Extreme Malleability of Consumer Preferences:
Absolute Preference Sign Changes under Uncertainty
JOACHIM VOSGERAU
EYAL PEER
Joachim Vosgerau ([email protected]) is Professor of Marketing at Bocconi
University, Italy. Eyal Pe’er ([email protected]) is Senior Lecturer at the Graduate School for
Business Administration at Bar-Ilan University, Israel. The authors thank Chris Olivola for
helpful suggestions, and Nurit Hod and Sharon Lieberman for research assistance.
Correspondence should be addressed to Joachim Vosgerau ([email protected]),
Bocconi University,Via Roentgen 1, 20136 Milan, Italy.
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EXTREME MALLEABILITY OF CONSUMER PREFERENCES:
ABSOLUTE PREFERENCE SIGN CHANGES UNDER UNCERTAINTY
ABSTRACT
The malleability of consumer preferences is central in marketing. The more malleable
preferences are, the more effective can marketing actions such as branding and advertising be.
How malleable preference really are, however, is a topic of debate. Do preference reversals
imply preference construction? The authors argue that to claim preferences are constructed, a
demonstration of more extreme preference malleability is required: absolute preference sign
changes within participants. If consumers value a prospect positively in one condition but
negatively in a different condition, preferences cannot be considered stable. Such absolute
preference sign changes are possible under uncertainty. In four experiments, we found
participants were willing to pay to take part in a gamble, but also demanded to be compensated
to take part in the same gamble. Such absolute preference sign changes led to simultaneous riskaversion and risk-seeking for the same risky prospect.
Keywords: preference reversal, preference construction, risk-preferences
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Are consumer preferences stable or malleable? This is arguably one of the most
fundamental questions in marketing. Marketing researchers differ in their assumptions about the
stability (or lack thereof) of consumer preferences. One school—the quantitative school—
models consumer preferences by means of utility functions, thereby assuming preferences to be
innate and stable (e.g., Carroll and Green 1997; Hauser and Rao 2004). A consumer knows her
or his preferences or—for unfamiliar products and services—may discover them through direct
experience (e.g., product trials; Hamilton and Thompson 2007). The role of marketing, according
to these models, is to inform consumers through advertising, and to stimulate demand by
encouraging consumers to try out new offerings. The purpose of consumer research is to measure
consumer preferences using sophisticated quantitative methods that elicit consumers’ individual
weighing of product attributes (e.g., Scholz, Meissner, and Decker 2010).
The other so-called behavioral school, in contrast, argues that consumer preferences are
construed—rather than stable and revealed—at the point of preference elicitation (Slovic 1995).
Marketing actions such as advertising, branding, packaging, pricing, distribution, product
assortment, etc. are used to directly manipulate preferences, and the purpose of consumer
research is to identify the factors that allow for such manipulations. The measurement of
consumer preferences, according to this school, is an intricate business. Construction of
preferences implies that measurements are context-dependent (Amir and Levav 2008), so
different elicitation methods may yield different preferences even within the same context (e.g.,
Hsee et al. 1999; Slovic and Lichtenstein 1983).
Simonson (2008a)—in criticizing the behavioral school—argued that extant
demonstrations of preference reversals have not yet conclusively shown that preferences are
indeed constructed. Following Simonson’s (2008a) argument, we suggest an extreme form of
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preference reversals—absolute preference sign changes within participants—which we argue
provide the best evidence that preferences can be truly constructed. We hypothesize that such
absolute preference sign changes can occur under uncertainty, because most consumers are
inherently uncertain about whether they like or dislike uncertainty.
ARE ALL PREFERENCES CONSTRUCTED?
In a seminal article, Simonson (2008a) criticized behavioral researchers for being too
dismissive of inherent, stable preferences. Simonson argued that consumers are bad at absolute
judgments, however they are surprisingly accurate in relative judgments (Ariely, Loewenstein,
and Prelec 2003; Drolet, Simonson, and Tversky 2000). Judgment and decision researchers—so
Simonson—have exploited this in their studies by asking participants to judge unfamiliar and
abstract stimuli in isolation. Not surprisingly, under these conditions preferences are malleable
and preference reversals can be easily observed. In contrast, in more realistic settings in which
familiar stimuli are judged/evaluated in the presence of meaningful reference standards,
preference consistency is the norm. Simonson (2008a) concludes that revealed preferences are
surely composed of both, inherent and stable, and context-specific and constructed, components.
Simonson’s (2008a) critique evoked a lively debate among consumer behavior
researchers about the degree to which preferences are inherent/stable or construed (Bettman,
Luce, and Payne 2008; Dhar and Novemsky 2008; Kivetz, Netzer, and Schrift 2008; Smith 2008;
Simonson 2008b). Complicating matters, it may not even be possible to empirically determine
the degree to which preferences are inherent or constructed, because consistency does not
necessarily imply inherent/stable preferences, nor does inconsistency necessarily imply
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construction of preferences (Amir and Levav 2008; Bettman et al. 2008; Dhar and Novemsky
2008).
WHEN ARE PREFERENCES STABLE OR MALLEABLE?
Instead of trying to determine the extent to which preferences are inherent or constructed,
it may be more fruitful to determine the conditions under which preferences are stable or
malleable. Information-processing models that take memory’s influence on choice into account
stipulate that preferences are stable when there are prior, accessible, and diagnostic attitudes
available for retrieval (Bettman, Luce, and Payne 1998; Feldman and Lynch 1988). Furthermore,
preferences will be stable to the extent that choice contexts are similar to each other, individuals
repeatedly use the same chronically accessible information, are in similar moods, and show
similar levels of processing motivation (Schwarz 2007; Schwarz and Bohner 2001). In
conclusion, preferences are stable across contexts when they are based on matching mental
representations.
