” Never, Rarely, Sometimes, Often, All

of the Time, 2-3 Times a Week, Daily

“How often do you use your Twitter account?” Never, Rarely, Sometimes, Often, All of

the Time, 2-3 Times a Week, Daily

“What is your age?”

“What is your gender?” Male, Female, Other

“What is your race/ethnicity? (Please check all that apply.)” Asian, African American,

Caucasian, Hispanic, Native American, Other

“On the scale below, please indicate your HOUSEHOLD’S approximate yearly income

before taxes.” Less than $20,000, $20,000-$40,000, $40,000-$60,000, $60,000-$90,000,

$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000

“Is English your native language?” Yes, No

“Do you have any comments/suggestions for us?”

Results

55

The pre-test results revealed that consumers would expect to pay approximately $3.30 for Wi-Fi

at an airport (M = 3.30, SD = 3.86), would expect to pay no less than $0.80 to use an airport’s

Wi-Fi (M = .80, SD = 1.35), and would expect to pay no more than approximately $5.80 to

access an airport’s Wi-Fi (M = 5.84, SD = 4.51). Moreover, in the scenario presented,

participants would be willing to pay approximately $3.50 to use the airport’s Wi-Fi (M = 3.54,

SD = 2.44), but would need to be paid approximately $7.50 to be willing to download the

airport’s app (M = 7.50, SD = 6.81).

STUDY 1A

Procedure

After consenting to participate, participants completed the following questionnaire. The

questionnaire was identical for all of the conditions except where it is noted otherwise.



Participants in the free condition then read, “Imagine that you are at an airport waiting for

a flight. You have used up your data for this month, but you would love to check your Facebook

and Twitter. As you are waiting at your gate, you see a sign that says, ‘Free Wi-Fi.’ All you

need to do is click the standard accept terms statement.”

Participants in the pseudo free condition read, “Imagine that you are at an airport waiting

for a flight. You have used up your data for this month, but you would love to check your

Facebook and Twitter. As you are waiting at your gate, you see a sign that says, ‘Free Wi-Fi if

you download the airport’s app.’”

56

Participants in the pseudo free with free salient condition read, “Imagine that you are at

an airport waiting for a flight. You have used up your data for this month, but you would love to

check your Facebook and Twitter. As you are waiting at your gate, you see a sign that says,

‘Free Wi-Fi if you download the airport’s app.’”

Participants in the pseudo free with cost salient condition read, “Imagine that you are at

an airport waiting for a flight. You have used up your data for this month, but you would love to

check your Facebook and Twitter. As you are waiting at your gate, you see a sign that says,

‘Free Wi-Fi if you download the airport’s app.’”

Participants in the non-free condition read, “Imagine that you are at an airport waiting for


and Twitter. As you are waiting at your gate, you see a sign that says, ‘Wi-Fi for $3.50.’”


airport’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely.

Participants were then asked, “Please take a moment to think about what you must give

the airport in order to access its Wi-Fi. What you must give the airport in order to access its Wi-

Fi can be considered the cost of the Wi-Fi offer. Accordingly, how costly is the airport’s Wi-Fi

offer?” on a seven point scale with 1 = Not at all costly and 7 = Extremely costly.






57









$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



STUDY 1B

Procedure





Participants in the free condition then read, “Imagine that you are at a coffee shop, and

you plan to hang out for a while. You really want to check your Facebook and Twitter, but

you’ve used up your data for the month. However, you see a sign that says, ‘Free Wi-Fi.’ All

you need to do is click the standard accept terms.”

58

Participants in the pseudo free non-monetary cost condition then read, “Imagine that you

are at a coffee shop, and you plan to hang out for a while. You really want to check your

Facebook and Twitter, but you’ve used up your data for the month. However, you see a sign that

says, ‘Free Wi-Fi if you fill out a short customer satisfaction survey.’”

Participants in the pseudo free monetary cost condition then read, “Imagine that you are

at a coffee shop, and you plan to hang out for a while. You really want to check your Facebook

and Twitter, but you’ve used up your data for the month. However, you see a sign that says,

‘Free Wi-Fi with any purchase of $3 or more.’”

Participants in the non-free condition then read, “Imagine that you are at a coffee shop,

and you plan to hang out for a while. You really want to check your Facebook and Twitter, but

you’ve used up your data for the month. However, you see a sign that says, ‘Wi-Fi for $3.’”

