Buy Now or Buy Later

7/21/2019 Buy Now or Buy Later

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Journal of Marketing Theory and Practice, vol. 21, no. 4 (fall 2013), pp. 441455 2013 M.E. Sharpe, Inc. All rights reserved. Permissions: www.copyright.com

ISSN 10696679 (print) / ISSN 19447175 (online

DOI: 10.2753/MTP1069-6679210407

In the wake of the most crippling economic downturn

since the Great Depression, Americans have curbed their

shopping habits, and retailers are trying to adapt. Experts

do not expect the financial circumstances and buying prac-tices of the average consumer to bounce back as they have

after past recessions (International Council of Shopping

Centers 2009), which means that value-based pricinga

staple of retailmay be shortsighted (Anderson, Wouters,

and van Rossum 2010).

A new consumer requires a new strategy. Instead of sell-

ing a high volume of the same product at minimal margins

above cost, it may be more beneficial to implement a pricing

strategy that will (1) drive customers to visit the store more

frequently; (2) allow for a smaller inventory, and therefore

decrease warehouse costs; and (3) maximize profits forparticular items by understanding how scarcity and price

promotions influence the purchase decision.

In an effort to protect fragile margins, some retailers have

begun to lower inventory to avoid offering huge discounts

on overstocked products; however, rising costs continue

to exert pressure on these firms (Holmes 2011). Although

pricing strategy has received a great deal of attention in

the literature (e.g., Samli and Jacobs 1993), the subarea of

price promotions is underdeveloped (Sivakumar 1996). The

current research investigates a pricing strategy that employs

the counteracting effects of product scarcity and a known

price decrease on the same purchase decision. Essentially

the store offers some product in limited supply and then

lowers the price incrementally over the coming days orweeks until all are sold. The customer has full knowledge

of the number of items remaining as well as the timing

and amount of the discount schedule, which is posted on

a sign or the price tag. A typical price tag on a shirt may

read: $100April 1 through April 7; $75April 8 through

April 14; $50April 15 through April 21; $25April 22 and

after. The strategy combines the techniques of Dutch auc

tions and price skimming but differs in that there is often

more than one item and the customer has full knowledge

of the price decrease.

The decision is whether to buy the product at the currenthigher price or at the future, lower price, with the implied

risk of the product no longer being available. This strategy

is commonly seen in consignment shops, flea markets, and

garage sales, but we posit that it could be applied to the

broader retail sector. Specifically, traditional retailers often

need to sell seasonal merchandise to make room for new

inventory. This model may help retailers sell their remain-

ing inventory more quickly and at a higher profit margin

We call it the steadily increasing discount (SID) model

because it shares a foundation with Tsiros and Hardestys

(2010) steadily decreasing discounting (SDD) model for

ending a price promotion. Whereas the SDD model sug-

gests that firms can maximize profits by bringing a heavily

discounted product back to its normal price in steps rather

than all at once, the SID predicts that firms can maximize

profits on the front end by gradually arriving at the deep

discount in similar steps. In short, this pricing strategy

could be used to speed up the clearance process while

optimizing profits on those items.

BUY NOW OR BUY LATER: THE EFFECTS OF SCARCITY ANDDISCOUNTS ON PURCHASE DECISIONS

Colin B. Gabler and Kristy E. Reynolds

This research investigates a burgeoning pricing strategy and its effects on purchase behavior. Drawingfrom the expected-utility and prospect theories, we test the counteracting variables of scarcity and futurediscount across two studies. We first implement a flea market scenario to demonstrate that scarcity createsemotional value that increases purchase likelihood. Next, we determine the levels of scarcity and discountthat maximize purchase in a department store context. The findings suggest that the level of discountpredicts the purchase of highly visible products; for less visible products, scarcity drives the decision.These relationships are moderated by involvement with the product class.

Colin B. Gabler(Ph.D., University of Alabama), Assistant Profes-

sor of Marketing, College of Business, Ohio University, Athens,

OH, [email protected].

Kristy E. Reynolds(Ph.D., University of Alabama), Bruno Profes-

sor of Marketing, Culverhouse College of Commerce and Business

Administration, University of Alabama, Tuscaloosa, AL, kreynold@

cba.ua.edu.


