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RED-WINGED BLACKBIRD MANUSCRIPT REVIEW HISTORY
MANUSCRIPT (ROUND 2)
Abstract
The use of advertised reference prices in the general form “Regularly $119.99, Sale $39.99” is
ubiquitous and effective. A long history of pricing research supports the conclusion that
advertised reference prices exert an influence on consumer deal evaluations via changes in the
internal reference price. We advance an additional mediator of this process, product thought
overlap. Specifically, we hypothesize that the overlap in memory of product-related thoughts
(e.g., features, benefits, usage situations) evoked by (1) the advertised reference price
(“Regularly $119.99”), and (2) the offering price (e.g., “Sale $39.99”), mediates the influence of
the advertised reference price on the internal reference price, thereby influencing consumer deal
evaluations. Results also show that product thought overlap varies naturally across product
categories (experiment 1), or can be manipulated within product category by manageriallycontrollable context variables (experiments 2 and 3). Implications for theory, practice, and public
policy are discussed.
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Perhaps no sales promotion technique is used as frequently by retailers as are advertised
reference prices (ARPs). An ARP promotional technique entails the pairing of an advertised
reference price (e.g., “Regularly $119.99”) with an offer price (OP) (e.g., “Sale $39.99”). An
ARP signals that the consumer can pay a lower than market price for the advertised product if
s/he purchases it from the retailer (Della Bitta, Monroe, and McGinnis 1981). The use of ARPs is
pervasive across merchant types, product and service categories, and media vehicles (e.g.
newspapers, radio, direct mail, television, in-store signage).
This widespread use of ARPs is motivated by one simple truth: they can and often do
exert a powerful influence on consumer purchase behavior. For example, Ortmeyer, Quelch, and
Salmon (1991) and Kaufmann, Smith, and Ortmeyer (1994) review instances in which major
retailers (e.g., Sears, Macy’s) attempted to move away from ARP advertising in favor of
everyday low pricing (ELP). These retailers quickly abandoned the ELP strategy because of the
immediate and negative effect on sales. In describing the common experiences of these retailers,
Kaufman et al. (1994, 117-118) state:
“Given the bump in sales volume that each promotional event inevitably produced, it
appeared that consumers responded to the ‘sale’ message even if competitive everyday
prices could be found elsewhere. Incorporating greater discounts into the sale events also
spurred consumer response. Even with the same net purchase price, consumer response
was greater to the retailer offering a greater percentage off the higher original
price…Discontinuing a promotional event often meant giving up sales volume to the
competitor that did promote.”
Consistent with the experiences of these retailers, there is also much academic research
evidence that points to the same conclusions; ARPs have been shown to positively influence a
range of consumer price-related responses, including perceptions of a fair price, the normal price,
the average market price, the lowest available price in the market, perceptions of savings,
purchase value, and purchase intentions from the advertiser, and to negatively influence
intentions to search the marketplace for a lower OP (e.g., Bearden, Lichtenstein, and Teel 1984;
Berkowitz and Walton 1980; Biswas and Blair 1991; Blair and Landon 1981; Burton,
Lichtenstein, and Herr 1993; Grewal, Monroe, and Krishnan 1998; Lichtenstein and Bearden
1988, 1989; Lichtenstein, Burton, and Karson 1991; Urbany, Bearden, and Weilbaker 1988).
This influence has been shown to hold for ARPs that exceed OPs by both plausible and
implausible amounts (Krishna et al. 2002; Monroe 1990; Urbany et al. 1988). Thus, evidence
from the collective marketplace and academic research appears unambiguous: ARP advertising
is widespread and can have a powerful influence on consumer responses, including sales.
Despite this influence, what we now know about the process by which ARPs influence
consumer responses is, at its most fundamental level, what we have known for approximately
three and a half decades; ARPs influence consumer responses (e.g., deal perceptions, willingness
to buy, search intentions) via their influence on consumer internal reference prices (IRPs) (see
Monroe 1979; 1990). Researchers have identified moderators of this three-construct process
(e.g., Lichtenstein and Bearden 1989; Lichtenstein et al. 1991; Urbany et al. 1988), but to our
knowledge, additional mediators of the process have not been identified. The purpose of the
present investigation is to propose and test a second mediator of this process. Specifically, based
on principles rooted in Norm Theory (Kahneman and Miller 1986), Assimilation-Contrast
Theory (Sherif, Taub, and Hovland 1958), and the Selective Accessibility account of anchoring
(Mussweiler 2002; Strack and Mussweiler 1997), we advance the construct of product thought
overlap and define it as the degree of overlap in product-related thoughts (e.g., attributes,
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benefits, usage situations) evoked in memory by the ARP, and (2) product-related thoughts
evoked in memory by the OP. We hypothesize that product thought overlap will mediate the
influence of the ARP on the IRP and, thus, subsequent deal evaluations.
To illustrate our hypothesis, consider the process model in figure 1. A consumer
encounters an advertisement for blue jeans of “Regularly $119.99, Sale $39.99” (see left side of
figure 1). Inherent in such an advertisement is an offer price ($39.99) that the consumer
recognizes as a bona fide price. That is, the consumer understands that she can purchase the blue
jeans at that price. Also in this advertisement is the ARP ($119.99), which represents an explicit
claim on the part of the seller that the jeans he is selling for $39.99 have a value of $119.99.
Because the OP is a bona fide market price, a consumer should logically anchor on it. The
critical issue is the effectiveness of the ARP claim in shifting the IRP toward the ARP, thereby
enhancing subsequent consumer deal evaluations. To evaluate the claim, we hypothesize that the
consumer simultaneously considers jeans at both price points (the ARP and the OP). Consistent
with Norm Theory (Kahneman and Miller 1986), we posit that the consumer considers a pair of
blue jeans in the context of the $119.99 ARP (see top of fig. 1). The ARP acts as a mental probe
in evoking a set of exemplars (e.g., jeans A, B, C, D) and an associated set of product-related
thoughts (attributes, benefits, usage situations) that are characteristic of these exemplars (e.g.,
denim, high end buttons and zippers, comfortable, fashionable, snobbish, dress occasions A, C,
F, and G). We posit that the consumer also considers jeans in the context of the $39.99 OP (see
bottom side of fig. 1). The OP acts as a mental probe in evoking a set of exemplars (e.g., jeans E,
F, G, and H) and an associated set of product-related thoughts that are characteristic of these
exemplars (e.g., denim, comfortable, fading, rugged, dress occasions A, B, C, and E). The thesis
of the present investigation is that the influence of the ARP claim on the IRP and deal
evaluations is mediated by the degree of overlap in the sets of product-related thoughts evoked
by the ARP and OP (see shaded region of fig. 1). The region of overlap is the mediator we label
“product-thought overlap.”
-------------------------------------------------------Insert figure 1 about here
------------------------------------------------------We should note that our hypothesis is silent as to if the exemplars evoked by the ARP
and OP come from a single product category (e.g., moderately-priced jeans) or two different
product categories (e.g., value-priced jeans, fashion jeans). To some degree, this would be
contingent on how product category boundaries are conceptualized and is not germane to our
prediction. Rather, our prediction focuses on the overlap in product-related thoughts associated
with the two sets of exemplars, regardless of the category or categories in which the evoked
exemplars reside. This perspective is consistent with Norm Theory where Kahneman and Miller
(1986, 137) note “the probe recruits an evoked set of individual elements (exemplars), each of
which is described by several features. The evoked set is described by an aggregate of individual
features.” Consequently, it is the set of individual exemplars that the probes (ARP and OP,
respectively) recruit from memory that define the evoked sets. Thus, for our hypothesis, the
critical issue is the overlap in the product-related thoughts associated with the respective
exemplars – from whatever categories those exemplars happen to reside, be it the same or
different categories.
THEORY AND HYPOTHESES
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Background
Adaptation-Level (Helson 1964) and Assimilation-Contrast (Sherif et al. 1958) Theories
are most often cited as principle frameworks to account for the process by which ARPs influence
consumer responses (Lichtenstein and Bearden 1988, 1989; Monroe 1979, 1990; Urbany et al.