Participants in the thoughts about motives condition then read, “As you are deciding

whether to accept or reject the offer, you think about why the coffee shop is offering this Wi-Fi

deal. Specifically, you think about the coffee shop’s motives behind the offer. Please take a

moment to think about the potential reasons why the coffee shop has these Wi-Fi terms and is

offering this Wi-Fi deal.”


coffee shop’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely.

Participants then indicated their level of agreement with the following statements on a

seven point scale with -3 = Strongly disagree and +3 = Strongly agree:

“The coffee shop has this Wi-Fi deal so that customers have a pleasant experience at the

coffee shop.”

“The coffee shop has this Wi-Fi deal to help customers get a good deal.”

59

“The coffee shop has this Wi-Fi deal to increase its profits.” (reverse coded)

“The coffee shop has this Wi-Fi deal to monitor the activity of customers.” (reverse

coded)

Participants then responded to the question, “Is this Wi-Fi deal better for the consumer or

the coffee shop?” on a seven point scale with 1 = Definitely better for the coffee shop and 7 =

Definitely better for the consumer

Participants were also asked, “When you were thinking about whether to accept the

coffee shop’s Wi-Fi offer, what weighed more heavily in your decision—the cost of the Wi-Fi

(i.e., what you would need to do to get the Wi-Fi) or the benefit of the Wi-Fi?” on a seven point

scale with 1 = Definitely the cost and 7 = Definitely the benefit. They were also asked, “How

suspicious were you of the coffee shop’s motives for having this Wi-Fi offer?” on a seven point

scale with 1 = Not at all suspicious and 7 = Very suspicious.


“Do you have a Facebook or Twitter account?” Yes, No

“How often do you use your Facebook and/or Twitter account?” Never, Less than Once a

Month, Once a Month, 2-3 Times a Month, Once a Week, 2-3 Times a Week, Daily

“Have you completed this study or a similar study in the past?” Yes, No







60

STUDY 2

Procedure





Participants in Condition 1 ($0.50 & $3.50) read, “Imagine that you are deciding between

two versions of a music streaming app. Version 1 is $0.50, has periodic commercial breaks, and

mediocre sound quality. Version 2 is $3.50, has no commercials, and high sound quality.”

Participants in Condition 2 (pseudo free & $3.00) read, “Imagine that you are deciding

between two versions of a music streaming app. Version 1 is free, has periodic commercial

breaks, and mediocre sound quality. Version 2 is $3.00, has no commercials, and high sound

quality.”

Participants in Condition 3 (pseudo free & $2.00) read, “Imagine that you are deciding

between two versions of a music streaming app. Version 1 is free, has periodic commercial

breaks, and mediocre sound quality. Version 2 is $2.00, has no commercials, and high sound

quality.”

All participants were then asked, “Which version of the music streaming app would you

choose?” Version 1 or Version 2

Participants were then asked the following two questions:

61

“Which version of the music streaming app would you be more likely to choose?” on a

seven point scale with 1 = Definitely Version 1 and 7 = Definitely Version 2

“Overall, how happy do you think you will be with your music app choice?” on a seven

point scale with -3 = Very unhappy and +3 = Very happy

Participants then responded to the following items from the Deal Proneness Scale

(Lichtenstein, Burton, and Netemeyer 1997) on a seven point scale with 1 = Strongly disagree

and 7 = Strongly agree:

“I am more likely to buy a brand if it has a cents-off deal on the label.”

“Compared to most people, I would say I have a positive attitude toward cents-off deals.”

“I enjoy buying products that come with a free gift.”

“Beyond the money I save, buying a brand that comes with a free gift gives me a sense of

joy.”

“I have favorite brands, but if I see a ‘2 for 1’ offer, I am more likely to buy that brand.”

“When I take advantage of a ‘buy-one-get-one-free’ offer, I feel good.”

“I am more likely to buy brands that are displayed at the end of the aisle.”

“End-of-aisle displays have influenced me to buy brands I normally would not buy.”

“When I use coupons, I feel that I am getting a good deal.”

“I enjoy using coupons, regardless of the amount I save by doing so.”

“Receiving cash rebates makes me feel good.”

“By the time you pay postage, mail-in cash rebates are not worth the hassle.” (reverse

coded)

“I feel compelled to respond to contest or sweepstake offers.”

“Manufacturers’ contests and sweepstakes are fun to enter, even if I know I’ll never win.”

62

“One should try to buy the brand that is on sale.”

“I am more likely to buy brands that are on sale.”