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442 Journal of Marketing Theory and Practice

Few scholars have investigated the SID model, and even

fewer retailers have adopted it. Zara International has imple-

mented a strategy of tantalizing exclusivity (Ferdows,

Lewis, and Machuca 2004), and Lululemon uses scarcityto attract customers (Mattioli 2012). But like Tsiros and

Hardestys (2010) model, the SID model does not have a

foothold in mainstream retailing. The U.S. consumer wants

fewerbut more specificproducts, and this trend is not

likely to change in the near future, which means the SID

could have marketplace potential.

We begin with a review of the literature on pricing,

discounts, expected-utility theory, and scarcity. We then

use prospect theory to form hypotheses that are tested and

analyzed across two studies. Study 1 isolates the effects

of scarcity and tests the mediation of emotional value on

the purchase decision while Study 2 compares the effects

of product scarcity with that of a known future discount.

Involvement with the product class is tested as a modera-

tor in each context (see Figure 1). We conclude the paper

with a discussion of the important findings, implications

for theory and managers, limitations, and future research

directions.

CONCEPTUAL FRAMEWORK

Pricing, Discounts, and Expected-Utility Theory

The pricing literature demonstrates that when people expect

a price to rise, they buy now, and when they expect a price to

drop, they wait for the sale (Jacobson and Obermiller 1990).

But consumers view prices relative to what they paid in the

past and what they expect to pay in the future. In effect,

consumers create their own reference price or standard by

which they judge the price of an item (Monroe 1973), usu-

ally forming a range that they deem acceptable for a given

product (Rao and Sieben 1992). So, when consumers make

purchases, they are analyzing how much they think they

should pay for an item relative to what they paid in the past,what the price is now, and what it will be later.

Consumers use reference prices to make decisions about

the quality of the product and the monetary sacrifice

required to make the purchase (Monroe 2003). Generally,

individuals view a deviation above their reference price

negatively and a deviation below it positively. But a typical

reference price is dynamicnot just because of the normal

fluctuations of supply and demand but because of price

promotions. Consumer reactions to price promotions are

not always rational; they can even influence consumers to

abandon the cost-benefit analysis that usually governs their

decisions (Frank 2007).

Expected-utility theory (EUT) says that when consumers

face uncertain decisions, they try to maximize the expected

utility of their final assets (von Neumann and Morgenstern

1947). EUT assumes that, presented with a choice where

the outcomes are not known, individuals will compare the

weighted sum of option one versus the weighted sum of

option two, where the weighted sum is the utility of each

outcome multiplied by its probability (Mongin 1997).

All purchase decisions can be seen as a balance between

two types of this utility maximization. On the one hand,

consumers manage the pricequality relationship, wanting

to maximize the value gained for the price incurred. On the

other hand, they view the purchase as a monetary sacrifice,

wanting to maximize the utility (or minimize the disutil-

ity) of spending money (Monroe 2003). When a purchase

opportunity is unlimited, individuals place more weight

on the pricesacrifice relationship. Because they have time

to evaluate other indicators of quality (design, craftsman-

Figure 1Conceptual Model


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ship, detail, etc.), consumers view price less as an indica-

tor of quality and more for the financial burden it incurs

(Lichtenstein, Bloch, and Black 1988). Utility maximization

of this relationship occurs when the price represents the

smallest sacrifice possible. When a purchase opportunity is

limited, individuals place more weight on maximizing the

utility of the pricequality relationship (Suri and Monroe

2003). Because they cannot process as much information,the price tag serves more as an indicator of quality than

monetary sacrifice and utility maximization hinges more

on the level of perceived quality (Suri, Kohli, and Monroe

2007).

Given an unlimited purchase opportunity, buying a

discounted product represents a smaller sacrifice, and

therefore higher utility. Using Jacobson and Obermillers

(1990) logic, if individuals know that a price will drop in

the future, they would be less likely to buy the product in

the present. Thus, the knowledge of an upcoming discount

should lead to purchase postponement.