1988). Adaptation-Level Theory advances the simple notion that consumers have or construct
internal reference prices (IRPs) that serve as neutral reference points for the evaluation of selling
prices for products under purchase consideration. According to this theory, prices below/above
the IRP are viewed more/less favorably. Thus, to the extent there are factors that exert influence
on the IRP, these factors will necessarily affect price evaluations.
Principles from Assimilation-Contrast Theory have been applied to the domain of price
perception in a complementary fashion to account for the process by which the introduction of
price anchors influences IRPs. As the offering price (OP) represents a bona fide market price, it
is hypothesized to be a very strong anchor in influencing the level of consumers’ IRP. In cases
where OP is accompanied by a second price anchor, i.e., the ARP, consumer IRPs may
assimilate towards to the ARP making the OP appear more favorable. Couched in terms of
Assimilation-Contrast Theory, assimilation effects are evidenced when the introduction of a
context stimulus (e.g., an ARP) is used as a perceptual anchor such that the target stimulus (e.g.,
an IRP for the target product) is perceived as closer to the context stimulus than had the
contextual stimulus not been present (Herr, Sherman, and Fazio 1983; Lynch, Chakravarti, and
Mitra 1991). For example, if exposure to an ARP/OP of “Was $119.99, Now Only $39.99”
results in a higher IRP than would be the case after exposure to an OP of “Now Only $39.99,”
that would be evidence that the IRP was assimilated towards the ARP. As noted in the
introduction, there is an abundance of research showing that ARPs influence purchase
evaluations via their assimilative influence on IRPs. Consequently, we offer the oft-supported
H1–H2 (shown in fig. 2a) only as a baseline for testing for the hypothesized mediators and
moderators in H3-H7.
H1: ARPs exert a positive (assimilative) influence on IRPs.
H2:
IRPs exert a positive influence on consumer evaluations of the deal.
-------------------------------------------------------Insert figure 2a and figure 2b about here
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The Mediating Role of Product Thought Overlap
A focus on product category differences. Herr and his colleagues (Herr 1989; Herr et al.
1983) contend that the extent of feature overlap between a context cue (e.g., a context product)
and a target object (e.g., a second, focal product) determines whether assimilation occurs. To the
extent features of the context cue and the target object share considerable overlap, the target
object will be categorized as a member of the same category as that activated by the context cue,
and an assimilation effect is likely to occur.
Note that couched in terms of product perception, the definition of assimilation of Herr
and his colleagues entails the presence of both a context and target product. In an ARP scenario,
there is only one product. However, according to Norm Theory (Kahneman and Miller 1986),
when evaluating a single product, the product is evaluated against the evoked set of exemplars
that the product itself recruits from memory. For example, when evaluating a purchase offer for a
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pair of jeans for $39.99, the $39.99 pair of jeans is hypothesized to act as a probe in recruiting
alternative exemplars from memory and this evoked set of exemplars serves as the context set for
evaluating the purchase offer for the target jeans. In cases where the features of the target
product are perceived to have more overlap with the recruited exemplars, the target object is
assimilated into the set of evoked exemplars and evaluated as a member of this set (Schwarz and
Bless 1992).
Extending this explanation to the case of a single product with two prices (the OP and
ARP), we hypothesize that the consumer evaluates the product separately in the context of both
prices. Thus, by the same process described above, the result is two sets of recruited exemplars,
one for the product at the level of the OP and one for the product at the level of the ARP.
Whereas Schwarz and Bless (1992) speak to the overlap in features between the single target
object and the recruited exemplars, our theory speaks to overlap between two sets of productrelated thoughts, one associated with recruited exemplars at the level of the ARP, the other
associated with recruited exemplars at the level of the OP (see shaded area of fig. 1). To the
extent there is more/less overlap in the these two sets, we hypothesize that the consumer will
perceive the ARP to be more/less relevant to the product offered for sale at the OP, resulting in
stronger/weaker assimilation of the IRP for the target product to the ARP.
This noted, a central premise of the present research is that for a given ARP and OP, the
degree of overlap in product related thoughts associated with exemplars recruited by the ARP
and OP varies naturally across product categories, and therefore can be manipulated via product
category. For product categories where a given ARP and OP evoke more thought overlap, there
will be stronger assimilation of the IRP toward the ARP (H3) which will result in more favorable
deal evaluations (H4).
H3: The assimilative influence of ARPs on IRPs noted in H1 is moderated by product
category. Specifically, assimilation will be greater for product categories in which
consumers perceive more (versus less) overlap in product related thoughts evoked
by the ARP and OP.
H4: The interaction of product category with ARP presence has an indirect effect on
deal evaluations through IRPs.
A focus on similarities vs. differences within product category. We also predict that
holding product category constant, upon exposure to a given ARP and accompanying OP, people
who have a mindset to focus on similarities as opposed to a mindset to focus on differences will
perceive more overlap in product-related thoughts at the level of the ARP and OP, and thus
demonstrate larger assimilation effects of the IRP toward the ARP. This prediction follows
Strack and Mussweiler’s (1997) theoretical account of anchoring, a phenomenon defined as “the
assimilation of a numeric estimate towards a previously considered standard” (Mussweiler 2002,
67). According to their Selective Accessibility Model, Strack and Mussweiler (1997)
conceptualize the anchoring phenomenon as dependent on two fundamental principles of social
cognition research: hypothesis-consistent testing and knowledge accessibility. The former
principle rests on the premise that when mentally testing a hypothesis, people are prone to
attempt to confirm, rather than disconfirm, the hypothesis (Trope and Liberman 1996). In so
doing, they are more likely to recruit hypothesis-consistent information than hypothesisinconsistent information, giving hypothesis-consistent information an accessibility advantage in
memory. Consequently, when people are exposed to some standard and then asked to provide a
response in the domain of that standard, information consistent (rather than inconsistent) with
that standard is more likely to be recruited and used as a basis for developing responses. As such,
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people are said to use the previously encountered standard as an “anchor” in formulating
subsequent responses.
Strack and Mussweiler (1997) test their Selective Accessibility predictions of anchoring
using the two-question sequence developed by Tversky and Kahneman (1974). The first question
presented to participants is one that is comparative in nature (e.g., “Is the Mississippi River
longer or shorter than 10,000 miles?”). This question is followed by a second that is absolute in
nature (e.g., “How long is the Mississippi River?”). A very robust finding is that the value
provided to participants in the comparative question (e.g., 10,000 miles) acts as an anchor in
influencing their response to the subsequent absolute question such that higher values in the
comparative question result in higher response values to the absolute question.
Mussweiler (2002) notes that the phenomenon of hypothesis-consistent testing may result
from a basic human propensity to focus on similarities rather than differences whenever two
entities are compared (Gentner and Markman 1997). As such, Mussweiler suggests that any
contextual factor that promotes/inhibits a focus on similarities should result in stronger/weaker
assimilation effects. As a test of this prediction, Mussweiler (2002) had participants engage in
two seemingly unrelated tasks. In the first task, he primed similarities or differences by having
one half of the participants list all the similarities they could identify between two scenes, and
the other half list all differences. After completing this task, they completed the comparativeabsolute two-question anchoring task. Consistent with the hypothesis that the value provided in
the comparative question would recruit more value-consistent information when preceded by a
similarity prime than a difference prime, Mussweiler found stronger assimilation effects under
the similarity prime.
Of relevance to the current research, there is reason to believe that the moderating
influence of a similarity mindset on the strength of assimilation effects as theorized by
Mussweiler acts in a manner consistent with how a similarity mindset would influence the
strength of assimilation effects as theorized in the present research. That is, to the extent that
consumers focus on similarities/differences between sets of product exemplars that come to mind
when considering product versions at the level of the ARP and OP, we hypothesize there will be
more/less overlap in product-related information available in memory (H5), which in turn will
result in a larger/smaller degree of assimilation of the IRP to the ARP (H6). This implies a
sequential mediation in which product-related thought overlap and IRP mediate the relationship
between consumer mindset when exposed to an ARP (a mindset of similarities vs. differences)
and deal evaluations (H7) (see fig. 2b).
