Digital Nudging: Numeric and Semantic Priming in E-Commerce Alan R. Dennis a, Lingyao (IVY) Yuan b, Xuan Fengc, Eric Webb d, and Christine J. Hsiehe aOperations and Decision Technologies Department Kelley School of Business, Indiana University, Bloomington, Indiana, USA; bDepartment of Information Systems and Business Analytics, Debbie & Jerry Ivy College of Business, Iowa State University, Ames, Iowa, USA; cDivision Management Information Systems Price College of Business, University of Oklahoma, Norman, Oklahoma, USA; dDepartment of Operations, Business Analytics, and Information Systems Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, Ohio, USA; eSan Francisco, California, USA ABSTRACT Most research on e-commerce has focused on deliberate rational cognition, yet research in psychology and marketing suggests that buying decisions may also be influenced by priming (a form of what Information Systems researchers have called digital nudging). We con- ducted seven experiments to investigate the impact of two types of priming (numeric priming and semantic priming) delivered through what appeared to be advertisements on an e-commerce website. We found that numeric priming had a small but significant effect on consumers’ willingness to pay when the value of the product was unclear, but had no effect when products displayed a manufacturer’s suggested retail price (MSRP) or a fixed selling price. Semantic priming had larger effects on willingness to pay and the effects were significant but smaller in the presence of an MSRP. Thus, the combination of numeric and semantic priming has a larger impact on consumers’ willingness to pay. Taken together, these experiments show that some of the research on numeric priming and semantic priming done in offline settings generalizes to e-commerce settings, but there are important boundary conditions to their effects in e-com- merce that have not been noted in offline settings. In online auctions (e.g., eBay), sellers can influence customers to pay more for products whose value is unclear by displaying products with clearly labelled high prices alongside the products the consumer searched for. However, such tactics will have only minimal effects for auctions of products whose price is known (e.g., those with an MSRP) and no effects on products with clearly listed prices (e.g., Amazon). KEYWORDS Decision making; anchoring and adjustment; priming; dual process cognition; System 1 cognition; System 2 cognition; digital nudge; online auctions; willingness to pay; pricing; price anchors Introduction What affects how much a consumer is willing to pay for a product in an e-commerce marketplace? Much prior research has focused on the rational aspect of consumer buying behavior, so past research suggests that willingness to pay is influenced by consumers deliberately considering pricing information, product value, product image, trust in the seller, website design, available information, and.

1. Digital Nudging: Numeric and Semantic Priming in
E-Commerce
Alan R. Dennis a, Lingyao (IVY) Yuan b, Xuan Fengc, Eric
Webb d,
and Christine J. Hsiehe
aOperations and Decision Technologies Department Kelley
School of Business, Indiana University,
Bloomington, Indiana, USA; bDepartment of Information
Systems and Business Analytics, Debbie & Jerry Ivy
College of Business, Iowa State University, Ames, Iowa, USA;
cDivision Management Information Systems Price
College of Business, University of Oklahoma, Norman,
Oklahoma, USA; dDepartment of Operations, Business
Analytics, and Information Systems Carl H. Lindner College of
Business, University of Cincinnati, Cincinnati,
Ohio, USA; eSan Francisco, California, USA
ABSTRACT
Most research on e-commerce has focused on deliberate rational
cognition, yet research in psychology and marketing suggests
that
buying decisions may also be influenced by priming (a form of
what
Information Systems researchers have called digital nudging).
We con-
ducted seven experiments to investigate the impact of two types
of
priming (numeric priming and semantic priming) delivered
through
what appeared to be advertisements on an e-commerce website.
We

2. found that numeric priming had a small but significant effect on
consumers’ willingness to pay when the value of the product
was
unclear, but had no effect when products displayed a
manufacturer’s
suggested retail price (MSRP) or a fixed selling price. Semantic
priming
had larger effects on willingness to pay and the effects were
significant
but smaller in the presence of an MSRP. Thus, the combination
of
numeric and semantic priming has a larger impact on
consumers’
willingness to pay. Taken together, these experiments show that
some of the research on numeric priming and semantic priming
done in offline settings generalizes to e-commerce settings, but
there are important boundary conditions to their effects in e-
com-
merce that have not been noted in offline settings. In online
auctions
(e.g., eBay), sellers can influence customers to pay more for
products
whose value is unclear by displaying products with clearly
labelled
high prices alongside the products the consumer searched for.
However, such tactics will have only minimal effects for
auctions of
products whose price is known (e.g., those with an MSRP) and
no
effects on products with clearly listed prices (e.g., Amazon).
KEYWORDS
Decision making; anchoring
and adjustment; priming;
dual process cognition;
System 1 cognition; System