Scarcity, Prospect Theory, and Decisions

Under Risk

One way that retailers can limit the purchase opportunity

is by manipulating scarcity. Scarcity can take the form of

a purchase limit (e.g., Limit X per customer), a purchase

precondition (e.g., Product X only available to those who

buy product Y), a time limit (e.g., Call within the next

30 seconds!), or the focus of this research, a product limit

(e.g., Only X number of products remain!) (Inman, Peter,and Raghubir 1997). Brocks (1968) commodity theory pos-

ited that individuals assign values to commodities based on

their availability, viewing scarce products as more attrac-

tive than readily available ones. Furthermore, increases in

scarcity also lead to increases in perceived value (Cialdini

1993) and desirability (Lynn 1989).

Consumers normally prefer to gather information

before the purchase decision, but retailers can induce

excess demand to force consumers to decide before they

are comfortable (DeGraba 1995). Apple creates one of these

buying frenzies with each iteration of the iPad. Similarly,

Lululemon purposely keeps a low inventory to drive faster

purchases (Mattioli 2012). In such a restricted consumer

setting, consumers often use availability as their main

source of information to make the decision (Inman, Peter,

and Raghubir 1997).

EUT has failed to predict some very common human

behaviors. One reason is that many studies focus on back-

ward-looking consumer choices although most research on

consumption shows that people, in general, are forward

looking (Hall 1978). Another reason for the theorys lack

of explanatory power is that money is not always the most

important outcome in consumer decisions (Bell 1982)

consider the people waiting in line for the newest release

at the Apple store. But the main reason that EUT does not

apply to many situations is that people do not always opt

to maximize their utility.Numerous nonexpected-utility models have been devel

oped to address this behavior. For instance, prospect theory

says that people tend to place more weight on outcomes

they consider to be certain than on those they consider to

be probable. Therefore, individuals will more often choose

a modest gain if it is a sure thing over some probability

of a larger gain. However, when forced to choose between

a certain modest loss and some probability of a larger loss

individuals more often risk the larger loss (Kahneman and

Tversky 1979).

Suppose that Apple is selling iPads for $500 today and$450 tomorrow. This pits the certainty of a modest gain

(full-price iPad) against the probability of a slightly larger

gain (discounted iPad). Given an infinite number of prod

ucts, the rational individual would wait for the lower price,

as predicted by EUT. But what if there were 100 people who

wanted to buy 10 iPads? Then, an iPad is a scarce com-

modity, and the probability of obtaining one is 1 in 10, or

10 percent. If there were 50 iPads, that probability would

increase to 50 percent; however, if there was only one, i

would decrease to 1 percent. Scarcity shares a theoretica

root with probability; the literature tells us that individual

would perceive more value and desirabilit y in the lone iPad

than 1 of the 10, and more in 1 of the 10 iPads than 1 of

the 50 (Cialdini 1993; Lynn 1989). Because people tend to

overweight outcomes based on their certainty (Kahneman

and Tversky 1979), and the probability of obtaining an item

changes with the probability, as scarcity increases, so does

purchase likelihood. Formally,

Hypothesis 1: Scarcity positively influences purchase.

But what if there is a limited number andan upcoming

discount? Prospect theory suggests that the overweighing

of certainty favors the risk averse in the domain of gains

and the risk seeking in the domain of losses (Kahneman and

Tversky 1979). Risk aversion is a personality trait that, like

scarcity and pricing discounts, influences the probability

that an individual will accept uncertainty over certainty

(Burton et al. 1998). In an experiment, individuals preferred

a definite one-week vacation to England to a 50 percent

chance of a three-week vacation to England, France, and


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Italy. In the iPad scenario, the $500 product would be the

certain one-week trip and the $450 product the uncertain

three-week trip. In weighing the two prospects, the former

is certain, hence increasing its prospect, but the latter has

greater value, thus increasing its prospect. In general, the

latter prospect increases or decreases with the number avail-

able. An individual will be more likely to take the certain

modest gain (product) over the probability of a larger gain(product plus discount) because the certainty of option 1

outweighs the added value of option 2.