H5: Consumers primed to focus on similarities in the presence of an ARP will
perceive more thought overlap than those who are not primed to focus on
similarities.
H6: Consumers who perceive more thought overlap will show stronger assimilation of
the IRP toward the ARP than consumers who perceive less thought overlap.
H7: A focus on similarities exerts a positive indirect effect on deal evaluations through
an increase in product related thought overlap which itself increases IRPs.
We test H1-H4 (fig. 2a) in experiment 1 using product categories pretested to vary in
degree of product thought overlap at a given level of ARP and OP. Evidence consistent with H3
will be provided if there is more assimilation of the IRP to the ARP for product categories
pretested to have more product thought overlap at the levels of the given ARP and OP. We test
H5-H7 (fig. 2b) in experiments 2 and 3 by: (1) manipulating managerially controllable variables
within product category that prime a focus on similarities vs. differences in the presence of an
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ARP, thereby affecting product thought overlap, and (2) measuring product thought overlap in
order to assess its mediating role in influencing the IRP and deal evaluations as hypothesized in
H5-H7. Evidence consistent with H5-H7 will be provided if there is a higher level of thought
overlap for the similarity (vs. difference) mindset condition, resulting in stronger assimilation of
the IRP to the ARP, thereby influencing more favorable deal evaluations.
At this point, it may be helpful to note that the models in figure 2a and 2b both represent
conceptual models used to provide evidence for our process model shown figure 1 (Spencer,
Zanna, and Fong 2005). Specifically, the model provided in figure 2a represents a test of our
presence of an ARP  product thought overlap  assimilation of IRP  deal evaluations
sequence process depicted in figure 1 via a “moderation-of-process” approach in which the
hypothesized mediator (depicted by the dashed box in fig. 2a) is not directly measured. However,
as product category is hypothesized to moderate the influence of ARPs via this unmeasured
variable, evidence of moderation will be taken as evidence consistent with the process model
(see Spencer et al. 2005). The necessary conditions specified by Spencer el al. (2005) for taking
these moderation-of-process results as evidence of mediation for an unmeasured mediator are
discussed further in the discussion of experiment 1 results. The model provided in figure 2b
represents a test of this same process via a “measurement-of-mediation” approach where the
hypothesized mediator is directly measured.
PILOT STUDY
In order to test H3 and H4 (in conjunction with H1-H2), it was necessary to find product
categories that (1) vary in the degree to which the set of product related thoughts evoked at a
given OP and ARP overlap and (2) would be relevant to a student-subject population such that
product knowledge would be accessible in memory. We hypothesized that product categories
that exhibit more discernible, tangible, and identifiable attribute differences between product
versions at the level of the ARP and at the level of the OP would also exhibit larger differences
in overlap in product-related thoughts at the two price levels. For example, our a priori belief was
that jeans, a wardrobe staple for students, provide similar functionality whether they are high or
low priced (and thus should possess high thought overlap at the two price levels). However,
cordless phones should have differing functionality based on price (and thus should possess low
thought overlap at the two price levels).
While we sought student-relevant product categories that would all have product versions
at one common ARP and OP level, the given ARP/OP level we selected needed to have an ARP
that exceeded the OP by an amount large enough that it would allow for participants to perceive
more or less thought overlap. Based on our review of the literature, the largest percentage that we
found an ARP to be manipulated relative to an OP was by a factor of 2.86 (Urbany et al. 1988).
Even at this “implausibly high” level, the ARP was found to influence consumer price
perceptions. Considering these factors, we selected the 15 product categories (see table 1), and
tested them at an OP of $39.99 and an ARP of $119.99 (i.e., an ARP that exceeded the OP by a
factor of 3).
-------------------------------------------------------Insert table 1 about here
------------------------------------------------------For each of the 15 product categories, we created a survey that had the following
introduction:
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Imagine a (product name inserted here, e.g., “pair of jeans”) that normally sells for
$39.99. Think of all the features that you might expect (product name inserted here) at
this price to have.
Now, imagine a second (product name inserted here) that sells for $119.99. Think of all
the features that you might expect (product name inserted here) at this price to have.
After being prompted to consider these two product/price combinations, participants responded
to the following product feature overlap question, “How many features would you expect the two
(product category) to have in common?,” anchored by “very few” and “very many.” Next was a
seven-point knowledge question, “Compared to other college students you know, how
knowledgeable do you think you are about (product category)?,” anchored by “Less
Knowledgeable Than Others – More Knowledgeable Than Others.”
For ease of survey administration, we split the 15 products into three groups of five
products so that each pretest participant responded to only five of the 15 product categories.
Surveys presenting each of the three sets of five product categories were administered to a group
of n = 24 pretest participants (total pretest n = 72). Based on the pretest results, we sought to
identify four product categories to carry through to experiment 1; two that could be characterized
as low feature overlap and two that could be characterized as high feature overlap. We also
wanted to have categories for which the mean level of subject knowledge was high. In addition,
to the extent possible, we strived to ensure that feature overlap would not be confounded with
other variables (e.g., the low feature overlap conditions also being more or less technological
than the high overlap condition).
Based on the results of this pilot study, we selected backpacks and cordless telephones as
the low feature overlap product categories and sunglasses and jeans as the high feature overlap
categories. As shown in table 1, the measure of overlap is among the highest for sunglasses and
jeans, and much lower for backpacks and cordless phones. Further, product knowledge ranks
first, second, third, and fifth highest for these four categories. Finally, the two product categories
that operationalize the two product overlap conditions do not appear to be obviously confounded
with some other salient product category classification with perhaps one exception; the high
overlap items are more fashion related. However, there is always the possibility that some other
influence that we have not identified is confounded with product category (e.g., price variability
in the product category). This potential limitation is addressed in experiments 2 and 3.
EXPERIMENT 1
Method
Two hundred and twenty-eight undergraduates from a U.S. university were randomly
assigned to one of eight conditions in a 2 (ARP presence: ARP/OP vs. OP only) × 4 (product
category: jeans, sunglasses, backpack, cordless phone) between-subjects design. In the ARP/OP
condition, participants saw one of the four products offered at “Regularly $119.99, Store X Sale
Price $39.99.” In the OP only condition, they saw one of the four products at “Store X Sale Price
$39.99.” As noted above, two of the four product categories used in the present study
operationalized low feature overlap (backpack, cordless phone), and two operationalized high
feature overlap (jeans, sunglasses). Experimental sessions were run in groups of 12-16 and
participants were randomly assigned to one of the eight experimental conditions within the
experimental session. Participants were told that the experimenters were working with a local
retailer “who is interested in gathering information on how consumers perceive prices on
products that they may discount in the future, depending on feedback gathered in this survey.”
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The instructions stated that the brand would be referred to as “Brand ABC” and the retailer
would be referred to as “Store X” in order to maintain anonymity. Participants were then
instructed to turn to the next page where they saw an advertisement for one of the four products.
Below the ad image, participants saw either “Regularly $119.99, Store X Sale Price $39.99” or
“Store X Sale Price $39.99.” In order to allow for variance in perceptions of product thought
overlap, no product descriptions were included in the advertisements. The ARP/OP ad for the
backpack is provided as appendix A.
After considering the price advertisement, participants turned to the next page and
responded to a range of dependent variables. The first variable assessed average selling price
perceptions and was used to operationalize the IRP (Grewal et al. 1998; Mazumdar, Raj, and
Sinha 2005; Urbany et al. 1988). Specifically, participants in the ARP condition saw the
following:
There are 16 different department and specialty stores in the (city) area that sell the
(product category) identical to that shown on the previous page, one of which is Store X.
The (product) is offered on sale at Store X for $39.99. According to Store X’s
advertisement, the regular selling price of the Brand ABC (product) is $119.99.