3. 2 cognition; digital nudge;
online auctions; willingness
to pay; pricing; price anchors
Introduction
What affects how much a consumer is willing to pay for a
product in an e-commerce
marketplace? Much prior research has focused on the rational
aspect of consumer buying
behavior, so past research suggests that willingness to pay is
influenced by consumers
deliberately considering pricing information, product value,
product image, trust in the seller,
website design, available information, and so on [6, 16, 34, 35,
41, 48, 57, 70]. Yet, psychology
CONTACT Alan R. Dennis [email protected] Operations and
Decision Technologies Department Kelley School
of Business, Indiana University, 1275 E 10th Street,
Bloomington, IN 47405, USA
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
2020, VOL. 37, NO. 1, 39–65
https://doi.org/10.1080/07421222.2019.1705505
© 2020 Taylor & Francis Group, LLC
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019.1705505&domain=pdf&date_stamp=2020-02-19
research shows that much of human behavior is guided by

4. nonconscious cognition and can be
influenced by seemingly irrational elements in the environment
[5, 10, 30, 37, 38].
For example, Nunes and Boatwright [47] set up two booths on a
west coast beach
boardwalk one Saturday. One booth displayed a single product
(a plain sweatshirt) whose
price was advertised as $10 or $80. The adjacent booth sold a
CD in a single bid auction:
the consumer was asked to name a price and if the price met the
threshold amount, the
consumer purchased the CD. When the unrelated sweatshirt in
the adjacent booth was
priced at $80, consumers bid significantly more for the CD than
when the sweatshirt was
priced at $10.
In the 15 years since this study, the irrational nature of
consumer buying decisions
being affected by the price of a nearby unrelated product has
been widely used in research,
and reported in the popular press [5, 30]. More than a dozen
studies have actually looked
more deeply into this phenomenon, most in traditional offline
environments [e.g., 2, 30,
32, 69]. The general consensus is that this is a special case of
Tversky and Kahneman’s
[65] anchoring and adjustment bias called numeric priming,
where individuals were asked
to produce a numeric value as an initial anchor (which can be
easily biased by any number
in sight) and then insufficiently adjust this anchor up or down
to arrive at a final
decision [25].

5. Research has also identified semantic priming, where the price
of a related product can
influence a consumer’s willingness to pay [2, 44]. For example,
presenting an expensive
bicycle on the same catalog page as moderately priced bicycles,
increased the amount
consumers were willing to pay for a moderately priced bicycle
[44]. Semantic priming and
numeric priming share common roots but operate through
different yet complementary
psychological processes [2].
Virtually all of the prior research on numeric and semantic
priming in consumer
buying decisions has focused on traditional offline settings, not
e-commerce settings.
There are at least three key differences between e-commerce
and traditional settings.
First, e-commerce retailers can offer a much larger assortment
of products because they
are not limited by physical size [30]. Second, many products are
sold in name-your-price
auction sites such as eBay [21, 72]. Finally, and most
importantly, e-commerce retailers
have much greater control over how a consumer accesses and
interacts with the available
products and can strategically design their websites to influence
consumer behavior
through digital nudges [52, 67].
We conducted a series of seven experiments to investigate the
extent to which numeric
priming and semantic priming can be used to influence
consumer’s willingness to pay in
e-commerce. We used advertisements displayed during the
buying process as digital

6. nudges to implement the priming. Both numeric and semantic
priming influenced buying
decisions, but we also identified some clear boundary
conditions which limit the general-
izability of the findings of research in offline settings to e-
commerce settings. In short, the
effects of numeric priming are small and limited to only a
certain group of products in
online auctions, while semantic priming has a small to medium
effect sizes across a much
wider set of conditions. Thus, we conclude that the widely
reported irrational effects of
numeric priming [5, 30, 47] have very limited application in e-
commerce, while semantic
priming [2], which has received less attention, has much wider
application as a form of
digital nudging [52, 67]. We begin with a theoretical
background on priming and its
40 DIGITAL NUDGING.
implementation through digital nudging, and then present each
of the seven studies in
turn. We close with a discussion and the implications for
research and practice.
Theoretical Background
E-commerce has changed the face of business, enabling
consumers to purchase goods from
both businesses and individual sellers [9, 16]. In this
environment, understanding consu-
mers’ willingness to pay has become a key to business success
[4, 8, 9, 33]. Most prior