When stimuli and their mental representations are ambiguous, in contrast, preferences
become malleable and susceptible to contextual influences. The difficulty with which attribute
levels can be discerned across choice options, for example, has been shown to affect preference
malleability. In the famous study by Hoch and Ha (1986), participants in the ambiguous
condition inspected and evaluated a set of white polo t-shirts, all of the same weave mesh and
styling features, some with logos, which were difficult to distinguish in terms of overall quality.
An advertisement for one of the shirt brands highlighting its exceptional quality had a dramatic
effect on quality ratings. The advertisement was most effective in influencing quality ratings
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when it was watched before than after the shirt had been inspected. Participants in the
unambiguous condition inspected and evaluated paper towels which varied dramatically in
quality (thickness, strength, and absorbance). Advertising claims had little impact on quality
ratings, whether claims were watched before or after product inspections (cf., also Deighton
1984, and Lee, Frederick, and Ariely 2006). Likewise, Lee et al. (2015) asked participants to
evaluate international flight tickets in a conjoint-like experiment, either providing them with
prices or with flight duration information. Preferences were less transitive when prices rather
than flight durations were provided, indicating greater preference malleability. Prices are
ambiguous signals for overall evaluations, because they can be interpreted as status/quality
signals (benefits), or as cost. Flight duration, on the other hand, is an unambiguous attribute, the
shorter the flight the better. In general, analytical evaluations require consideration of more
ambiguous information and more tradeoffs between opposing information pieces than affective
evaluations do, thus often leading to less preference consistency than affective evaluations (Lee,
Amir, and Ariely 2009).
EXTREME MALLEABILITY: ABSOLUTE PREFERENCE SIGN CHANGES
Malleability of preferences can be demonstrated by showing that preference ratings differ
as a function of irrelevant factors. Ground beef described as 75% lean, for example, is rated as
higher quality than beef described as 25% fat (Levin and Gaeth 1988). Levin, Schneider, and
Gaeth (1998) called such framing effects “choice shifts”, compared to stronger demonstrations of
malleability as preference reversals. Prominent examples of preference reversals often involve
uncertainty: When asked how much one is willing to pay to play a gamble, most consumers
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value a high payoff/low probability gamble (e.g., win $100 with 10% chance) more than a low
payoff/high probability gamble (e.g., win $10 with 90% chance). But when asked to choose
between the two gambles, the latter is chosen more often than the former (Grether and Plott
1979; Lichtenstein and Slovic 1971; Slovic and Lichtenstein 1968, 1973).
The weak form of preference malleability—choice shifts—involves differing evaluations
of the same object, whereas the strong form of preference malleability—preference reversals—
involves a reversal of the rank-ordering of objects. Simonson (2008a)—as pointed out
beforehand—criticized both demonstrations of preference malleability as (unfairly) exploiting
consumers’ weakness in making absolute judgments. If judgments were made in the presence of
meaningful reference standards, the prevalence of preference reversals would be greatly reduced.
According to this argument, the strongest form of preference malleability would be to
demonstrate an absolute preference sign change within participants: the same consumer likes a
prospect in condition A but dislikes it—in an absolute sense—in condition B. To measure
absolute preference sign changes, a ratio-scaled evaluation-variable is required.
Willingness to pay to consume a product, and its counterpart, the amount that participants
require as compensation for consumption, would provide such a ratio-scaled evaluation-variable
(Ariely, Lowenstein, and Prelec 2006). Ariely et al. (2006) asked one group of students whether
they would be willing to pay $2 to attend Prof. Ariely recital of Walt Whitman’s Leaves of
Grass: 3% agreed. Another group was asked whether they would attend if they were paid $2:
here 59% agreed. Then both groups were asked whether they would attend for free. Thirty-five
percent of those who were previously asked to pay responded affirmatively, compared to only
8% of those who were previously asked how much they needed to be compensated.
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Ariely et al.’s demonstration of preference malleability is a relative preference reversal in
hypothetical scenarios. Participants rank-ordered the two options—attending or not attending—
differently depending on the preceding question of whether they would be willing to pay to
attend or be willing to attend for pay. In both cases, participants may have liked the poem recital
(they just liked one a bit more than the other), or they may have both disliked the recital (they
just disliked one a bit less than the other). In contrast, if one were to assess how much
participants are actually willing to pay—and demand as compensation—to consume the same
prospect, one may be able to demonstrate an absolute preference sign chance.
We believe that such extreme preference malleability can be observed under uncertainty.
Uncertainty can be aversive, such as the unforeseeable risks of traffic accidents, falling ill,
political unrest, wars, environmental disasters, etc. Uncertainty, however, can also be appealing
and fun: gambling, bungee-jumping, climbing, motor-biking, all are examples of activities for
which inherent uncertainty is appealing. As ambiguity in the evaluation of products engenders
preference malleability, uncertainty can make offerings appear both aversive and appealing.
Hence, when asking someone how much they would be willing to pay to play a gamble, the
person may infer that the gamble is desirable. If asking the same person how much they would
need to be compensated to play the gamble, the person may infer that the gamble is undesirable.
In combination, those insights suggest that absolute preference sign changes are possible under
uncertainty, and may even be observed within-subjects. Note that the risky prospect needs to
entail both positive (excitement, curiosity; Vosgerau, Wertenbroch, and Carmon 2006) and
negative (potential harm, loss of control) facets for this to work.