Perceived Emotional Value

Emotions are subjective states of being that involve a

hedonic component and motivate behavior specific to that

emotion (Baron 1992). In a shopping context, emotional

value is the utility derived from the feelings created by

a product (Sweeney and Soutar 2001), and we use this

definition in our study. Cialdini (1993) found that peopleassume that something is more valuable if it is less common

and suggested that scarcity clouds the brain and limits the

ability to process information. This response is similar to

that of impulse buying (Rook 1987). Both trigger emotions

that may lead to purchase based on something other than

attributes of the product itself. However, impulsivity is typi-

cally a trait of the individual while scarcity is a situational

characteristic. Accordingly, under high scarcity conditions,

the perceived emotional value of a product should be high;

in a low scarcity condition, the perceived emotional value

should be low. Therefore,

Hypothesis 2: Scarcity positively influences perceived

emotional value.

When time and quantity are unlimited, one can pay

more attention to the important information and relevant

cues. The lack of temporal pressure is associated with extra

processing (Bozzolo and Brock 1992). In auctions and other

scenarios where time or quantit y is limited, the emotional

arousal compromises a persons cognitive ability, which

decreases the number of information cues one can grasp

to assess the decision (Dhar and Nowlis 1999). Under suchconditions, this distraction leads to decision making based

on heuristics (Cialdini 1993). Because emotional value

serves as a heuristic in the purchase decision, we posit that

it will act as a mediator between scarcity and purchase.

Therefore,

Hypothesis 3: Perceived emotional value mediates the

relationship between scarcity and purchase.

Involvement with Product Class

In the marketing literature, involvement is defined as the

importance of a product or decision to an individual (Mittal

1995). Involvement creates a state of arousal or motivation

and drives us to process information more thoroughly

(Rothschild 1984). Enduring involvement is ongoing; it is

independent of a specific occasion, and it usually enforcesan individuals self-concept (Richins and Bloch 1986).

While scarcity limits our ability to process informa-

tion, it also creates two key antecedents of involvement,

perceived risk and perceived pleasure value (Laurent and

Kapferer 1985), each of which is fundamental in deter-

mining the complexity and extensiveness of the cognit ive

process (Celsi and Olson 1988). When an item is scarce,

its perceived pleasure value increases as does the perceived

risk of missing out on the opportunity (Bloch and Richins

1983). An individual involved with the product class will

process information more readily because the productand

consequently the decisionare both more important and

interesting.

If an individual is involved with music, he or she cares

about it and most likely is knowledgeable about it (Brucks

1985). For these individuals, a scarce album would likely

have a higher pleasure value and risk associated with its

purchase. However, scarcity alone would not cause low-

involvement individuals to suddenly become interested

in music, so the pleasure value and risk will not change

for them. Thus, scarcity will heighten the importance of

a product for high-involvement individuals but not low-

involvement individuals. Formally,

Hypothesis 4: Involvement with the product class

moderates the positive relationship between scarcity

and purchase such that when involvement is high this

relationship is strong and when involvement is low, this

relationship is weak.

STUDY 1

Sample and Procedure

Based on a pretest, we developed manipulations for high

and low scarcity as well as the choice of product for the

main study. The sample consisted of 247 undergraduate

students at a large southeastern university. The students

completed the survey in class using paper and pencil. When

finished, they were given extra credit and debriefed about

the purpose of the study. Their average age was 21 with a

range from 18 to 35, with 45 percent male and 55 percent


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female. The subjects identified their favorite musical art-

ist or band and the dollar amount they would spend on

an album of that bands first concert. Then we presented

them with a CD-purchasing scenario. Random assignment

to one of two scarcity conditions (high-low) placed the

respondents at a flea market where they found that CD for

$5 higher than the price they identified. The low-scarcity

condition had three CDs while the high-scarcity conditionhad one. The scenario read that the flea market offered

any unsold items at half-price the following Saturday, and

asked subjects to decide between purchasing the CD that

day or next week.