7. research is based on rational choice theory, which assumes that
an individual acts rationally
to balance costs against benefits to maximize personal
advantage [17, 51, 53]. The amount
a consumer is willing to pay is a function of the consumer’s
perceived value of the product
[50]. Prior research shows that willingness to pay is influenced
by many factors, some
related to the product, some related to the seller, and some
related to the design of the
purchasing environment. These factors are then used as
consumers evaluate the perceived
costs and benefits and rationally choose the most appropriate
product [17, 51, 53].
Information about the product and product reviews influence the
perceived value of
a product [15, 45, 63], as does prior experience with e-
commerce [23]. The design of the
e-commerce site itself can also influence willingness to pay [28,
35]. The quality of
a business’s e-image also affects the prices received at the
auction and individuals’ will-
ingness to transact business [16, 29]. Implementing information
feedback helps build trust,
which is critical in e-commerce [3, 4, 9, 16].
Recent research suggests that we need to look beyond theories
of fully informed
rational choice in e-commerce buying decisions because
consumers have cognitive biases
that influence their online buying decisions [52, 67]. One
interesting approach is digital
nudging, the use of interface design and product selection to
influence user buying
behavior by applying well known biases in human decision
making [52].

8. In this paper, we explore digital nudging that we implement
through the advertise-
ments displayed on an e-commerce website. We investigate how
digital nudging can
employ numeric priming and semantic priming which use well-
known biases in the
anchoring and adjustment decision process [65]. In this section,
we first describe the
anchoring and adjustment process, then examine how numeric
and semantic priming can
influence this process, and finally describe how to implement
numeric and semantic
priming in an e-commerce setting.
Anchoring and Adjustment
Anchoring and adjustment is a decision making process whose
effects are robust and
widely observed, even in the face of warnings about it [2, 24,
47, 65]. As the name
suggests, people using this process begin by making an initial
estimate (the anchor) and
then adjust this initial estimate to arrive at a final decision [47,
65]. Because people are
susceptible to confirmation bias, they tend to focus on
information that supports their
initial anchor and disregard information that refutes it [47].
This often results in insuffi-
cient adjustment [65] so that the final decision remains close to
the initial anchor [25].
Thus the choice of this initial anchor often has undue influence
over the final decision.
We would hope that rational decision makers would choose this
initial anchor with careful

9. thought. However, there is considerable empirical evidence that
this is not the case. Much
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 41
research has shown that decision makers are easily swayed by
the presentation of a number
prior to a decision — even a number they know is randomly
generated [6, 13, 55, 65]. This
number biases their initial anchor and thus has strong influence
on the final decision.
Recent research in psychology provides the theoretical
underpinnings of cognition that
explains why this occurs. Researchers have long argued that
there are two fundamentally
different forms of cognition [26]. Kahneman [38] summarized
research in this area and uses
the terms System 1 (automatic cognition) and System 2
(deliberate cognition). System 2 is
what we mean when we think of a decision maker taking time to
invest deliberate and
conscious thought to make a decision [64]. In contrast, System
1 runs continuously, and
delivers conclusions automatically and involuntarily; “it cannot
be turned off” [38, p. 25]. It
is the impulsive driver of behavior [64], our intuitive thought
process [19, 38]. When
presented with a stimulus, our System 1 cognition automatically
generates a response in
less than one second [38], often as quickly as 300 milliseconds
[62]. System 1 runs
continuously and supplies these assessments to us, even though
they are not asked for

10. [26, 38]. System 2 runs much slower and often adopts the
conclusions of System 1 without
thought [38]. When we use System 2 to override the initial
instinctive reaction of System 1,
the results of System 1 often strongly influence the System 2
cognition that follows [19, 38].
Influencing Price Anchoring through Priming
The anchoring and adjustment process plays out in buying
decisions [1, 5]. When
deciding to buy a product, the consumer often sets an initial
anchor price and then
adjusts this anchor to arrive at the final amount to pay [2]. This
initial anchor price is
influenced by relevant external information that the consumer
encounters (e.g., reference
prices such as prices for substitute products that could be
bought instead) [2]. There is
also evidence that this initial anchor price is influenced by
irrelevant external information
that is completely unrelated to the decision (e.g., the price of a
sweatshirt when buying
a CD [47]).
Two different theoretical mechanisms have been proposed to
explain these effects: numeric
priming and semantic priming [2, 58]. Priming (whether
numeric or semantic) is the presenta-
tion of a stimulus designed to influence subsequent cognition or
behavior [11]. There are several
decades of research showing that priming influences cognition
[12, 56]. Priming gets its name
because the stimulus is presented first (often via a game or
separate experimental task), followed
by the focal task of interest; the priming stimulus is designed to