In four experiments, we test whether participants are willing to pay (WTP) and demand
compensation (compensation amount—CA) for identical risky prospects. WTP and CA thus
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serve as measures of revealed risk preferences. Our hypothesis that absolute preference sign
changes are possible under uncertainty implies that participants may be risk-averse and riskseeking at the same time.
It has been shown that consumers can be risk-seeking in one domain, for example
gambling, and risk-averse in another, for example, in their choice of health insurance plans
(Slovic 1964; Barseghyan et al. 2011; Blais and Weber 2006; Weber, Blais, and Betz 2002).
Likewise, buyers have been shown to be risk seeking when bidding in a first price auction, but to
be risk averse when bidding for the same asset in an English clock auction (which begin with a
high asking price which is lowered until someone is willing to accept the auctioneer's price; Berg
et al. 2005; cf., also Isaac and James 2000). One may hence argue that such opposing risk
preferences already show that absolute preference sign changes are possible. We believe,
however, such a conclusion to be premature. Absolute risk preferences cannot be directly
observed, they are inferred from observable valuations such as willingness to pay or
compensation demanded for taking part in risky gambles (Charness, Gneezy, and Imas 2013).
When monetarily evaluating gambles, study participants are usually quite surprised to learn that
their valuations are inconsistent when they imply opposing risk preferences (Gelman 1998). The
reason for their bewilderment is of course the non-observability of risk preferences, in contrast to
observable monetary valuations that seem perfectly consistent. In directly observable valuations
such as monetary evaluations, absolute preference sign changes have never been shown. The
goal of this paper is to demonstrate such changes in monetary evaluations.
In the first experiment we tested whether absolute preference sign changes can occur in
hypothetical scenarios in a between-subjects design. In the second experiment, we ruled out
alternative accounts for these preference sign changes. In the third experiment, participants
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played gambles for real money (again in a between-subjects design) to test preference sign
changes in an incentive-compatible manner. In the last experiment, we used real money and
employed a within-subjects design to test whether absolute preference sign changes can be found
for the same participant evaluating the same risky prospect. For all experiments, we report all
measures and conditions employed. We analyzed data only after data collection was completed.
All materials and data files can be found at https://osf.io/8av9f.
EXPERIMENT 1
In experiment 1 we tested the malleability of preferences under uncertainty by asking
participants to provide either their maximum WTP or their minimum CA for three hypothetical
gambles with a positive expected value. Each gamble offered a 60% chance of winning $X and a
40% chance of losing $X. The stake X was manipulated within-subjects at three levels: $10,
$100, and $1000.
Method
Participants, Stimuli, and Procedure. Participants were recruited on Amazon Mechanical
Turk, a platform that has been shown to produce high quality data (Buhrmester et al. 2011,
Goodman et al. 2013, Paolacci and Chandler 2014). MTurk workers were invited to complete a
short study in return for 50 cents. We collected 399 responses from 239 males and 160 females.
Participants’ age ranged from 19 to 69 with an average of 35.67 (SD = 10.54). We used a custom
script to ensure that none of our participants took part in more than one experiment; recruitment
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was restricted to participants who reside in the U.S., are 18 years or older, and who had a 95%
approval rating on their previous tasks (Peer, Vosgerau, and Acquisti 2013).
All participants were given the following instructions: “In the next pages we are going to
show you descriptions of several gambles with different stakes and chances. For each gamble,
please imagine that you can actually play that gamble and actually win or lose the amounts
specified.” Participants were then given a description of the three gambles and were asked to
state, for each, either “What is the lowest amount (in dollars) that you would have to get paid to
take part in this gamble?” (CA condition) or “What is the highest amount (in dollars) that you
would be willing to pay to take part in this gamble? (WTP condition). The three gambles were
presented separately from each other and in random order. The experiment employed thus a 2
(frame: CA vs. WTP, between-subjects) x 3 (stake: $10 vs. $100 vs. $1000, within-subjects)
design.
Because participants could indicate only positive values for WTP and CA, the data would
be censored at 0. Consequently, any noise could affect WTP and CA in only one direction, which
would be interpreted as support for our hypothesis. To address this concern, participants were
reminded “You may enter any amount in dollars, including zero”, thus minimizing the possibility
that participants thought they were supposed to indicate values greater than 0. Participants
entered their valuation in an open-ended text-box, which accepted only numerical entries
between $0 and X, the stake of the gamble in question to avoid unrealistic excessive values.
Results
For all three gambles, the majority of participants was willing to pay—and asked to be
compensated—to play the gambles (all proportions > 50%, all ps < .001). For the distribution of
WTP and CA for the $10 gamble see Figure 1, for all proportions and medians of WTP, CA, and
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their corresponding proportions of implied risk preferences see Table 1. To test whether
participants would be more risk-averse the larger were the stakes (Holt and Laury 2002), we
classified the behavior of each participant in each gamble as either risk-seeking/neutral or riskaverse (see Table 1), and ran a non-parametric related samples test (Cochran’s Q) across the
three gambles. In accordance with Holt and Laury’s (2002) findings, participants were indeed
more risk-averse the larger were the stakes (test statistic (2) = 66.27, p < .001).
[insert Figure 1 and Table 1 about here]
Discussion and test of alternative explanations
In accordance with our hypothesis that consumers do not have well-defined preferences
under uncertainty, the majority of participants was willing to pay—and demanded to be
compensated—to play the gambles. In accordance with prior work on risk preferences,
participants were found to be more risk-averse the higher were the stakes.
Because our measures of WTP and CA are censored at zero, participants could only enter
values equal to or greater than 0, alternative explanations for the findings are possible. For
example, participants may have simply answered the questions by inserting random values.