Measures

Laurent and Kapferers (1985) three-item scale (= 0.85)

was used to measure involvement with the product classand

Sweeney and Soutars (2001) five-item scale (= 0.91) was

used to measure perceived emotional value. As discussed,certain individuals are more prone to buy on impulse than

others. For that reason, Rook and Fishers (1995) modified

impulse buying scale (= 0.91) was included as a covari-

ate so that the emotional response of scarcity could be

isolated. Consumers also differ on their general aversion

to risk. Therefore, Burton et al.s (1998) four-item risk aver-

sionscale (= 0.76), which measures the degree to which

an individual avoids taking risks in life, was included as

a covariate. The survey also contained a manipulation

check for scarcity (The number of CDs at the flea market

is . . .) using a seven-point scale ranging from (1 = low)

to (7 = high). Appendix A contains the scenario and scale

items after purification.Purchasewas measured as a binary

response variable (yes/no).

Analysis and Results

Participants in the high-scarcity scenario described the

CD as significantly (F(1,245) = 27.415;p < 0.001) scarcer

(M = 5.52) than those in the low-scarcity scenario (M= 4.59).

An interesting finding was that the amount the participants

were willing to spend positively influenced both their likeli-

hood to purchase (p < 0.01) and the perceived emotional

value assigned to the CD (p < 0.001). The only gender dif-

ference that emerged was that women placed significantly

more emotional value than men on the CD (p < 0.05) .

Sixty-three of the 120 respondents (53 percent) on the

low-scarcity condition chose to buy the CD while 93 of the

127 respondents (73 percent) on the high-scarcity condition

chose to buy the CD (2(1,245) = 11.484,p = 0.001). This

supports H1; despite the knowledge of a future discount

scarcity positively influences purchase behavior. We imple

mented Baron and Kennys (1986) three-step regression

procedure to determine if perceived emotional value medi-

ated the relationship between scarcity and purchase, adding

the covariates in each step to demonstrate unique variance

(Neubert et al. 2008). While ordinary least squares regression

was appropriate for step 1, steps 2 and 3 required binarylogistic regression. Using a procedure developed by (MacK

innon and Dwyer 1993), the coefficients were multiplied by

the standard deviation of the predictor variable and then

divided by the standard deviation of the outcome variable

This step allowed the authors to compare continuous and

binary variables in the same analysis (see Table 1).

The results from step 1 support H2. Essentially, the scarcer

the CD, the more emotional value the respondents attached

to owning it. Step 2 replicates H1 while step 3 included both

scarcity and perceived emotional value and tested their

relationship with purchase. The beta for scarcity droppedwith the addition of perceived emotional value; however, i

remained significant (p < 0.05), which exhibits partial media

tion and support for H3. This is evidence that scarcity led

consumers to purchase the CD, in part because they placed

more emotional value on the idea of owning the CD.

Finally, it is worth noting that the average amount that

respondents were willing to spend on the CD was $23. The

scenario specifically asked if they would pay $5 more than

that amount. Five dollars represents an increase of 22 percen

to the average self-reported price of $23. Therefore, scarcity

did not just lead people to buy the CD at their upper limit

the respondents were willing to spend, on average, 22 per-

cent more thantheir upper limit.

A Sobel (1982) test confirmed that the indirect effect was

significant. The test statistic was 2.01 and was significant on

both the one-tail (p < 0.05) and two-tail (p < 0.05). These

results indicate that the indirect effect of the independent

variable, scarcity, on the dependent variable, purchase

through the mediator, perceived emotional value, was

significant. Thus, we can conclude that the regression was

significant on all levels.

To test the moderating effect of involvement with the

product class, a median split was performed to create high

and low levels. A t-test confirmed that a high-involvement

respondent was significantly more involved (M = 6.54)

than someone with a low level of involvement (M= 4.49

t(1,244) = 21.03,p < 0.001), which allowed us to treat the

high and low levels as unique variables. Next, we conducted

a mean difference test on both levels of involvement for the

high- and low-scarcity conditions. We found support for H4


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confirmed by a difference of proportions test (Z= 0.406,

p< 0.001). For high involvement individuals, those in the

high-scarcity condition bought the CD 85 percent of the

time compared to those in the low-scarcity condition who

bought the CD 53 percent of the time. Conversely, for low

involvement individuals, the purchase percentages did not

change significantly as a result of the scarcity condition

(53 percent in low-scarcity condition; 61 percent in the

high-scarcity condition). Interestingly, in the low-scarcity

condition, the same percentage (53 percent) of individuals

chose to buy the CD regardless of their level of involvement

with the product class. This means that for those with low

involvement, the relationship between scarcity and purchase

was not just weak, but actually nonexistent (see Figure 2).