11. “prime” one’s cognition to
perform in a specific way. The priming stimulus is often
presented supraliminally, such that the
individual is consciously aware of the stimulus but not its intent
[11]. Individuals are unaware of
priming; therefore, they will often deny its effects, even in the
face of evidence [11]. Even if
individuals are aware of the purpose of priming, it still affects
their cognition and behavior [11].
Numeric priming is defined as the presentation of a number
prior to a task requiring the
individual to make a decision involving a number [68]. Buying
decisions in online auctions
require consumers to name a price that they are willing to pay,
so they may be susceptible to
numeric priming. The consumer knows he or she must generate
a number to bid in the
auction. When the consumer looks at a product in an auction,
his or her System 1 cognition
attempts to instantly answer the question “How much should I
bid?” There are no quick
answers to this question, because it depends on the features of
the product. However,
System 1 will produce an answer in less than a second by
quickly finding a number (e.g., the
42 DIGITAL NUDGING.
priming number in working memory [68]), and delivers this as
its conclusion. Any reason-
able number in working memory [2] or in the visual field is
considered, even numbers in
the name of the product [22]. This System 1 result often

12. becomes the initial price anchor
although the consumer can invoke System 2 cognition to
override it. System 2 can (and
often does) change this initial price anchor through the
adjustment process, but this initial
anchor biases the final price determined by System 2 [2],
because the adjustment process is
often insufficient [25].
Semantic priming is defined as the presentation of a high or low
quality product prior
to a buying task [2, 44, 58]. Semantic priming is similar to
numeric priming, but works via
a different theoretical mechanism. The intent of semantic
priming is for the priming
stimulus to trigger the consumer to engage System 2 to think
about features of the
stimulus product, with its features entering working memory [2]
and influencing System
2’s deliberations. Thus priming with a high quality product
leads individuals to think
about features associated with high quality, while priming with
an low quality product
leads to thoughts about features associated with a low price [1,
44]. These product features
become salient in working memory and exert stronger influence
as the consumer uses
System 2 to evaluate a product and considers how much to pay
[2, 44, 58]. Features
associated with more or less expensive products become the
basis on which the product is
evaluated [58], which influences willingness to pay [2, 44].
The two theoretical mechanisms are not mutually exclusive.
System 1 cognition is more
susceptible to numeric priming, while System 2 cognition is

13. more susceptible to semantic
priming. Research investigating semantic priming has often
included numeric priming
because one way to indicate an expensive product is with a high
price — a number. There
are other ways to indicate an expensive product, such as
showing a luxury brand name
(e.g., BMW). Likewise, past research examining semantic
priming has usually used
a related product as the stimulus (i.e., in the same product
category such as priming
with an expensive bicycle when the task is to buy a bicycle),
whereas past research on
numeric priming has often used unrelated products.
Priming in the Online Context
Priming has received considerable attention in psychology and
marketing, but priming as
traditionally studied, is impracticable; in the real world, we
cannot force consumers to
spend five minutes doing a priming task before they buy a
product. Instead, we need to
deliver the priming stimulus concurrently with the task. Rather
than requiring consumers
to focus on the priming stimulus prior to the task, the priming is
presented on the same
screen at the same time as the task itself [54, 71]. The
advantage of this approach is that it
is readily applied to e-commerce environments. The task is
online and as the user
performs the focal task on one part of the screen (i.e., shopping
for a product), the
priming stimulus appears on an adjacent part of the screen.
We call this form of priming concurrent priming. Concurrent