Suppose you checked prices at the remaining 15 stores that sell this same (product). What
would you assume to be the average selling price of the (product) at the 15 stores?
For participants in the OP only condition, the third sentence in this average selling price measure
was deleted.
Then participants responded to a series of four 7-point scales designed to assess the value
of the price offer (“I believe the (product) would be a good value at the advertised selling price
of $39.99,” anchored by “Disagree” and “Agree”), attitude toward the offer (“My attitude toward
the advertised deal is,” anchored by “Unfavorable” and “Favorable”), perceived quality (“The
quality of this (product) is,” anchored by “Poor” and “Excellent”), and purchase intentions (“If
you were in the market to purchase a (product) in the price range of $40.00 (for yourself or as a
gift), how likely would you be to purchase the advertised (product),” anchored by “Very
Unlikely” and “Very Likely”) (Lichtenstein et al. 1991). A principle components analysis
showed that these four scales loaded on a single factor (eigenvalue = 2.46) explaining 62% of
variance in the data. Thus, we combined the four items into an additive multi-item scale to
operationalize evaluations of the deal (α = .79).
Results
Prior to analyses, we removed data from fourteen participants who responded to the
average price perception measure (IRP) with a value of less that $1 or greater than the ARP of
$119.99, resulting in an effective sample size of 214. An analysis of variance indicated that
within each of the two overlap conditions, product category did not interact with the price
manipulation in affecting either of the dependent variables. Thus, in order to formally test H1H4, we collapsed product category cells to create a high overlap (jeans and sunglasses) category
that was coded as 1 and a low overlap (backpack and cordless phone) category that was coded as
0. For the ARP presence manipulation, the OP only condition was coded as 0 and the ARP/OP
condition was coded as 1. Cell sizes, means, and standard deviations for both dependent
variables across the eight experimental cells are shown in table 2.
-------------------------------------------------------Insert table 2 about here
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Internal Reference Price. A two-way analysis of variance revealed a significant
interaction between product category and ARP presence on internal reference price (F(1, 210) =
3.70, p = .056; see fig. 3a) such that the difference in internal reference prices between the OP
only and ARP/OP conditions was greater for products with high overlap (MOP only = 40.79,
MARP/OP = 75.44) than the difference in internal reference prices for products with low overlap
(MOP only = 41.66, MARP/OP = 67.73). That is, ARPs had a greater assimilative influence for high
overlap product categories than low overlap product categories. In addition, the difference
between the high and low overlap product categories was significant in the ARP/OP condition
(F(1, 210) = 6.09, p = .014), but not in the OP only condition (F(1, 210) = .074, p = .785). This
suggests that the effect of product category influenced the assimilation of the internal reference
price to the ARP, and not internal reference prices in general.
Deal Evaluation. A two-way analysis of variance on deal evaluations also revealed a
significant interaction between product category and ARP presence (F(1, 210) = 6.12, p = .014;
see fig. 3b) such that the difference in deal evaluations between the OP only and ARP/OP
conditions was greater for products with high overlap (MOP only = 16.38, MARP/OP = 21.33) than
for products with low overlap (MOP only = 17.08, MARP/OP = 18.84). In addition, the difference
between the high and low overlap product categories was significant in the ARP/OP condition
(F(1, 210) = 7.65 p = .006), but not in the OP only condition (F(1, 210) = .56, p = .453),
suggesting that product category only influenced deal evaluations when an ARP was present and
not more generally.
-------------------------------------------------------Insert figure 3a and 3b about here
-------------------------------------------------------Mediation. Next, we tested for mediational evidence. The relationships predicted in H1H4 jointly represent mediated moderation, as shown in figure 4. That is, the effect of the ARP
manipulation on evaluations of the deal is moderated by the product category, and this
moderation is mediated by the internal reference price. As such, we followed procedures
consistent with Hayes (forthcoming) and Preacher and Hayes (2008) for jointly testing these
hypotheses. The ARP presence x product category interaction positively influenced internal
reference prices (b = 8.59, t(210) = 1.92, p = .056), which in turn positively influenced deal
evaluations (b = .10, t(209) = 5.07, p < .001; see table 3 for regression results). The indirect
effect of the ARP presence x product category interaction on deal evaluations through internal
reference price was 0.82 (95% CI: .058 to 1.901). There was also a significant direct effect of the
interaction on deal evaluations (b = 2.37, t(209) = 1.92, p = .056). We conclude that the
moderation of the effect of ARP on deal evaluations by product category is mediated by the
internal reference price.
-------------------------------------------------------Insert table 3 and figure 4 about here
------------------------------------------------------Discussion
Across four product categories, experiment 1 provides evidence consistent with the
hypothesis that the degree of influence that an ARP exerts on consumer price response is
dependent on the degree of overlap in (1) the set of product-related thoughts associated with
versions of the product at the level of the ARP and (2) the set of product-related thoughts
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11
associated with versions of the product at the level of the OP. Consistent with our hypothesis,
greater assimilation effects were found for product categories where the overlap was greater.
We note that the evidence of process of the mediating role of product thought overlap
was provided without direct assessment of the mediator. Rather, we provide evidence of
mediation using a “moderation of process” approach. Spencer et al. (2005, p. 847) argue that
moderation of process designs provide strong evidence of a psychological process if they meet
two assumptions. First, there has to be evidence that the moderator does indeed affect the
proposed psychological process, and second, that the only way that the proposed moderating
variable exerts its influence is through its influence on the unmeasured mediator. That is, there
can be no alternative explanation for the observed moderation.
Regarding the first criterion, the hypothesized moderation was obtained. Regarding the
second criterion, consideration of several factors suggests that this criterion was also met. First,
the product categories that served as the high and low product overlap operationalization were
specifically pretested and selected on the criterion of high and low overlap. Second, while it is
possible that some other variable was also manipulated by our product category
operationalization (e.g., high overlap items being more fashion-oriented and low overlap items
being more functional, high or low overlap categories having more/less price variability within
the product categories) and is responsible for the moderation we find, the nature of the
interaction we obtained makes this rival account unlikely. Specifically, there were no product
category differences in the OP only condition; product category differences existed only in the
ARP/OP condition where assimilation effects were more pronounced for the high overlap
product categories than the low overlap product categories. Thus, any alternative explanation
would have to be able to account for this interaction.
EXPERIMENT 2
In experiment 2, we wanted to investigate the possibility that a third variable, related to
product category, was responsible for the effects found in experiment 1. Our approach to
addressing this possibility was to use a single product category and given level of ARP and OP
while manipulating executional ad elements designed to prime the perception of “similarities”
(hence, high overlap) or “differences” (hence, low overlap) between the exemplars brought to
mind by the ARP and OP, respectively. Additionally, we directly measured thought overlap and
tested its mediating role in the relationship between the manipulated condition (similarity vs.
difference mindset when viewing an ARP) and downstream consumer responses (i.e., internal
reference prices and deal evaluations). This allowed us to assess mediation using a
“measurement of mediation” approach. The conceptual model we used to assess measurement of
mediation is provided in figure 2b.
Method
One hundred and twenty residents of the United States were recruited from mTurk and
paid a small amount to participate in this experiment. The experiment was a simple two cell
design where two versions of an ARP advertisement were created to prime either a similarity or
difference mindset. Males viewed an advertisement with a male model while females viewed an
advertisement with a female model. The gender of the model in the advertisement was matched
to the gender of the participant to increase relevance of the ad and involvement in the purchase
scenario and was not of theoretical interest. There were no significant differences between males
and females nor were any of the gender by prime interactions significant, so all reported analyses
were conducted collapsing across gender.