Alternatively, participants may have some true valuation for the gamble that is invariant to the
method of elicitation and is bounded between -$10 and $10. The elicited valuation may be the
sum of their true valuation and some error, e, which is symmetrically distributed around zero. In
other words, consumers may have well-defined risk preferences within a (small) margin of error.
According to both alternative explanations, the left-censoring of WTP and CA at zero would
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then be sufficient to produce the result that more than 50% of elicited values in both measures
exceeded zero.
We test both alternative explanations by looking at the distribution of elicited values. The
first explanation of random valuations would imply that valuations are symmetrically distributed
around zero, and WTP and CA should show the same distributions when left-censored. The
second explanation of well-defined preferences within a margin of error would imply that the
malleability of preferences is either a) greatest for small valuations and declines as valuations of
the gambles increase (if the margin of error is constant and thus uncorrelated with the valuation
of a gamble), or b) constant across valuations (if the margin or error is proportional to the
valuation of gambles).
In contrast, if participants are uncertain about their preferences but do not appreciate
losing money, preference-variability and valuations should be correlated in a specific manner. In
the CA condition, participants could hedge against a potential loss, at the extreme setting CA = X
(the stake of the gamble) which would provide complete insurance against a potential loss. In the
WTP condition, in contrast, participants ‘hedged’ against a potential gain, at the extreme of WTP
= X completely erasing any potential gain. So, in general CAs should be higher than WTPamounts, in contrast to the prediction from the random-valuation account that valuations are
symmetrically distributed. Furthermore, the discrepancy between WTP and CA should increase
as valuations of the gamble increase (loss-aversion would further exacerbate the effect, but is not
necessary for it). The explanation of well-defined preferences within a margin of error, in
contrast, predicts the opposite, either a decrease in the discrepancy between WTP and CA as
valuations of the gamble increase, or constant discrepancy between WTP and CA independent of
the valuations of the gambles.
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Figure 2 displays the median WTP and CA for the three gambles. In accordance with our
account of preference malleability, CAs are much higher than amounts for WTP for all three
gambles, contrary to the random valuation explanation. Furthermore, the discrepancy between
CA and WTP amounts increases as the stake of the gamble—and thus its valuation—increase,
opposite to what the alternative explanation of well-defined preferences within a margin of error
would imply. Regression analysis further support these pattern of results (see Table 2). We
regressed the money amounts on a frame dummy (CA and WTP), two stake dummies, and their
interaction terms using robust error estimation. Errors were clustered by participants to account
for each participant responding to all three stake gambles. The regression estimates show that
CAs were greater than amounts for WTP (frame dummies, ps ≤ .001), and that both CA and
WTP were higher for larger than smaller stake gambles (stake dummies, ps < .001). The
predicted interactions of frame and stake dummies (ps < .001) show that as stakes increased, so
did the discrepancy between CA and WTP.
[insert Figure 2 and Table 2 about here]
EXPERIMENT 2
Another way to test the extent to which left-censoring contributed to the finding that over
50% were willing to pay and asked to be paid to take part in the gamble would be to use noncensored DVs, akin to Ariely et al’s (2006) study. So, one could first ask: “Would you be willing
to pay to take part in this gamble?” or “Would you have to get paid to take part in this gamble?,”
and only when these questions are answered affirmatively proceed to assess the numerical values
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of WTP or CA. In case participants responded with “no,” they would be provided with the
opposite evaluation assessments asking for CA or WTP, respectively1.
While this is an elegant way to get around the issue of left-censored DVs, the questions
are also almost entirely devoid of the framing effect which makes the gamble appear desirable or
aversive. “Would you be willing to pay to take part in this gamble?” insinuates to a much lesser
extent that the gamble is attractive, unlike the question “What is the highest amount you would
be willing to pay to take part in this gamble?” The same holds for the CA questions.
Consequently, running the study with these modified questions should produce weaker
differences than observed in experiment 1.
Method
Participants, Design and Procedure. Participants were recruited through the Prolific
Academic website (www.prolific.ac) which is an online recruitment platform similar to MTurk
(Woods et al. 2015). We collected 199 responses from 105 males and 94 females. Participants’
age ranged from 18 to 67, with an average of 31.13 (SD = 2.91). Participants were paid 0.5 GBP
for completing the study.
Participants were asked to express their personal preferences regarding two gambles. One
gamble had a 60% chance to win $10 and a 40% chance to lose $10, the other had a 60% chance
to win $100 and a 40% chance to lose $100. The order of the gambles was counterbalanced
between participants.
Participants in the CA condition were asked “Would you have to get paid to take part in
this gamble?” and could answer “yes” or “no”. If answered “yes”, the following question
appeared on the screen: “What is the lowest amount (in dollars) that you would have to get paid
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to take part in this gamble?” If answered “no”, participants were asked: “What is the highest
amount (in dollars) that you would be willing to pay to take part in this gamble?” In the WTP
condition, participants were asked: “Would you be willing to pay to take part in this gamble” and
could answer “yes” or “no”. If answered “yes”, the following question appeared on the screen:
“What is the highest amount (in dollars) that you would be willing to pay to take part in this
gamble?” If answered “no”, they were asked: “What is the lowest amount (in dollars) that you
would have to get paid to take part in this gamble?” In both WTP and CA conditions, the
questions were asked for both of the two gambles. At the end of the study all participants were
asked to provide their gender and age, and were given an opportunity to provide comments about
the study.