14. priming may be weaker
than traditional a priori priming (with the stimulus presented
prior to the task) because
there is no guarantee that the user will actually see and process
the stimulus; the user may
simply ignore it as he or she works on the focal task. Yet,
research shows that individuals
do not need to consciously process priming stimuli for priming
to have an effect [14].
There is also some evidence that the effect of priming is
stronger when the priming stimuli
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 43
are presented closer in time to a buying decision [44]; therefore,
concurrent priming may
be stronger than traditional priming.
We chose to use advertisements as the concurrent priming
stimuli. Advertisements are
frequently found on e-commerce sites [66], so these
advertisements should appear natural
to users. Prior research also has used advertisements to deliver
priming [71]. We consider
concurrent priming a form of digital nudging [52, 67].
In this paper, we investigate the extent to which concurrent
priming influences
a consumer’s willingness to pay. There has been a substantial
amount of research on price
anchoring [2, 30, 32], but none of this research has examined
priming that is practical to
deliver in an online setting, nor examined the relative strengths
of the two theoretical

15. mechanisms (numeric priming, semantic priming) believed to
influence price anchoring.
Therefore, our goal is to understand the effects and limitations
of this form of digital
nudging and to investigate the two theoretical mechanisms that
have been proposed to
explain price anchoring (numeric priming and semantic
priming). We conducted a series
of seven experiments which are described in the following
sections. We begin with a series
of experiments using a name-your-price online auction (e.g.,
eBay), because this has
context has long been used by researchers to measure
willingness to pay. However, as
we explain later, this context may intensify the priming effect,
so in two experiments we
use a fixed-price e-commerce setting (e.g., Amazon).
Study 1: Numeric and Semantic Priming via Online Advertising
In this first experiment, we examine the effects of both numeric
and semantic priming
delivered through advertisements for products. This provides an
initial test of the effec-
tiveness of concurrent priming in influencing online buying
behavior and also enables us
to examine the relative influence of the two theoretical
mechanisms.
Numeric Priming
The first theoretical mechanism is numeric priming. This
mechanism theorizes that a number
displayed as a visual stimulus influences the amount a consumer
is willing to pay. In an online

16. auction, the consumer must decide how much to pay. System 1
cognition is always operating
so it will suggest an answer to this question in less a second
[38], regardless of whether the
consumer wants the answer or not [38]. System 1 quickly
searches working memory and looks
at available visual stimuli for a reasonable number to produce
[38].
With concurrent priming as envisioned here, an advertisement
for a product is
displayed on the screen at the same time that System 1 is asked
to produce a number.
This advertisement contains a number, a price for a product.
System 1 quickly latches
onto this number and uses it as it produces a number for how
much you should pay. This
number, despite its rather arbitrary source, is then often used as
an initial anchor. The
consumer invokes System 2 cognition and adjusts this number
to a more reasonable value,
but because adjustment is often insufficient [65], this initial
System 1 result often biases
the final answer produced by System 2 [2].
One may attempt to deliberately avoid the conclusions from
System 1 cognition, but this is
difficult because we are usually unaware of them [51].
Likewise, one does not need to focus
conscious cognition on stimuli in the visual field for them to
affect nonconscious cognition;
44 DIGITAL NUDGING.

17. as long as the stimuli are in the visual field, they will be
processed, even though the consumer
may not remember seeing them [14]. Therefore, when an
advertisement with a reasonable
price is shown on the screen, System 1 recognizes the price and
proposes this as an answer.
It does not matter if the advertised price is for a relevant or
irrelevant product — for
example if the price of a printer is displayed while shopping for
a camera — System 1
cognition uses it as an answer. The number must be within the
realm of possibility for the
price of the product under consideration [61, 68], but the source
of the number is irrelevant;
the priming effect is as strong whether the number comes from a
rational source (e.g., a related
product) or something unrelated [61] (e.g., a social security
number [5] or subliminal
priming [1]).
Semantic Priming
The second theoretical mechanism is semantic priming. This
mechanism theorizes that
a priming product triggers the consumer to think about features
of the product and these
features enter working memory [2] and become more accessible
as the consumer uses
System 2 to deliberate about willingness to pay. Thus the
features associated with the
priming product are more likely to be used in assessing how
much to pay for the focal
product. This is in sharp contrast to the numeric priming
mechanism, where a number —
any number — is the driving force.