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12
On the first screen, participants read the same cover story as in experiment 1. The second
screen showed the advertisement containing the manipulation (see Appendix B for the female
similarity prime version of the advertisement and the male difference prime version of the
advertisement). Principles of perceptual organization propose that people are more likely to
perceive two objects as a single unit when they are proximally located, share a common region
and are similar in shape and color (Palmer and Rock 1994). Therefore, to promote a mindset of
similarity, we designed an advertisement in which the ARP and OP were displayed in close
proximity with a box around them and used the same font style and color. We further primed
similarity by placing the following slogan in the advertisement: “The same classic construction
that you’ve always known. Because good things stay the same.” To promote a focus on
differences, the ARP was placed further apart from the OP (the ARP was located above the
photo while the OP was located below the photo), there was no box around the two prices, and
the two prices used different font colors and styles. The tagline for this advertisement further
primed differences by using the following text “A uniquely different construction from what
you’ve known in the past. Because being different is what our jeans are all about.”
After viewing this advertisement, participants responded to the dependent variables of
interest. First, they responded to the same average price operationalization of the IRP used in
experiment 1.Participants next provided their evaluations of the deal. This was assessed using the
value of the price offer and attitude toward the price offer items from experiment 1, and a new
item assessing how long participants believed the sale would be on offer for (“Based on your
best guess, how long do you think a retailer would offer a sale such as the one above?” anchored
by “Very small amount of time” and “Very large amount of time”). We included this measure on
the belief that if participants believe a deal is particularly good, they will believe it to be offered
for a limited time only. This measure is consistent with “time tests” many enforcement agencies
across states (e.g., Massachusetts) and countries (e.g., Canada) employ to operationalize if an
ARP is a bona fide bargain (Kaufman et al. 1994). That is, if the ARP is offered beyond some
specified period of time, it may be legally deceptive, hence not a good deal. These three items
were combined into an additive multi-item scale to operationalize deal evaluations (α = .71).
(Recognizing that this operationalization of deal evaluation is partially different from that
employed in experiment 1, we employ both in experiment 3 to demonstrate equivalency.)
Following this, we assessed perceived feature overlap associated with ARP and OP.
While viewing the ARP/OP “Regularly $119.99, Now Only $39.99” advertisement, participants
were provided with a list of 15 product features and told “A typical $120 pair of jeans has the
features listed below.” From this list, they were then asked to check off the features they believe
are also possessed by the pair of jeans available at the $39.99 OP shown in the advertisement.
We summed the number of features checked off to create a measure of product-thought overlap.
Results
Prior to analyses, we removed data from eight participants who indicated that they were
familiar with or had taken a similar survey previously (we had conducted a similar survey for a
study reported in a prior version of this paper), four participants who spent less than five seconds
on each webpage in the survey, one participant who spent more than 32 minutes (more than 10
SD from the mean) on the page assessing internal reference price and 1 participant who spent
more than 16 minutes on the page assessing feature overlap (more than 10 SD from the mean).
Thus, 106 participants were used in this analysis. Cell sizes, means and standard deviations are
reported in table 4.
--------------------------------------------------------
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13
Insert table 4 about here
------------------------------------------------------We proposed that a similarity mindset would lead to greater thought overlap than a focus
on differences, and that this thought overlap would in turn influence average price perceptions
(the IRP) and subsequently deal evaluations (see fig. 2b). To assess whether the relationship
between the manipulated condition and deal evaluations was mediated by a sequential process
that influenced thought overlap and subsequently the IRP, the PROCESS macro (model 6) was
used (Hayes, forthcoming; Hayes and Preacher, forthcoming). The mediation model tested is
provided in figure 5. We coded the low overlap condition (difference prime) as 0 and the high
overlap condition (similarity prime) as 1.
Participants who were exposed to the similarity prime perceived marginally more feature
overlap than those who were exposed to the difference prime (b = 1.20, t(104) = 1.75, p = .083;
see table 5 for regression results), those who perceived more feature overlap had higher internal
reference prices (b = 1.86, t(103) = 3.41, p < .001) and those with higher internal reference prices
had more positive evaluations of the deal (b = .06, t(102) = 3.13, p = .002). Although the total
effect of prime on deal evaluations did not reach conventional levels of statistical significance (b
= 1.16, t(104) = 1.59, p = .116), we nonetheless find support for our proposed mediational
process (Hayes, forthcoming; Zhao, Lynch, and Chen 2010). A bias-corrected bootstrap
confidence interval for the indirect effect of prime on deal evaluations through thought overlap
and then internal reference price based on 5,000 bootstrap samples was .12 (95% CI: .0057 to
.4488). There was no evidence that the similarity versus difference prime influenced deal
evaluations independent of the effect of thought overlap and internal reference price (b = .31,
t(102) = .43, p = .665). We conclude that relative to those in the low overlap condition, those in
the high overlap condition perceived more feature overlap, which in turn was associated with a
higher internal reference price and that this increase in internal reference price translated into a
more favorable deal evaluation.
-------------------------------------------------------Insert table 5 and figure 5 about here
------------------------------------------------------Discussion
These results support our hypothesis that the ARP exerts a stronger influence on the IRP
when people perceive greater overlap in product related thoughts, and that this increase in the
IRP subsequently influences deal evaluations. We held constant the product category and
manipulated aspects of the advertisement to promote a similarity versus differences mindset.
Thus, results of the first two experiments, the first using a moderation-of-process approach, the
second using a measurement-of-mediation approach (Spencer et al. 2005), provide convergent
evidence that the degree of overlap in product-related thoughts mediates the degree to which
IRPs are assimilated toward ARPs. Whereas experiment 1 entailed a more inductive approach in
which we conducted a pilot study to find products that participants believed to vary on overlap,
experiment 2 used a more deductive approach where a similarity vs. difference mindset in the
presence of an ARP was manipulated, resulting in downstream effects on product thought
overlap, the IRP, and finally deal evaluations.
Experiment 2 results provide added confidence that product-related thought overlap, and
not some other variable, mediates the influence of an ARP on an IRP and, subsequently, the
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14
evaluation of the deal. Our next experiment seeks to provide additional evidence for this process
by manipulating another managerially controllable variable that has direct implications for
perceived overlap. For instance, consider that a reference price offer for one product (the focal
product in this example) is frequently accompanied by a reference price offer for a second
product in the same advertisement (the context product). We hypothesize that the relationship of
the context product to the focal product can influence the degree of thought overlap for the focal
product.
To expand on this idea, a context product (e.g., dining table A) may either be a substitute
for the focal product (e.g., dining table B), a complement (e.g., dining chairs matching dining
table B) to the focal product, or a product that is unrelated (e.g., a nonmatching rug) to the focal
product. By definition, substitute and complement products are related (i.e., there is some level
of cross-elasticity), whereas unrelated products are not (i.e., cross-elasticity is zero). When
consumers are asked to evaluate an ARP and OP on a focal product (e.g., dining table B), the
presence of another related product should put consumers in a relationship mindset that
encourages a similarity focus and, thus, encourages higher levels of thought overlap. That is, the
presence of related products should encourage a person to think in terms of common and/or
shared attributes, benefits, and uses across products. This mindset should persist and be applied
to exemplars recruited at the levels of the ARP and OP for the focal product. Thus, to the extent
substitute and complement products encourage a focus on relationships across products whereas
unrelated products do not, the consumer should be put in a similarity and dissimilarity mindset,
respectively. As found in experiment 2, under a similarity mindset, the exemplars evoked by the
ARP and OP should be perceived as having more overlap and the ARP should more effectively
influence the internal reference price and deal evaluations.
EXPERIMENT 3
In experiment 3, we manipulated the relationship of a context product (substitute,
complement, unrelated) to the focal product of evaluation. Two other design and procedural
changes were made as robustness checks and/or to address alternative explanations for results of
the first two experiments. First, we used a deep discount (ARP/OP of $119.99/$39.99, a 3/1
ratio) in experiments 1 and 2 in order to allow for variability in product thought overlap and thus,
variance in the downstream consumer responses (the IRP and deal evaluations). However, as a
check on the robustness of the effect, we moved to an ARP/OP of $699/$549 (1.27/1 ratio) in
this experiment. In terms of prior literature, discounts in experiments 1 and 2 would be classified
as implausible, while that in experiment 3 would be classified as plausible (Krishna et al. 2002;
Urbany et al. 1988).