Results and Discussion
Replicating the findings in experiment 1, the majority of participants was willing to pay
(69.4%) for the $10 gamble and demanded to be compensated (51.5%) to play the same gamble
(joint test against 50%: χ2 (1) = 8.45, p = .004; see Table 3). For the $100 gamble, participants
were more risk-averse overall, which reduced the number of participants willing to pay (42.9%)
to play the gamble and increased the number of participants demanding to be compensated
(61.4%) for playing the gamble (together, the percentages are not significantly different from
50%: χ2 (1) = 0.41, p = .523). Implied risk-aversion and risk-seeking followed the same pattern.
[insert Table 3 about here]
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While the weaker framing effect still produced the same pattern of over 50% being
willing to pay and demanding to be paid to play the $10 gamble, for the $100 gamble increased
overall risk-aversion was stronger than the framing effect. Together, the results suggest that the
framing effect found in experiment 1 is not only caused by the use of left-censored DVs. To the
contrary, it can be produced without censored DVs in small stake gambles. To get the same
effect in large stake gambles one would have to use gambles with a higher expected value since
risk-aversion increases as the stake in the gamble increases.
EXPERIMENT 3
Note that in experiment 1, some participants chose extreme values of CA = X and WTP =
X (X denoting the gamble’s stake, see Figure 1). It is hard to believe that these participants
would play the gambles only if they could not lose any money (CA = X) or if they were
guaranteed to lose all their money (WTP = X). We believe that such (literally) unbelievable
responses are the result of cheap-talk. To test this, experiment 3 and 4 employed gambles
involving real monetary gains and losses. Incentive-compatible measures of WTP and CA should
also further decrease the influence of left-censoring on observing opposing preferences.
In experiment 3, participants in the WTP condition were asked how much of $2 they
were willing to pay to play each of two gambles. Participants in the CA condition were asked to
indicate how much they needed to be compensated to play the two gambles. Both gambles had—
as in the previous experiment—a 60% chance to win $X and a 40% chance to lose $X. For the
low stake gamble X = 10 cents, and for the high stake gamble X = $1. To avoid the possibility
that prior gains/losses would influence participants’ valuations (Thaler and Johnson, 1990),
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participants were told that—after their valuations—one of the two gambles would be randomly
chosen and played out for real. CA and WTP were elicited incentive-compatible with the BDM
procedure (Becker et al. 1964), and payments and losses were taken from a base amount all
participants received after completing unrelated studies.
Method
Participants. MTurk workers were invited to complete a study online in return for 50
cents plus a bonus of $2 and “a chance to take part in a lottery”. We collected 101 responses
from 69 males and 32 females. Participants’ age ranged from 18 to 62 with an average of 30.78
(SD = 9.00).
Design and procedure. Participants completed this study as the final part of a larger study
that included various questionnaires and surveys that took approximately 15 minutes. We asked
participants to first fill out the unrelated surveys to give them the feeling that they had earned the
bonus of $2 that would be used as endowment for the gambling part. Participants arriving at the
gambling stage were told: “You have now earned a bonus of $2 for completing the previous
studies. The next, and last, study is about lotteries. In this study, you can take part in one of two
lotteries using the bonus you just earned. We will describe these two lotteries to you and ask you
to state your personal preference toward taking part in each of them. After you indicate your
preference for each of the lotteries, our survey software will randomly pick one of the lotteries
and show you the results. Depending on the outcome of that lottery, it may increase or reduce
your final bonus. Please read the following instructions carefully as it will affect your final
payment.” These instructions appeared for at least 10 seconds before the participants could
continue. Then, participants were randomly allocated to either the CA or WTP condition, and
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were given further instructions (according to condition) regarding the BDM preferenceelicitation method (see Appendix).
After reading the instructions, participants were asked to summarize the instructions
briefly in their own words and to answer a question that verified their comprehension of the
BDM procedure (see the Appendix). Participants who failed this check question received a
message saying their answer was incorrect, and were given another chance to answer the
question. Twenty-one participants failed to answer the question the second time (12 in the CA
condition and 9 in the WTP condition). Excluding these responses does not change the results.
Participants were then presented with the two lotteries and asked to indicate how much
money (in cents) they would have to be paid [be willing to pay] to take part in the lottery. The
two lotteries were presented on separate pages and in random order. After participants entered
their WTP/CA for both lotteries, the survey software randomly chose one of the lotteries and
checked whether the participant’s WTP/CA was higher/lower than the randomly generated
number. Participants were informed about the randomly generated number and whether they
would or would not play the lottery. If the lottery was to be played, the survey software
generated a random outcome for the lottery according to the gamble’s probabilities (60%
winning, 40% losing) and informed the participant of the lottery’s outcome. Participants were
then paid accordingly.
Results and discussion
As in experiment 1, for both gambles the majority of participants was willing to pay—
and demanded to be compensated—to play the gambles (all proportions > 50%, all ps < .01; see
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Table 4). Absolute preference sign changes as observed in experiment 1 and 2 seem to reflect
realistic behavior in the sense that participants put their money where their mouth was.
To our surprise, we still observed a few excessive valuations, even though evaluations
were incentive-compatible. As can be seen in Figure 3, 13.7% of participants chose the
maximum value of CA = $1 in the $1 stake gamble, implying that they would play the gamble
only if they could not lose any money. Likewise for WTP, 14.0% of participants were willing to
pay 50 cents for a gamble with an EV of only 20 cents. To further decrease the possibility of
cheap talk or misunderstandings, in the final experiment we increased the stake to over $5 and
test whether absolute preference sign changes can also be observed within-subjects.