18. With semantic priming, the nature of a high or low quality
priming product triggers
cognitions about product features that would warrant paying a
high or low price [2, 58].
Priming with a high quality camera will increase willingness to
pay because the priming
product triggers thoughts of features of expensive cameras (e.g.,
high pixels, low weight)
and those features become more accessible in working memory
and thus more salient to
System 2 as it deliberates [2]. In contrast, priming with an
unrelated product such as
clothes when buying a camera will have weak effects, if any at
all [2], because priming with
high quality clothes will trigger thoughts of features associated
with clothes not cameras.
It is important to note that much prior research on semantic
priming has indicated
whether the priming product is high or low quality by showing a
price. When a semantic
priming treatment includes a number, the treatment also triggers
numeric priming.
Unfortunately, not all authors have recognized this and many
have drawn erroneous
conclusions that their studies show the effects of semantic
priming alone (when in fact
they show the combined effects of both semantic and numeric
priming). As we previously
noted, it is possible to indicate high or low quality priming
products without simulta-
neously including a number that triggers numeric priming.
Hypotheses

19. The two theoretical mechanisms are complementary. Numeric
priming primarily affects
System 1 and is such that a reasonable number from any
stimulus source will have a direct
effect: higher priming numbers lead to a higher willingness to
pay, lower priming numbers
lead to lower willingness to pay; this forms our first
fundamental hypotheses (H1).
Semantic priming primarily affects System 2 and relies on the
stimulus to trigger thoughts
about features related to the focal product the consumer is
buying which will have a direct
effect: higher quality priming products lead to a higher
willingness to pay, lower quality
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 45
priming products lead to lower willingness to pay; this forms
our second fundamental
hypotheses (H2). Thus, our two specific hypotheses for the first
study are:
Hypothesis 1a: Individuals’ willingness to pay will be higher
when exposed to numeric
priming with a higher number than a lower number in an online
auction.
Hypothesis 2a: Individuals’ willingness to pay will be higher
when exposed to semantic
priming with a higher quality product than a lower quality
product in an online auction.
Method

20. Participants
Seventy-three undergraduate students taking an introductory
business course at a large US
public university participated in the study. The average age was
19.5 years; 57 percent were
male. Participants received extra credit for participating.
Task
We used a single bid auction to assess participants’ willingness
to pay. The specific task
was an online shopping task modified from Yuan and Dennis
[72], which asked partici-
pants to imagine themselves as a new student in a new Master of
Science in Graphic
Design program in the business school. We used a repeated
measures design in which
participants experienced the priming treatment twice; as a
result, they performed the same
buying task twice (with two different sets of products). The task
stated that to take courses
in this program, students needed to purchase two products from
the auction website (a
camera and a laptop). The instructions provided minimum
configuration requirements as
well as recommended configurations. We used laptops and
professional cameras because
both product categories have a similar price range and products
in each category have
a reasonably wide range of prices.
For each product category (cameras and laptops), participants
chose from a selection of
five products, one of which was a bargain brand. We used a set
of products rather than
one single product because it is well established that evaluating
a set of products follows

21. a different cognitive process than evaluating a single product
[36]. Individuals shopping
online usually compare multiple products, so for ecological
validity it was critical to
present multiple products to the participants. All products in the
same category had the
same color and appearance to reduce the effects of appearance.
Participants were pre-
sented with product descriptions, brand descriptions, rating
reports, and detailed test
results. Product descriptions and brand descriptions were
adapted from Amazon.com.
Rating reports and detailed test results were adapted from
Consumer Reports (see
Appendix A). Participants bid on only one product from each
category.
Treatment
We used a repeated measures design with relatedness of
advertised product (related vs.
unrelated) as a between-subjects factor and the price of the
advertised priming product
(high vs. low) as a within-subjects factor. Priming with an
unrelated product was a test of
numeric priming alone because it presents a number (the price
of the unrelated product)
46 DIGITAL NUDGING.
to induce priming. Priming with a related product was a test of
the combined effects of
numeric priming and semantic priming because the price
induces numeric priming and
the use of a price of a related product to induce perceptions of

22. high and low quality has
often been used for semantic priming.
Participants were randomly assigned to one of the two product
relatedness treatments
and received both priming price treatments (one for the camera
and one for the laptop,
with product and treatment order randomized). A within-
subjects design for the two
advertised prices better controls for individual differences. The
advertisement was pro-
vided on the right side of the screen (see Figure 1). We wanted
the advertisement to
appear different on each page, so System 1 would perceive it as
a new stimulus, but wanted
the stimulus to remain consistent as the participant moved from
page to page. The words
and price remained constant on every page, but the picture
changed from page to page.
The relatedness of the advertised product was manipulated
through the similarity of the
advertised product to the bidding product. A related product
was the same product type as
the bidding product, but not one of the specific products that the
participants could buy.
For example, a camera was used when the subject was buying a
camera. An unrelated
product was a different product type from the bidding product.
We chose a bicycle as the
unrelated product …