Regarding the second design change, because we were manipulating overlap between the
ARP and OP for the focal product (table) via the presence of an ARP/OP for a second related
(table, chairs) vs. nonrelated (rug) context product in the advertisement, we were concerned that
consumers may use the discount for the context product as a reference point in evaluating the
ARP/OP discount for the focal product, thereby exerting an additional influence on responses.
We could have addressed this by matching the discount for the context product with the focal
product, but we were concerned that would lack realism and raise suspicions. Therefore, we
decided to use ARP/OP combinations for the context products that were both less than, and
greater than, the ARP/OP discount for the focal product. Thus, if the discount offered for the
context product influenced responses to the focal product, our manipulation would capture this
influence. Consequently, the OP of the context product was varied: ARP of $699 and OP of $659
versus ARP of $699 and OP of $299.
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15
Method
Four hundred and twenty-eight residents of the United States (mean age = 34 years) were
recruited from mTurk and paid a small amount to participate in a 3 (relationship of the context
product to the focal product: substitute, complement, unrelated) x 2 (discount level on context
product: high [ARP/OP = $699/299], low [ARP/OP = $699/659]) online experiment. Participants
were randomly assigned to condition.
At the outset of the survey participants were told that:
We are working with a (City) area retailer who is interested in how consumers
perceive products that are on sale. The retailer wishes to remain anonymous and
for that reason the advertisement we are going to ask you to evaluate is very
simple and contains no identifying clues about who the retailer might be. We have
also substituted the name “Store X” for the actual name of the retailer.
The advertisement shows two products that are on sale. One of the products is the
"Santiago Dining Table." That is the product we are going to ask you about and
the one we want you to focus on in responding to the questions that follow.
Now please go to the next screen and view the advertisement for the Santiago
Dining Table, then answer the questions that follow.
Participants then saw an advertisement for two products, both with an ARP and OP. The
focal product, a dining table with an ARP/OP of $699/$549 was shown at the bottom of the
advertisement. This was constant across all conditions. At the top of the advertisement, the
context product – a dining table (substitute), chair set (complement), and rug (unrelated) varied
across conditions as did the ARP and OP for the context product. (See Appendix C for the three
versions of the context product manipulation for the high discount condition.) In the high
discount condition, the context product was offered at “Regularly $699, Sale $299,” whereas in
the low discount condition the ARP and OP was $699 and $659. Thus, the discounts across the
high and low levels of the context product were much higher and lower than the discount offered
on the focal product.
After viewing the advertisement, respondents advanced to the next screen. With both the
context and focal products and associated ARP/OPs still on the screen, participants provided an
internal reference price estimate modified to fit the cover story in experiment 3:
There are 3 different stores in the (city) area that sell the Santiago Dining Table
identical to that shown above, one of which is Store X. As you can see, the table
is offered on sale at Store X for $549.00. According to Store X’s advertisement,
the regular selling price of the Santiago table is $699.00. Suppose you checked
prices at the remaining 2 stores that sell the same Santiago table, knowing that
each of these stores can set its own price for the table. What would you assume to
be the average selling price of the Santiago table at those 2 stores? The average
price of the Santiago table would be: ____________.
Participants also responded to measures of overlap and evaluations of the deal. Again, both
products remained on the screen while these measures were taken. Regarding overlap,
participants responded to the following: “Select the image you feel best represents the similarity
in thoughts that come to mind when thinking about the Santiago Table that is being offered for
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16
sale at $549.00 and a dinner table that would typically sell for $699.00.” The seven-point scale
was a modified version of an item developed to measure the overlap in perceptions of one’s self
and a target organization (Bergami and Bagozzi 2000). The seven scale positions were
accompanied by two circles that were far apart on one end (and labeled as “far apart”) and by
two circles that were totally on top of each other at the other end (and labeled as “complete
overlap). The five intermediate scale positions showed circles that increasingly overlapped to a
larger degree, as in a Venn diagram, with each scale positions individually labeled (e.g., “close
together but separate,” “small overlap,” “large overlap”). Participants chose the set of two circles
that indicated their perceived overlap in thoughts.
Given the differing operationalizations of deal evaluations across experiments 1 and 2, in
experiment 3 we sought to provide evidence that results would be robust across both
operationalizations. Therefore, we assessed the five items used to form the two
operationalizations in experiments 1 and 2 (i.e., the four scale items used in experiment 1, and
the additional sale length scale item in experiment 2 that was combined with two of the four
items from experiment 1). As results were invariant across the experiment 1 and experiment 2
operationalizations, we only report results relevant to the same four-item operationalization used
in experiment 1. We provide results for the experiment 2 operationalization in the web appendix.
Results
To check whether the effect of the context product on thought overlap, internal reference
prices or deal evaluations differed depending on the discount level of the context product, we
first regressed these dependent variables on context product, discount level and the interaction of
context product by discount level. Context product did not interact with discount level in
affecting any of the dependent variables, so we collapsed across discount level and used a
planned contrast coding of the related (hypothesized to prime a similarity mindset) versus the
unrelated (hypothesized to evoke a differences mindset) context products (i.e., substitute = .5,
complement = .5, unrelated = -1) for further analyses. Cell sizes, means and standard deviations
are provided in table 6.
-------------------------------------------------------Insert table 6 about here
------------------------------------------------------The PROCESS macro (model 6; Hayes, forthcoming; Hayes and Preacher, forthcoming)
was used to assess whether the influence of the context product (related vs. unrelated) on deal
evaluations was mediated by a sequential process of product thought overlap and updating of the
focal product IRP. The mediation model tested is provided in figure 6. First, participants who
were exposed to a related context product perceived more feature overlap than those who were
exposed to the unrelated context product (b = .37, t(426) = 2.41, p = .016; see table 7 for
regression results). Second, participant who perceived more feature overlap had higher internal
reference prices (b = 6.63, t(425) = 2.33, p = .020). Third, participants with higher internal
reference prices had more positive evaluations of the deal (b = .006, t(424) = 7.62, p < .001). The
total effect of context product on deal evaluations was significant (b = .40, t(426) = 2.49, p =
.013). A bias-corrected, 5,000 sample bootstrap confidence interval for the indirect effect of
prime on deal evaluations through feature overlap and then internal reference price was .014
(95% CI: .0015 to .0383). There was no evidence that the relatedness of the context product
influenced deal evaluations independent of the effect of feature overlap and internal reference
price (b = .10, t(424) = .68, p = .499). We conclude that, relative to those in the unrelated context
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17
product condition, those in the related context product condition perceived more feature overlap,
which in turn was associated with a higher internal reference price and this increase in internal
reference price translated into a more positive deal evaluation.
-------------------------------------------------------Insert table 7 and figure 6 about here
-------------------------------------------------------
GENERAL DISCUSSION
The process of ARP perception has been the focus of much research over the past 35
years (see meta-analysis by Krishna et al. 2002). Within this time frame researchers have
adopted the three construct presence of an ARP  assimilation of IRP  deal evaluation
sequence as given, and have focused their efforts on identifying moderators of this process. This
approach has identified some useful insights. For example, semantic cues (Lichtenstein et al.
1991), past merchant promotion practices and the promotion history of competing merchants
(Lichtenstein and Bearden 1989), and individual differences in terms of consumer skepticism
(Urbany et al. 1988) are just a few examples of variables that have been shown to moderate the
ARP’s influence on the IRP, hence deal evaluations. Importantly, it was only by the
identification of the IRP as a mediator of ARP perception did variables that may moderate the
influence of the ARP by affecting the IRP become evident. Following this logic, a premise of the
present study is that identification of additional mediating variables operating within the
presence of an ARP  assimilation of IRP  deal evaluation sequence would increase the
chances of discovering other yet-to-be-identified moderators. Thus, the purpose of the present
investigation was to advance a new mediator of the process of ARP perception, that being
product-thought overlap.