Participants then responded to the following eighteen items of the Need for Cognition

Scale (Cacioppo, Petty, and Kao 1984) on a nine point scale (-4 = Very strong disagreement, -3

= Strong disagreement, -2 = Moderate disagreement, -1 = Slight disagreement, 0 = Neither

agreement nor disagreement, +1 = Slight agreement, +2 = Moderate agreement, +3 = Strong

agreement, +4 = Very strong agreement):

“I would prefer complex to simple problems.”

“I like to have the responsibility of handling a situation that requires a lot of thinking.”

“Thinking is not my idea of fun.” (reverse coded)

“I would rather do something that requires little thought than something that is sure to

challenge my thinking abilities.” (reverse coded)

“I try to anticipate and avoid situations where there is likely a chance I will have to think

in depth about something.” (reverse coded)

“I find satisfaction in deliberating hard for long hours.”

“I only think as hard as I have to.” (reverse coded)

“I prefer to think about small, daily projects to long-term ones.” (reverse coded)

“I like tasks that require little thought once I’ve learned them.” (reverse coded)

“The idea of relying on thought to make my way to the top appeals to me.”

“I really enjoy a task that involved coming up with new solutions to problems.”

“Learning new ways to think doesn’t excite me very much.” (reverse coded)

“I prefer my life to be filled with puzzles that I must solve.”

“The notion of thinking abstractly is appealing to me.”

63

“I would prefer a task that is intellectual, difficult, and important to one that is somewhat

important but does not require much thought.”

“I feel relief rather than satisfaction after completing a task that required a lot of mental

effort.” (reverse coded)

“It’s enough for me that something gets the job done; I don’t care how or why it works.”

(reverse coded)

“I usually end up deliberating about issues even when they do not affect me personally.”

Participants then responded to the following questions:

“Do you have a smart phone?” Yes, No

“If yes, how often do you use a music streaming app?” Never, Less than Once a Month,

Once a Month, 2-3 Times a Month, Once a Week, 2-3 Times a Week, Daily

“In general, how important are the following to you when you consider the selection of a

streaming music app?”

“Low price” 1 = Not at all important, 7 = Very important

“Number of music offerings” 1 = Not at all important, 7 = Very important

“High sound quality” 1 = Not at all important, 7 = Very important

“Variety of music offering” 1 = Not at all important, 7 = Very important

“A commercial free experience” 1 = Not at all important, 7 = Very important

“Ease of use” 1 = Not at all important, 7 = Very important


“Have you completed this study or a similar study in the past?” Yes, No



64





STUDY 3

Procedure
















65

All of these participants were then asked, “How likely are you to accept the terms and use

the airport’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely.

These participants were also asked, “How positively or negatively do you feel about the

airport’s Wi-Fi terms of use?” on a seven point scale with -3 = Very negatively and +3 = Very

positively.

Participants in the internet willingness to pay condition read, “Imagine that you are at an

airport waiting for a flight. You have used up your data for this month, but you would love to

check your Facebook and Twitter.” They were then asked, “How much would you be willing to

pay to use the airport’s Wi-Fi?” on a sliding scale with $0 and $20 as the endpoints.

Participants in the app need to pay condition read, “Imagine that you are at an airport

waiting for a flight. You have used up your data for this month, but you would love to check

your Facebook and Twitter. As you are waiting at your gate, you see a sign that says the airport

is trying to get people to download its app.” They were then asked, “How much would the

airport have to pay you in cash or credit toward airport services (e.g. Wi-Fi) in order for you to

be willing to download the airport’s app?” on a sliding scale with $0 and $20 as the endpoints.









66






$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



STUDY 4

Procedure











67






Participants in the thoughts about motives present condition then read the following

instructions, “As you are deciding whether to accept or reject the offer, you think about why the

airport is offering this Wi-Fi deal. Specifically, you think about the airport’s motives behind the

offer. Please take a moment to think about the potential reasons why the airport has these Wi-Fi

terms and is offering this Wi-Fi deal.”



They were also asked the following three questions:

“When thinking about how likely you are to accept the terms and use the airport’s Wi-Fi,

why did you say that you were likely or unlikely to accept the terms and use the airport’s Wi-

Fi?” as an open-ended question.

“When thinking about your likelihood of accepting the airport’s offer, how suspicious

were you of the airport’s motives behind the Wi-Fi offer?” on a seven point scale with 1 = Not at

all suspicious and 7 = Extremely suspicious.

“How satisfied are you with the airport’s Wi-Fi terms?” on a seven point scale with -3 =

Not at all and +3 = Extremely.