STUDY 2

Study 2 was conducted to assess how the relationships from

Study 1 might change when the level of discount is also

manipulated. A more traditional retail setting (shopping

for jeans in a department store) was chosen to increase

generalizability.

Suppose that the iPad, originally priced at $500, soldfor $375 the next day. This pits the certainty of a modest

gain (just the iPad) against the probability of an even larger

gain (iPad + 25 percent discount). Finally, imagine that the

iPad sold for $250 the next day. This pits the certainty of

a modest gain (just the iPad) against the probability of a

much larger gain (iPad + 50 percent discount). Based on

weighted probabilities, the first prospect remains the same

while the second prospect gains value as the incentive, or

discount, increases (Kahneman and Tversky 1979). Conse-

quently, the value of the first prospect decreases relative to

the second. At some threshold, the value added of prospect

two outweighs the certainty of prospect one.

Consumers use the level of perceived risk as a factor in

product decision making (Bettman 1973), and individuals

use availability to estimate that level of risk (Tversky and

Kahneman 1973). We predict that when there is a lot of the

product in stock and the manager puts that product on a

high future discount, the risk is low, and thus the incentive

to wait is large. If the discount was low, the possible savings

would not be worth the wait; if fewer products were avail-

able, the risk that they would be purchased would be high

(Folkes 1988). In both cases, the incentive to wait decreases.

Therefore, the most ideal time to wait for the sale is when the

scarcity is low and the future discount is high. Conversely,

the most ideal time for a consumer to buy the productor

when the incentive to wait is smallestis when the product

is scarce and the future discount is low. Formally:

Hypothesis 5: Those in the low-scarcity and high future

discount condition are least likely to purchase while those

in the high-scarcity and low future discount conditionare most likely to purchase.

As discussed, involvement is the importance of a deci-

sion based on an individuals specific needs, values, and

interest in that product (Zaichkowsky 1985). An unimport-

ant purchase decision is then, by definition, uninvolving

(Richins and Bloch 1986). Scarcity implies a perceived risk,

which would heighten the relevance of that product to ones

values and interests (Bloch and Richins 1983). Similar to

Table 1

Regression Analysis for Study 1

Dependent Variable: Purchase

Step 1: IV Med Step 2: IV DV Step 3: IV/Med DV

Variable S.E. VIF S.E. S.E.

Control Variables

Risk Aversion 0.185 0.060* 1.000 0.060 0.122 0.030 0.131Impulse Buying 0.054 0.055 1.000 0.115 0.113 0.094 0.117

Independent Variable

Scarcity 0.167** 0.135 1.000 0.898*** 0.273 0.771* 0.282

Mediator Variable

Perceived Emotional Value 0.505*** 0.141

Adjusted R2or Nagelkerke R2 0.054 0.068 0.141

F(df) or (df) 5.668 (3, 242)** 12.499 (2, 244)* 26.804 (3, 243)***

Notes:IV = independent variable; Med = mediator variable; DV = dependent variable; VIF = variance inflation factor; S.E. = standard error; df = degrees

of freedom. Listwise n= 247. Standardized betas are reported. *p< 0.05; **p< 0.01; ***p< 0.001.


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Study 1, we predict scarcity to accentuate the importance

of the decision for high-involvement individuals in the

clothing product category, making the decision more risky,

interesting, and relevant.