Three studies provide support that product-thought overlap does indeed mediate ARP
perception; specifically, evidence supports a presence of an ARP  product thought overlap 
assimilation of IRP  deal evaluations sequence. The evidence comes from three studies of very
differing natures, the first one being a demonstration of “moderation-of-process,” the second two
being demonstrations of “measurement of mediation” (Spencer et al. 2005). Further, within the
second two studies, the operationalization of product-thought overlap was very different in
nature, yet results were consistent. The theoretical value of identifying this mediator is illustrated
by results of the three experiments. For example, based on results of experiment 1, we have
evidence that product category serves as a moderator of the ARP’s influence on IRPs and
subsequent deal evaluations via its influence on product thought overlap. Simply stated, ARPs
will be more effective when used with some product categories as opposed to others.
Identification of product thought overlap also led to the question motivating experiments 2 and 3
regarding if product thought overlap elicited by an ARP could be manipulated by context
variables within product category such that ARPs would be more or less influential. Results of
these two experiments provide affirmative support for this question. By the ad layout that
retailers use, and/or by the nature of accompanying products retailers place in an advertisement,
they can affect how successful an ARP will be for a focal product. Of note, the manipulations in
these studies would not have become apparent had the mediator not first been identified.
Implications for Theory
Although Assimilation-Contrast Theory is the most oft-cited theory for explaining the
influence of ARPs, limited theoretical elaboration exists on the process mechanisms behind this
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effect within the ARP domain. By linking the degree of product thought overlap evoked by
exemplars at the levels of the ARP and OP for a single product (in the present research) to
product feature overlap for two different products as theorized in basic assimilation-contrast
literature (see Herr 1989; Herr et al. 1983), our research connects the application of
Assimilation-Contrast Theory in the ARP domain to its more general theoretical domain,
allowing for process-related insights into consumer assimilation of ARPs. In so doing, we
provide the first empirical support in the pricing literature for a theoretically-based mediator
beyond the IRP of ARP influence.
We should note that finding assimilation of the IRP towards the ARP given the provision
of two external anchors, an ARP and an OP, differs in a fundamental way from the findings of
assimilation of Strack and Mussweiler (1997) and Mussweiler (2002) cited earlier. In those
studies, participants are provided with one external anchor in the “comparative” question (e.g.,
“Is the Mississippi River longer or shorter than 3000 miles?”) that influences responses to the
subsequent “absolute” question. Seemingly, given one anchor with relative uncertainty as to the
accuracy of that value to the unknown correct value (approximately 2,340 miles), there is
reasonable malleability in consumer response such that anchoring effects are observed. In the
present research, consumers encounter a bona fide OP that they know is valid and are provided
with an ARP claim where they know the retailer has a profit motive to make the ARP claim. In
some sense, we view our study context as one where, like Strack and Mussweiler (1997) and
Mussweiler (2002), participants are provided with an “influencing” anchor (the value in the
comparative question in their case, the ARP in our case), but one where the perception (IRP) we
are attempting to influence is more “nailed down” by the provision of a second bona fide anchor
(OP). Thus, relative to the study context of Strack and Mussweiler (1997) and Mussweiler
(2002), the present study setting would not seemingly lend itself to strong assimilation effects.
However, under contexts that promote a perception of high product thought overlap, the ARP
claim was effective in promoting the belief that the product’s average selling price (IRP) was
higher thereby resulting in more favorable deal evaluations of the product at the OP. Viewed in
this light, we see our findings of assimilation as even more meaningful.
Implications for Practice
While generalizing from lab studies to field settings is often a risky endeavor, to the
extent our findings do generalize, the implications appear fairly direct. The experiment 1 results
suggest that, assuming ARPs are indeed valid, marketers would be well advised to consider the
product categories for which they use ARPs. Using ARPs indiscriminately may lessen their
credibility, whereas restricting their use to a subset of products, those that generate higher levels
of overlap in product-related thoughts at the level of the ARP and OP, may result in higher levels
of consumer responsiveness. Also, the similarity-difference mindset manipulation in experiment
2 suggests some interesting possibilities. For instance, to the extent marketers can employ tactics
to put consumers in a “similarity” mindset, their ARPs may be more effective. On the other side
of the coin, our results provide evidence that the common marketer practice of differentiating an
advertised reference priced-product with ad slogans that focus on the word “difference,” may in
fact be having an adverse effect on consumer responses relative to wording that primes the
perception of similarity. Finally, while it is common for marketers to simultaneously promote
multiple products, the relationship for how the presence of one product may affect the perception
of another, to our knowledge, has not been investigated. Our results suggest that promoting
context products that share a relationship with the focal product, be it complement or substitute,
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19
may promote higher levels of product thought overlap, hence more favorable perception of
ARPs. However, because the present research was not conducted in a field setting, it is
premature to draw firm conclusions regarding the external validity of our findings.
Public Policy Implications
We believe that there are also public policy implications relating to our findings.
Consistent with much prior research and marketplace evidence, our research also supports the
significant influence that ARPs can have on consumer perceptions. For example, a comparison
of consumer responses between the ARP/OP and OP only conditions in Experiment 1, once
again, shows the influence that ARPs can have. However, this has been found numerous times
before and is “not news.” What is news is that the influence of the ARP was moderated by
product category (experiment 1) and influenced by perceptual context (ad layout in experiment 2,
context product in experiment 3). These findings have implications for the implementation of
“per se” rules for establishing criteria for a legal finding of deception for ARPs.
One of the authors has served as a testifying and consulting expert in several litigation
matters in both the U.S. and Canada relating to ARP deception. Across all cases, one issue that
arises is the standard of evidence needed to support a legal finding that a retailer has engaged in
deceptive ARP behavior. Arguments frequently center on the legal applicability of some “per se”
rule, or alternatively, a “rule of reason” approach. Per se rules impose an absolute threshold that
retailers must meet to ensure that they meet a standard of nondeceptive ARP behavior. For
example, Kaufmann et al. (1994) note that Massachusetts enacted ARP regulations in 1990
stipulating that an ARP is legitimate if at least 30% of the sales occurred at that price (i.e., a
volume standard) or if the ARP is established as the selling price for at least 15 days prior to the
reduction of the selling price to the new OP and that the item is not “on sale” for more than 45%
of a 180-day period (i.e., a time standard). Several other states and other countries (e.g., Canada)
have similar volume and time tests for per se rules. While these rules clearly give retailers set
criteria to meet in order to avoid being prosecuted for deceptive price advertising, and while they
also give those in the enforcement community a means to achieve efficient prosecution of
violations, we believe that these per se rules may not align in the most isomorphic manner with
consumer deception. Findings of the current study support this. Such “one size fits all” per se
rules would be totally insensitive to how consumers perceive ARPs differently across different
product categories (experiment 1) or contextual influences (experiments 2 and 3) that influence
consumer price perceptions. Consequently, findings from the present study would support the
adoption of a “Rule of Reason” approach to support a legal finding of deception. Such an
approach would require a focus on retailer operations and behavior, consumer perceptions,
industry characteristics, characteristics of the product in question, and contextual factors
surrounding the pricing behavior in order to make an informed finding regarding consumer
deception (Kaufmann et al. 1994). While this approach lacks the nicety of efficiency from an
enforcement perspective, the present study results suggest that it may align better with issues
relating to consumer perception and deception.
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APPENDIX A
Backpack Manipulation in Experiment 1
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APPENDIX B
Similarity (Female) Prime Condition in Experiment 2
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APPENDIX B (continued)
Difference (Male) Prime Condition in Experiment 2
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APPENDIX C
High Discount Complement Context Product Condition in Experiment 3
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APPENDIX C (continued)
High Discount Substitute Context Product Condition in Experiment 3
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APPENDIX C (continued)
High Discount Unrelated Context Product Condition in Experiment 3
*By mistake, our advertisement indicated that this circular rug had dimensions of 8 x 10.