Participants then responded to the dispositional suspicion scale (McKnight, Kacmar, and

Choudhury 2004):

68

“Please select the response that best reflects the extent to which you agree or disagree

with the following statements” (1 = Strongly disagree, 7 = Strongly agree).

“I usually trust people until they give me a reason not to trust them.” (reverse coded)

“I generally give people the benefit of the doubt when I first meet them.” (reverse coded)

“My typical approach is to trust new acquaintances until they prove I should not trust

them.” (reverse coded)

“People are usually out for their own good.”

“People pretend to care more about one another than they really do.”

“Most people inwardly dislike putting themselves out to help other people.”

“Most people would tell a lie if they could gain by it.”

“People don’t always hold to the standard of honesty they claim.”

“Most people would cheat on their income tax if they thought they could get away with

it.”










69





$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



STUDY 5

Procedure













70






They were also asked the following two open-ended questions:



Fi?”

“Why do you think the airport has this Wi-Fi offer? Specifically, what do you think are

the airport’s motives for having this Wi-Fi offer?”

Participants then responded to the following five-item attributions scale (-3 = Strongly

disagree, +3 = Strongly agree; unless noted otherwise):

“The airport has this Wi-Fi deal so that travelers have a pleasant experience at the

airport.”

“The airport has this Wi-Fi deal to help travelers get a good deal.”

“The airport has this Wi-Fi deal to increase its profits.” (reverse coded)

“The airport has this Wi-Fi deal to monitor the activity of travelers.” (reverse coded)

“Is this Wi-Fi deal better for the consumer or the airport?” on a seven point scale with 1 =

Definitely better for the airport and 7 = Definitely better for the consumer

Participants were then asked the following three questions:

“Please take a moment to think about what you must give the airport in order to access its

Wi-Fi. What you must give the airport to access its Wi-Fi can be considered the cost of the Wi-

71

Fi offer. Accordingly, how costly is the airport’s Wi-Fi offer?” on a seven point scale with 1 =

Not at all costly and 7 = Extremely costly

“How positively or negatively do you feel towards the airport’s Wi-Fi offer?” on a seven

point scale with -3 = Very negatively and +3 = Very positively



all suspicious and 7 = Extremely suspicious


Choudhury 2004):













it.”

72

Participants were then asked, “To what extent do you agree with the following statement:

‘There’s no such thing as a free lunch.’” on a seven point scale with -3 = Strongly disagree with

that statement and +3 = Strongly agree with that statement














$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



STUDY 5B (SUPPLEMENTAL STUDY)

The goal of Study 5B was to provide further evidence that the attributions consumers

73

make regarding a pseudo free offer drive their responses to it. Although Study 5 provided

evidence that the attributions consumers make regarding offers mediate their response to them,

the measured agreement with attribution-related statements may not have truly captured the

attributions consumers would naturally make. Therefore, in this study, participants were

explicitly asked why they thought the coffee shop had the Wi-Fi offer in an open-ended format,

which was subsequently coded for level of positivity or negativity. This approach allowed us to

more directly examine whether consumers tend make positive attributions regarding pseudo free

offers, and if these attributions drive their responses to the offer.

Method

Participants and design. One hundred and twenty Amazon Mechanical Turk workers


Participants were randomly assigned to one of three conditions (free, pseudo free, or non-free).


that they were actually in the same scenario as in Study 1B (WiFi in a coffee shop), except that

the pseudo free monetary cost condition was dropped. After reading the scenario and learning

about the Wi-Fi sign for their condition and responding to the dependent variable, participants

were asked, “Why do you think the coffee shop has this Wi-Fi offer?” as an open-ended

question. Two independent coders rated how positive or negative participants’ attributions in

response to this question were on a five point scale with -2 = Very negative and +2 = Very

positive. The coders’ ratings were averaged to create an attributions score for each participant (α

= .80). Participants then indicated the degree to which they agreed or disagreed with the same

five attribution statements that were used in Study 5, although they were adapted to the coffee

shop scenario (i.e., “The coffee shop has this Wi-Fi deal so that customers have a pleasant

74

experience at the coffee shop”). These items were combined to form a second attributions scale

(α = .59), with higher scores indicating more positive attributions.

After answering these questions, participants completed some demographic questions—

such as gender and age—and were then debriefed and thanked for their participation.