[insert Figure 3 and Table 4 about here]
EXPERIMENT 4
In experiment 4 we increased the stake to a potential win of about $7.80 (30 New Israeli
Shekel - NIS) and a potential loss of about $5.20 (20 NIS). More importantly, we asked each
participant to indicate both—CA and WTP—for the same risky prospect. Experiment 4 is thus
the most conservative test of absolute preference sign changes. Participants were asked how
much they were willing to pay to toss a coin to win 30 NIS if ‘heads’ came up versus lose 20
NIS if ‘tails’ came up. Then, participants were asked how much they had to be compensated for
rolling a die to win 30 NIS if an even number came up versus lose 20 NIS if an odd number
came up. WTP and CA were elicited with the BDM procedure, and the order and combination of
WTP/CA and the risk-resolution mechanism (coin toss vs. die roll) were randomized. As in
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experiment 3, after WTP and CA were elicited, one of the two gambles was randomly selected to
be played out.
Method
Participants. Participants were invited using flyers distributed across an Israeli university
campus to take part in a study conducted in a computer laboratory. We collected 95 responses
from 33 males and 62 females. Participants’ age ranged from 18 to 36, with an average of 22.78
(SD = 2.91).
Design and procedure. The lab included 10 separated computer workstations. Two
research assistants facilitated data collection during three consecutive days. The study’s
instructions were provided using the same survey software as in experiment 3, except that
instructions were in Hebrew. The study consisted of five parts. As in experiment 3, three parts of
the study (parts 1, 3, and 5) consisted of unrelated questionnaires to give participants the feeling
that they had earned the money that they would be endowed with for the gambling part. Parts 2
and 4 were the experimental gambling parts. One part was called the “toss a coin” lottery:
participants were told that they would earn 30 NIS if ‘heads’ came up and lose 20 NIS if ‘tails’
came up (thus, EV = 5 NIS). The other part was the “roll a die” lottery: if an even number came
up participants would earn 30 NIS, in case of an odd number they would lose 20 NIS (again, EV
= 5 NIS). The “toss a coin” and “roll a die” lotteries (part 2 and 4) were counterbalanced. For
one of the lotteries participants were asked how much they would need to be compensated (CA),
for the other lottery they were asked for their WTP. Participants were told that one of the
lotteries (to be chosen at random) would be played out after they had completed all parts of the
21
study. Both CA and WTP were assessed with the BDM procedure as in experiment 3. The
experimenters ensured that all participants had understood the instructions.
Experiment 4 employed a 2 (frame order: WTP-CA vs. CA-WTP) x 2 (lottery order:
coin-die vs. die-coin) between-subjects design. Upon completion of the study, the software
randomly selected one lottery and checked whether a participant would play it according to the
outcome of the BDM procedure. If so, the software would display which lottery (coin or die) was
to be played. The participant was asked to inform the experimenter (who verified the
participants’ information as it was presented on the screen for the experimenter to see). The
experimenter then tossed a coin/die accordingly, and paid the participant according to the actual
outcome.
Results and discussion
Again, the majority of participants was willing to pay (50.5%)—and demanded to be
compensated (76.8%)—to play the gambles. For 46.3%, both CA and WTP were greater than 0
(t-test against 0%: t(94) = 9.01, p < .001). CA and WTP correlated positively, r = 0.20, p = .053,
indicating that the more participants asked to be compensated the more they were also willing to
pay. Obviously, this is at odds with any meaningful conceptualization of stable preferences. If
preferences had a stable component the correlation would have to be negative; the positive
correlation provides—together with the 46.3% of absolute preference sign changes withinsubjects—strong evidence that these preferences are constructed.
The implied risk preferences are displayed in Table 5; most participants were consistently
risk-averse (62.1%), however, over a third of participants (34.7%) were simultaneously riskaverse and risk-seeking/neutral (see Figure 4).
22
[insert Figure 4 and Table 5 about here]
GENERAL DISCUSSION
How malleable are consumer preferences? That is one of the most central questions in
marketing. The role of marketing changes fundamentally depending on how this question is
answered. At one extreme—when consumers preferences are regarded as innate and stable—
marketing actions such as advertsing and branding have the sole function to inform consumers.
On the other extreme—when preferences are assumed to be constructed at the point of
elicitation—all marketing actions are part of the marketer’s tool box to influence and shape
consumer preferences.
Inspired by Simonson’s (2008a, b) critique that consumer behavior researcher have not
conclusively shown preferences to be constructed (because they typically ask study-participants
to judge unfamiliar and abstract stimuli in isolation), we proposed and tested a stronger form of
preference malleability: absolute preference sign changes within participants. Such absolute sign
changes under uncertainty can be observed with gambles that entail both positive (gains) and
negative (losses) aspects. The overall expected value of these gambles were positive to overcome
general loss aversion. Participants were willing to pay and at the same time demanded to be paid
to play identical gambles.
Our results suggest that consumer preferences may be most malleable under uncertainty.
Market offers that entail uncertainty may hence enjoy greater advertising and branding
elasticities than offers that do not. Examples of such offers are sports activities, adventures, and
23
other forms of consumer experiences. In fact, it seems like all experiences can be easily framed
as entailing uncertainty. For material offers, in contrast, it may be less useful to emphasize
uncertainty (e.g., one does not want to highlight the unreliability of a product). But even here one
can think of examples where framing matters. For instance, when considering how much of a
premium to pay for a more lenient return policy (Petersen and Kumar 2015), consumers might
consider the benefits of reducing risk, but when asked how much to “get paid” in order to forego
a return option, consumers might focus more on the downsides of increasing risk. Finally,
probabilistic pricing methods (Mazar, Shampanier, and Ariely 2016) can be applied to
experience and material offers alike. The introduced uncertainty about which final price to pay
may not only increase the attractiveness of the offer but may also increase its susceptibility to
advertising and branding. Exploring different ways to introduce (enjoyable) uncertainty into
market offers and testing how they influence advertsing and branding elasticities seems to be an
exciting avenue for future research.