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WEB APPENDIX
The experiment 3 data were reanalyzed using the composite evaluation measure from
experiment 2. The results remained largely unchanged because the two measures were correlated
at .93. Again, the hypotheses were tested using a planned contrast coding of the related versus
the unrelated context products (i.e., substitute = .5, complement = .5, unrelated = -1). For deal
evaluation, there was a significant main effect of the relationship of the context product (F(1,
422) = 8.73, p < .05), but a nonsignificant main effect of the discount level of the context product
(F(1, 422) = 1.81, p > .05) and the interaction of the two variables (F(1, 422) = 2.34, p > .05).
Test for the mediators showed similar patterns. For product thought overlap, there was a
significant main effect of the relationship of the context product (F(1, 422) = 5.99, p < .05), a
nonsignificant main effect of the discount level of the context product (F(1, 422) = .22, p > .05),
and a nonsignificant interaction (F(1, 422) = 1.57, p > .05). For the IRP, there was a significant
main effect of the relationship of the context product (F(1, 422) = 10.24, p < .05), a significant
main effect of the discount level of the context product (F(1, 422) = 20.70, p > .05), and a
nonsignificant interaction (F(1, 422) = 2.33, p > .05).
The PROCESS macro (model 6; Hayes and Preacher in press) was used to assess whether
the influence of the context product (related vs. unrelated) on deal evaluations was mediated by a
sequential process of product thought overlap and updating of the focal product IRP. First,
participants who were exposed to a related context product perceived more feature overlap than
those who were exposed to the unrelated context product (b = .37, t (425) = 2.41, p = .02).
Second, participant who perceived more feature overlap had higher internal reference prices (b =
6.63, t(424) = 2.33, p = .02). Third, participants with higher internal reference prices had more
positive evaluations of the deal (b = .007, t(423) = 7.60, p < .001).
A bias-corrected, 5000 sample bootstrap confidence interval for the indirect effect of
prime on deal evaluations through feature overlap and then internal reference price was above
zero (95% CI: .0026 to .0461). There was no evidence that the relatedness of the context product
influenced deal evaluations independent of the effect of feature overlap and internal reference
price (b = .21, t(423) = 1.31, p = .19). We conclude that, relative to those in the unrelated context
product condition, those in the related context product condition perceived more feature overlap,
which in turn was associated with a higher internal reference price and this increase in internal
reference price translated into a more positive deal evaluation.
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TABLE 1
Experiment 1 Pilot Study: Product Category Selection Results
Product Category
Jeans
Men's tie
Sunglasses
Men’s Sports watch
Tennis racket
Frying pan
Hiking boots
Camping tent
Backpack
Cordless telephone
Bookshelf
Coffee maker
Binoculars
Hockey jersey
Microwave oven
Product Knowledge
Mean
Product Feature Overlap
($39.99 vs. $119.99)
4.77
4.02
4.60
2.85
2.94
3.76
3.23
3.75
4.71
4.16
3.69
4.23
3.60
3.85
3.92
5.92
5.33
5.08
5.08
4.92
4.92
4.75
4.67
4.42
4.33
4.17
4.07
3.92
3.75
3.58
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TABLE 2
Experiment 1: Dependent Variable Cell Sizes, Means, and Standard Deviations
Dependent
Variable
Product
Category
Phone
Internal
Backpack
Reference
Jeans
Price
Sunglasses
Phone
Backpack
Deal
Evaluations Jeans
Sunglasses
N
28
25
26
26
28
25
26
26
OP ($39) Only
Standard
Mean
Deviation
43.28
14.91
39.84
9.24
42.69
8.97
38.88
12.28
16.96
5.41
17.20
4.59
17.77
4.27
15.00
5.71
ARP/OP ($119/$39)
Standard
N
Mean
Deviation
26
64.50
22.46
29
70.62
18.38
27
75.52
17.06
27
75.37
22.88
26
18.42
4.81
29
19.21
4.20
27
21.78
3.19
27
20.89
4.95
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TABLE 3
Experiment 1: Regression Analyses
Dependent Variable
Independent
Variable
ARP Presence
Product Category
Interaction
IRP
Constant
A1
A2
A3
b
26.07
-.87
8.59
-
IRP
SE
3.14
3.19
4.46
-
p
<.001
.785
.056
-
41.66
2.24
<.001
R2 = .48
F(3,210) = 63.59
C'1
C'2
C'3
B
Deal Evaluation
SE
b
p
-.73
.99
.461
-.61
.87
.486
2.37
1.23
.056
.10
.02
<.001
13.09
1.00
<.001
R2 = .24
F(4,209) = 16.23
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TABLE 4
Experiment 2: Dependent Variable Cell Sizes, Means and Standard Deviations
Dependent
Variable
Thought Overlap
Internal Reference
Price
Deal Evaluations
N
54
Difference Prime
Standard
Mean
Deviation
5.80
3.81
Similarity Prime
Standard
N
Mean
Deviation
52
7.00
3.81
54
60.57
18.16
52
72.94
22.99
54
14.28
3.74
52
15.44
3.82
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TABLE 5
Experiment 2: Regression Analyses
Independent
Overlap
Variable
SE
b
p
Prime
A1 1.20 .69 .083 A2
Overlap
D21
IRP
Constant
5.80 .48 <.001
R2 = .03
F(1,104) = 3.07
Dependent Variable
IRP
SE
b
p
10.12 3.88 .011
1.86
.55 <.001
49.77 4.15 <.001
R2 = .18
F(2,103) = 11.05
C'
B1
B2
Deal Evaluation
SE
b
p
.31
.72
.665
.13
.10
.200
.06
.02
.002
10.13 1.16 <.001
R2 = .15
F(3,102) = 6.15
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TABLE 6
Experiment 3: Dependent Variable Cell Sizes, Means, and Standard Deviations
Dependent Discount
N
Variable Level
On sale
70
Thought $299
Overlap
On sale
69
$659
On sale
Internal
70
$299
Reference
On sale
69
Price
$659
On sale
70
Deal
$299
Evaluations On sale
69
$659
Unrelated
Standard
Mean
Deviation
N
Substitute
Standard
Mean
Deviation
Complement
Standard
N Mean
Deviation
3.13
1.54
74
2.99
1.49
74
3.64
1.54
2.94
1.25
75
3.48
1.47
66
3.53
1.54
533.67
135.66
74 577.03
82.58
74 573.80
72.41
589.30
75.83
75 601.35
72.50
66 606.85
49.27
3.57
1.77
74
3.60
1.54
74
3.86
1.42
3.38
1.49
75
4.13
1.67
66
3.91
1.45
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TABLE 7
Experiment 3: Regression Analyses
Independent
Overlap
Variable
SE
b
p
Context A1 .37
.15 .016 A2
Overlap
D21
IRP
Constant
3.04 .13 <.001
R2 = .01
F(1,426) = 3.07
Dependent Variable
IRP
SE
b
p
25.59 9.04 .005
6.63 2.84 .020
541.16 11.35 <.001
R2 = .03
F(2,425) = 7.60
C'
B1
B2
Deal Evaluation
SE
b
p
.10
.14
.499
.40
.04 <.001
.01 .001 <.001
-.94 .44
.034
R2 = .28
F(3,424) = 55.45
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FIGURE 2
Conceptual Models for Studies 1-3 for the Mediating Role of Product Thought Overlap on
Advertised Reference Price (ARP) Perception
FIGURE 2a
Study 1: The Moderating Role of Product Category on ARP Perception
FIGURE 2b.
Studies 2 and 3: The Mediating Role of Thought Overlap on Advertised Reference Price (ARP)
Perception
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FIGURE 3a
Results of Experiment 1: Product Feature Overlap x ARP presence on Internal Reference Price
FIGURE 3b
Results of Experiment 1: Product Feature Overlap x ARP presence on Deal Evaluations
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FIGURE 4
Experiment 1: Conceptual Model
Experiment 1: Statistical Model
*p<.10; **p<.05; ***p<.01
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FIGURE 5
Experiment 2: Statistical Model
*p<.10; **p<.05; ***p<.01
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FIGURE 6
Experiment 3: Statistical Model
*p<.10; **p<.05; ***p<.01
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