A one-way ANOVA revealed a significant effect of condition (F(2, 117) = 44.27, p <

.001). Tukey’s HSD post-hoc analyses again revealed that participants in the free (M = 5.95, SD

= 1.29) and pseudo free (M = 5.57, SD = 1.76) conditions were significantly more likely to

accept the coffee shop’s Wi-Fi terms and use the internet than participants in the non-free (M =

2.73, SD = 1.88; Tukey’s HSD q’s > 18.63, both ps < .001) condition. The difference between

the free and pseudo free conditions was not significant (Tukey’s HSD q = 2.49, p = .575).

We next conducted a one-way ANOVA that revealed a significant effect of condition on

participants’ self-generated attributions (F(2, 117) = 28.40, p < .001). Tukey’s HSD post-hoc

analyses revealed that participants in the free condition (M = .55, SD = .76) and pseudo free

condition (M = .88, SD = .48) made significantly more positive attributions regarding the Wi-Fi

offer than participants in the non-free condition (M = -.43, SD = 1.09; Tukey’s HSD q’s > 13.19,

both ps < .001). The difference between the free and pseudo free conditions was not significant

(Tukey’s HSD q = 4.43, p = .172).

Next, to test our main hypothesis in this study—that the self-generated attributions

consumers make regarding the offer mediate their response to the offer—we conducted

regression analyses and a test of mediation (Hayes and Preacher 2014). We first conducted

regression analyses to determine the paths between the independent variable (condition),

mediator (attributions), and dependent variable (likelihood to accept the coffee shop’s Wi-Fi

75

offer). Specifically, we did this twice, once to determine the paths when comparing the free and

pseudo free conditions, and a second time to determine the paths when comparing the pseudo

free and non-free conditions. As shown in the web appendix, the path from condition (free = 0,

pseudo free = 1) to likelihood to accept the airport’s Wi-Fi offer is nonsignificant (B = -.38, SE =

.35, t = -1.08, p = .283). Moreover, the path from condition (free = 0, pseudo free = 1) to

attributions (B = .33, SE = .14, t = 2.33, p = .022) was significant, but from attributions to

likelihood to accept the coffee shop’s Wi-Fi offer (B = .11, SE = .27, t = .39, p = .698) was not

significant. When attributions is included in the model, the path from condition (free = 0, pseudo

free = 1) to likelihood to accept the coffee shop’s Wi-Fi offer (B = -.44, SE = .36, t = -1.22, p =

.227), and the effect of attributions on likelihood to use the coffee shop’s Wi-Fi (B = .19, SE =

.28, t = .69, p = .493), remain nonsignificant.

As shown in the web appendix, the path from condition (pseudo free = 0, non-free = 1) to

likelihood to accept the coffee shop’s Wi-Fi offer is significant (B = -2.85, SE = .40, t = -7.09, p

< .001). Similarly, the paths from condition (pseudo free = 0, non-free = 1) to attributions (B = -

1.31, SE = .18, t = -7.09, p < .001) and from attributions to likelihood to accept the coffee shop’s

Wi-Fi offer (B = 1.32, SE = .19, t = 6.79, p < .001) are both significant. When attributions is

included in the model, the path from condition (pseudo free = 0, non-free = 1) to likelihood to

accept the coffee shop’s Wi-Fi offer is weakened but remains significant (B = -1.83, SE = .48, t

= -3.81, p < .001), and the effect of attributions on likelihood to use the coffee shop’s Wi-Fi

remains significant (B = .78, SE = .23, t = 3.39, p = .001).

The data were then submitted to a test of multilevel categorical variable indirect effects

(Hayes and Preacher 2014). Using the MEDIATE macro for SPSS (Hayes and Preacher 2014),

this test was conducted based on dummy coding (comparing the free to pseudo free and pseudo

76

free to non-free conditions) using a bootstrap sample n = 10,000. Mediation analysis revealed an

omnibus effect of condition on likelihood to use the coffee shop’s Wi-Fi through self-generated

attributions made about the offer, B = .23, CI (95%) = [.10, .44]. When a bootstrap confidence

interval for the omnibus test does not contain zero, one has evidence of mediation (Hayes and

Preacher 2014). However, omnibus indirect effects can also be broken down by comparing

specific levels of the independent variable. These tests revealed that the differences between the

free and pseudo free conditions (B = -.24, CI (95%) = [-.59, -.04]) and pseudo free and non-free

conditions (B = -.96, CI (95%) = [-1.63, -.45]) on the likelihood to use the coffee shop’s Wi-Fi

were both mediated by the attributions consumers made regarding the coffee shop’s Wi-Fi offer.