Our findings also bear implications for risk-preferences. In experiment 4, WTP and CA
were measured incentive-compatible for a gamble with a potential gain of $7.80 and a potential
loss of about $5.20. Over a third of participants showed simultaneously risk-aversion and riskseeking. Interestingly, WTP and CA correlated positively, implying that the more risk-averse a
person was the more they were also risk-seeking. This suggest that there is heterogeneity in the
susceptibility to framing. Some consumer segments may show stronger preference malleability
under uncertainty than others. The positive correlation also provides strong counter-evidence for
any theory of decision making under risk that assumes some form of stable risk-preferences.
Stable risk preferences are not only assumed in neo-classical utility models, even prospect theory
(Tversky and Wakker 1995) and the most flexible theories to-date that allow for context-
24
dependent risk preferences, for example Loomes’ (2010) PRAM and Stewart, Reimers, and
Harris’ (2014) range-frequency account, cannot capture the absolute sign changes in riskpreferences that were observed in the present studies. Like a small but growing number of
economists (Friedman et al. 2014), we believe the problem of current theories of risky choice lies
in the definition of risk. Most consumers understand risk not as dispersion of outcomes—the
commonly used definition of risk—but as the possibility of harm. Downside risks are perceived
as risky and are avoided, but upside risks can be exciting (cf., also Golman, Loewenstein, and
Gurney 2016).
25
Footnotes
1
We thank Chris Olivola for suggesting this study.
26
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Table 1. Medians and proportions of CA, WTP, and implied risk preferences in experiment 1.
Stake
Compensation
Amount (CA)
Md
frequency
CA > $0
(risk-averse)
+/- $10
$5
+/- $100
+/- $1000
Willingness to Pay (WTP)
Md
frequency
WTP > $0
frequency
WTP < EV
(risk-averse)
frequency
WTP > EV
(risk-seeking)
85.1%
[166/195]
$1
75.5%
[154/204]
55.4%
[113/204]
31.4%
[64/204]
$50
88.2%
[172/195]
$5
74.0%
[151/204]
70.1%
[143/204]
19.1%
[39/204]
$580
87.2%
[170/195]
$20
64.7%
[132/204]
85.3%
[174/204]
10.3%
[21/204]
34
Table 2. Regression (robust error estimation) results for data from experiment 1.
β
t-statistic
(SE)
p
intercept
2.60
(0.21)
12.15
< .001
dummy frame
0 = WTP
1 = CA
2.98
(0.34)
8.73
< .001
dummy stake 1
0 = $10
1 = $100
12.11
(1.41)
8.58
< .001
dummy stake 2
0 = $10
1 = $1000
90.73
(13.15)
6.90
< .001
frame x stake 1
37.84
(2.89)
13.08
< .001
frame x stake 2
464.77
(30.95)
15.02
< .001
R2
0.55
35
Table 3. Results for Experiment 2.
Gamble
Condition
Answered
Yes
Risk-averse
(CA > 0)
Risk-seeking
(WTP > EV)
CA:
Would you have to get
paid?
51.5%
[52/101]
50.5%
[51/101]
15.8%
[16/101]
WTP:
Would you be willing
to pay?
69.4%
[68/98]
28.6%
[28/98]
50.0%
[49/98]
joint test against 50%
χ2 (1) = 8.45
p = .004
CA:
Would you have to get
paid?
61.4%
[62/101]
60.4%
[61/101]
9.9%
[10/101]
WTP:
Would you be willing
to pay?
42.9%
[42/98]
52.0%
[51/98]
8.2%
[8/98]
joint test against 50%
χ2 (1) = 0.41
p = .523
+/- $10
+/- $100
36
Table 4. Medians and proportions of CA, WTP, and implied risk preferences in experiment 3.
Stake
Compensation
Amount (CA)
Md
frequency
CA > $0
(risk-averse)
+/- $0.10
$0.20
+/- $1.00
$0.50
Willingness to Pay (WTP)
Md
frequency
WTP > $0
frequency
WTP < EV
(risk-averse)
frequency
WTP > EV
(risk-seeking)
96.1%
[49/51]
$0.03
70.0%
[35/50]
46.0%
[23/50]
52.0%
[26/50]
96.1%
[49/51]
$0.10
70.0%
[35/50]
58.0%
[29/50]
36.0%
[18/50]
37
Table 5. Observed proportions of implied risk preferences in experiment 4 (EV = expected
value).
Risk Preference
Observed proportions in
Experiment 3
risk-averse (WTP < EV)
62.1% [59/95]
risk-neutral (WTP = EV ˄ CA = 0)
1.1% [1/95]
risk-seeking (WTP > EV ˄ CA = 0)
2.1% [2/95]
risk-neutral & risk-averse (WTP = EV ˄ CA > 0)
7.4% [7/95]
risk-seeking & risk-averse (WTP > EV ˄ CA > 0)
27.4% [26/95]
38
Figure 1. Distribution of CA and WTP for the $10 gamble in experiment 1.
Compensation Amount (CA)
35%
Willingness to Pay (WTP)
30%
25%
20%
15%
10%
5%
0%
$0
$1
$2
$3
$4
$5
$6
$7
$8
$9
$10
39
Figure 2. Median CA and WTP in experiment 1.
$160
$580
$140
Compensation Amount (CA)
Median CA (+)
$120
Willingness to Pay (WTP)
$100
$80
$50
$60
$40
Median WTP (-)
$20
$0
-$20
$5
-$1
-$11
-$40
$10 stake gamble
$100 stake gamble
-$20
$1000 stake gamble
40
Figure 3. Distribution of CA and WTP for the $1 gamble in experiment 3.