Low involvement with a product class translates to less

arousal or motivation to process information (Rothschild

1984). It means that the product is not as essential to ones

self-concept (Richins and Bloch 1986) and the decision isnot as important. Therefore, for low-involvement individu-

als, scarcity will not make the weight of the decision more

salient. We predict it will extenuate the interest, risk, and

importance of the decision. In fact, when consumers have

low involvement with a product, price often acts as the

main indicator of quality (Monroe 1973), and consequently,

determines behavior. Studying red wine, Zaichkowsky

(1985) found that low-involvement individuals placed more

emphasis on price than high-involvement individuals. In

essence, the deviation below the reference price was the

predictor of purchase for these consumers (Winer 1986).In this study, the discount is the price cue, and we predict

it will drive behavior for low-involvement consumers.

Conversely, high-involvement consumers care more about

product attributes and are willing to pay for them (Lich-

tenstein, Bloch, and Black 1988). Generally, higher prices

are deemed more acceptable by consumers with high

involvement than low involvement (Bloch, Sherrell, and

Ridgway 1986); therefore, the effect of the discount should

be minimal for these individuals. Therefore,

Hypothesis 6: Involvement with the product class moder-

ates the negative relationship between future discountand purchase such that when involvement is low this

relationship is strong and when involvement is high, this

relationship is weak.

Sample and Procedure

To test H5 and H6, we manipulated both scarcity and

future discount in a retail shopping scenario and analyzed

their effects on the purchase decision. We conducted a

pretest to uncover shopping habits that would adversely

affect the data, such as hiding or reserving the product,and addressed these issues in the final scenario. For the

main study, we employed Bitner, Booms, and Tetraults

(1990) student recruitment method to identify subjects,

first training them and then giving each a URL (Uniform

Resource Locator) to present to their recruit. The students

had the opportunity to recruit two people to participate,

receiving extra course credit for each respondent. To vali-

date the recruited sample, we contacted 10 percent of the

respondents to verify their participation in the study and

uncovered no problems.

The participants accessed the survey through a secure

Web site, which generated a random combination of manip

ulations each time the URL was clicked. The recruits could

only access the link once. The identification, recruitment

and data collection period lasted two weeks, resulting in a

total of 423 respondents. The mean age was 30 and ranged

from 18 to 64, with 60 percent female and 40 percent male

The scenario first asked the subjects to imagine that they

needed a new pair of jeans and to identify their go-to

brand. It then asked how much they would be willing to

spend for a pair of jeans from that brand in the exact style

fit, color, etc., that you are looking for. The mean pricewas $67 while both the mode and median were $50.

The scenario placed the respondents at a store where they

found that exact pair of jeans at the price they identified

We manipulatedscarcityat high and low conditions, with

ten pairs of jeans representing the low condition and two

pairs representing the high condition. To manipulate future

discount, the scenario contained the line Above the table

hangs a sign that reads, All jeans 50%-off [25%-off, 10%

off] next Saturday, which created high, moderate, and

low conditions. The Web site then randomly assigned each

respondent to one of the two scarcity conditions and oneof the three future discount conditions before presenting

them with two choices: Buy the jeans today or Come

back next Saturday to buy the jeans.

Measures

Laurent and Kapferers (1985) three-item scale (= 0.91)

was again used to measure the involvement with the produc

Figure 2Moderating Effect of Involvement for Study 1

Low Scarcity

High Scarcity


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class. Rook and Fishers (1995) modified impulse buyingscale

(= 0.94) was included as a covariate. The survey also con-

tained a manipulation check for scarcity (The number of

pairs of jeans on the table is . . .) and future discount. (The

discount being offered is . . .) using a seven-point scale

ranging from (1 = low) to (7 = high). Appendix B contains

the scenario and scales items after purification.

Analysis and Results

Both of the manipulations were successful. The partici-

pants in the high-scarcity scenario described the jeans as

significantly (F(1, 425) = 23.03,p < 0.001) scarcer (M= 4.50)

than those in the low-scarcity scenario (M= 3.67). Similarly,

the discount was rated as significantly different at each

level (Mhi= 5.39, M

mod= 4.29, M

lo= 3.42,F(2, 424) = 53.22,

p < 0.001). To determine whether scarcity and future

discount produced a significant interaction, we tested a

binary logist ic model. We entered three blocks separately;

starting with the covariate (impulse buying), followed by

the manipulated scarcity and future discount variables,and finally the interaction. We assessed the overall model

fit after each addition.