The same results were found when using participants’ responses to the five attribution statements

as the measure of consumers’ attributions.

The results from this study provide more evidence that consumers’ attributions regarding

offers mediate their response to the offers. Specifically, most consumers spontaneously generate

positive attributions regarding pseudo free offers, which lead them to respond to the offers in a

way similar to how they respond to free offers. Importantly, we found this effect when we used

participants’ self-generated attributions as our measure of attributions.

STUDY 5B

Procedure



77



Participants in the free condition then read, “Imagine that you are at a coffee shop, and

you plan to hang out for a while. You really want to check your Facebook and Twitter, but

you’ve used up your data for the month. However, you see a sign that says, ‘Free Wi-Fi.’ All

you need to do is click the standard accept terms.”

Participants in the pseudo free condition then read, “Imagine that you are at a coffee

shop, and you plan to hang out for a while. You really want to check your Facebook and

Twitter, but you’ve used up your data for the month. However, you see a sign that says, ‘Free

Wi-Fi if you fill out a short customer satisfaction survey.’”

Participants in the non-free condition then read, “Imagine that you are at a coffee shop,

and you plan to hang out for a while. You really want to check your Facebook and Twitter, but

you’ve used up your data for the month. However, you see a sign that says, ‘Wi-Fi for $3.’”


coffee shop’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely

They were then asked, “Why do you think the coffee shop has this Wi-Fi offer?” as an

open-ended question.

Participants then responded to the following five-item attributions scale (-3 = Strongly

disagree, +3 = Strongly agree; unless noted otherwise):

“The coffee shop has this Wi-Fi deal so that customers have a pleasant experience at the

coffee shop.”

“The coffee shop has this Wi-Fi deal to help customers get a good deal.”

“The coffee shop has this Wi-Fi deal to increase its profits.” (reverse coded)

78

“The coffee shop has this Wi-Fi deal to monitor the activity of customers.” (reverse

coded)

“Is this Wi-Fi deal better for the consumer or the coffee shop?” on a seven point scale

with 1 = Definitely better for the coffee shop and 7 = Definitely better for the consumer

Participants were then asked, “To what extent do you agree with the following statement:

‘There’s no such thing as a free lunch.’” on a seven point scale with -3 = Strongly disagree with

that statement and +3 = Strongly agree with that statement














$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



79

STUDY 7

Procedure













Participants in the pseudo free with positive attributions condition read, “Imagine that

you are at an airport waiting for a flight. You have used up your data for this month, but you

would love to check your Facebook and Twitter. As you are waiting at your gate, you see a sign

that says, ‘Free Wi-Fi if you download the airport’s app.’ As you are looking at the sign, an

airport employee tells you that the airport wants travelers to download the app so that it can

easily communicate important information to them. In addition, he tells you that it allows the

airport’s restaurants and shops to inform travelers about special deals.”

80

Participants in the pseudo free with negative attributions condition read, “Imagine that

you are at an airport waiting for a flight. You have used up your data for this month, but you

would love to check your Facebook and Twitter. As you are waiting at your gate, you see a sign

that says, ‘Free Wi-Fi if you download the airport’s app.’ As you are looking at the sign, an

airport employee tells you that the airport wants travelers to download the app so that it can

easily track them and monitor their behavior. In addition, he tells you that it allows the airport to

gather data about the travelers that can be sold to advertisers so they can target advertisements to

specific consumers.”





airport’s Wi-Fi?” on a seven point scale with 1 = Not at all and 7 = Extremely

Participants were also asked the following six questions:



Fi?” as an open-ended question.



all suspicious and 7 = Extremely suspicious

“Why do you think the airport has this Wi-Fi offer?” as an open-ended question.

“How satisfied are you with the airport’s Wi-Fi terms?” on a seven point scale with -3 =

Not at all and +3 = Extremely

81

“How much would you benefit from having access to the airport’s Wi-Fi?” on a seven

point scale with 1 = I would not benefit at all and 7 = I would benefit a lot

“Please take a moment to think about what you must give the airport in order to access its

Wi-Fi. What you must give the airport in order to access its Wi-Fi can be considered the cost of

the Wi-Fi offer. Accordingly, how costly is the airport’s Wi-Fi offer?” on a seven point scale

with 1 = Not at all costly and 7 = Extremely costly


Choudhury 2004):













it.”



82












$90,000-$120,000, $120,000-$150,000, $150,000-$200,000, Greater than $200,000



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