35%
Compensation Amount (CA)
30%
Willingness to Pay (WTP)
25%
20%
15%
10%
5%
0%
0
1
2
3
5 10 15 20 25 30 35 40 50 59 60 70 75 80 85 90 99 100
WTP / CA in cents
41
Figure 4. Joint distribution of compensation amount (CA) and willingness to pay (WTP) for the
risky prospect in Experiment 4.
20
Frequency
15
0
3
10
5
0
0 3
6 9
12 15
18 21
24 27
CA (NIS)
30
6
9
12
15
18
21
WTP (NIS)
24
27
30
42
APPENDIX
Instructions for Experiment 3
CA condition:
In this survey, you will be shown two lotteries. For example, in one of the lotteries you
will have a 60% chance to win $1.00 and a 40% chance to lose $1.00. For each lottery, you will
be asked how much you have to be paid to take part in the lottery. You may enter any amount
between 0 to 100 cents as the amount you have to be paid to take part in the lottery. We call that
amount your required payment. Our survey software will randomly choose a number between
1 and 100. This randomly determined number is the lottery offered payment. If your required
payment is equal to or lower than the lottery offered payment, the lottery will be played. In that
case, you will receive the payment you asked for, and you will gain an additional $1.00 if you
win the lottery or lose $1.00 if you lose the lottery. If your required payment is higher than the
lottery offered payment, the lottery will not be played and you will not receive any additional
payment. In that case, you will only receive the initial $2 bonus.
Examples:
Your required
Lottery offered
Play
payment
payment
lottery?
If you win you get:
If you lose you get:
20 cents
40 cents
Yes
$2 initial bonus plus
$2 initial bonus minus
$1.00 plus $0.20 =
$1.00 plus $0.20 = $1.20
$3.20
40 cents
40 cents
Yes
$2 initial bonus plus
$2 initial bonus minus
$1.00 plus $0.40 =
$1.00 plus $0.40 = $1.40
$3.40
60 cents
40 cents
No
$2 initial bonus only
Notice that because the lottery offered payment is determined randomly and
independently from your required payment, your best strategy is to ask for an amount that truly
43
reflects how much you actually have to get paid to take part in the lottery. That is because if you
ask for an amount that is lower than your true preference, there is a chance you might have to
play the lottery and receive less than what you were actually willing to accept. On the other
hand, if you ask for an amount that is higher than your true preference, there is a chance you
might not be able to play the lottery even if its offered payment turns out to be something you
were actually willing to accept. Lastly, remember that our survey software will pick only one
lottery and show you the results only for that lottery. So, you should indicate your preferences
independently for each lottery, as if that lottery is the only one that will be played.
After reading the instructions, participants were asked to summarize the instructions
briefly in their own words and to answer a question that verified their comprehension. In the CA
condition, the question was “If your required payment is 50 cents, in which of the following
cases would the lottery be played?” with four answer options:
§
the lottery offered payment is lower than 50 cents
§
the lottery offered payment is equal to or lower than 50 cents
§
the lottery offered payment is equal to or higher than 50 cents [correct answer]
§
the lottery offered payment is higher than 50 cents.
WTP condition:
In this survey, you will be shown two lotteries. For example, in one of the lotteries you
will have a 60% chance to win $1.00 and a 40% chance to lose $1.00. For each lottery, you will
be asked how much you are willing to pay to take part in the lottery. You may enter any
amount between 0 to 100 cents as the amount you are willing to pay to take part in the lottery.
We call that amount your offered price. Our survey software will randomly choose a number
between 1 and 100. This randomly determined number is the price that has to be paid to take part
in the lottery (lottery price). If your offered price is equal to or higher than the lottery price, the
lottery will be played. In that case, you will pay the lottery price, and you will gain an additional
$1.00 if you win the lottery or lose $1.00 if you lose the lottery. If your offered price is lower
than the lottery price, the lottery will not be played and you won't pay anything. In that case, you
will only receive the initial $2 bonus.
44
Examples:
Your offered
Play
Lottery price
price
lottery?
If you win you get:
If you lose you get:
40 cents
20 cents
Yes
$2 initial bonus plus
$1.00 minus $0.20 =
$2.80
$2 initial bonus minus
$1.00 minus $0.20 =
$0.80
40 cents
40 cents
Yes
$2 initial bonus plus
$1.00 minus $0.40 =
$2.60
$2 initial bonus minus
$1.00 minus $0.40 =
$0.60
40 cents
60 cents
No
$2 initial bonus only
Notice that because the lottery price is determined randomly and independently from
your offer, your best strategy is to give an offer that truly reflects how much you would actually
be willing to pay to play this lottery. That is because if you give an offer that is higher than your
true preference, there is a chance you might have to play the lottery and pay more than what you
were actually willing to pay. On the other hand, if you give an offer that is lower than your true
preference, there is a chance you might not be able to play the lottery even if its price turns out to
be something you were actually willing to pay. Lastly, remember that our survey software will
pick only one lottery and show you the results only for that lottery. So, you should indicate your
preferences independently for each lottery, as if that lottery is the only one that will be played.
After reading the instructions, participants were asked “If you offered 50 cents to pay for
the lottery, in which of the following cases would the lottery be played?” with four answer
options:
§
the lottery price is lower than 50 cents
§
the lottery price is equal to or lower than 50 cents [correct answer]
§
the lottery price is equal to or higher than 50 cents
§
the lottery price is higher than 50 cents.
45