The first model with just impulse buying was significant

and predicted 63.6 percent of the purchase outcomes. The

second model, which contained the manipulated indepen-

dent variables, showed superior fit as both the block and

the model were significant and the percentage of correct

predictions increased 3.1 points to 66.7 percent. The final

block, which included the interaction term, was not signifi-

cant and actually had less predictive power (66.3 percent),

allowing interpretation of coefficients from model 2. To

achieve probabilities, we used the formula, P= 1/exp(b),

where the expected beta is the effect of the independent

variable on the odds ratio. The resulting probability is

the change in likelihood that an individual will purchase

given an increase in that variable. As seen in Table 2, those

in the high scarcity condition were 1.36 times more likely

to buy than those in the low-scarcity condition (p < 0.01),which replicates H1 from Study 1. Those in the high future

discount condition were 0.74 times less likely to buy than

those in the moderate future discount condition, who were

0.74 times less likely to buy than those in the low future

discount condition (p < 0.01).

We used a difference of proport ions test to analyze H5.

Each of the six conditions produced a proportion of those

who chose to buy the jeans. Comparing this proport ion to

the overall proportion determined if there was a significant

difference in any one condition. The low-scarcityhigh

future discount condition yielded the lowest purchaseprobability (48.5 percent), which represents a significant

difference (p < 0.05) from the average of 62.6 percent. The

high-scarcitymoderate future discount condition yielded a

slightly larger purchase probability (72.4 percent) than the

high-scarcitylow future discount condition (71.2 percent).

Because these two percentages are not significantly different

from one another and the high-scarcitylow future discount

is significantly different from the low-scarcityhigh future

Table 2

Overall Logistic Regression Results for Study 2

Dependent Variable: Purchase

Model 1 Model 2 Model 3

Variable Coefficient Wald Coefficient Wald 1/exp() Coefficient Wald

Constant 0.439 2.78 0.492 3.39 0.507 3.57

Impulse Buying 0.290*** 15.22 0.310*** 16.66 1.36 0.314*** 16.94Scarcity 0.289** 7.47 1.34 0.286** 7.29

Discount 0.304* 5.63 0.74 0.300* 5.46

ScarcityDiscount 0.103 0.64

Model 2(df) 16.01 (1) 29.21 (3) 29.85 (4)

Block 2(df) 13.13 (2) 0.64 (1)

Hosmer and Lemeshow

2(df)9.78 (8) 5.70 (8) 14.94 (8)

Percent Correct

Predictions

63.6 66.7 66.3

Nagelkerke R2 0.05 0.09 0.09

Notes:The Wald statistics are distributed chi-square with 1 degree of freedom. *p < 0.05; **p < 0.01; ***p < 0.001.


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Fall 2013 449

discount condition (p < 0.01), we demonstrate partial sup-

port for H5 (see Table 3).

To test H6, as well as replicate the results of H4, we

performed a median split to create high and low levels of

involvement. The t-test confirmed that the high-involvement

group was significantly more involved (M = 5.85) than

the low-involvement group (M = 3.49, t(1,418) = 27.34,

p < 0.001).Using binary logistic regression, we attempted to repli-

cate H4 with the new data. In Study 1, scarcity affected those

with high involvement and had no effect on those with low

involvement. In this scenario, we found the opposite effect.

For individuals reporting low involvement with clothing,

scarcity was the driver of purchase (1/exp(b) = 1.56), mean-

ing that regardless of discount, low-involvement individuals

were 1.56 times as likely to buy the jeans when scarcity was

high than when it was low (p < 0.01). Furthermore, high-

involvement individuals were more influenced by the level

of the discount (1/exp(b) = 0.65) as the respondents were0.65 times less likely to buy a pair of jeans at each discount

interval (p < 0.05) (see Tables 4 and 5).

In essence, low involvement increased the salience of

scarcity while high involvement increased the salience of the

discount, which is opposite of the predicted effects in H4

and H6. When the product class was music, high involve-

ment was pronounced by scarcity. The decision was more

risky and interesting, leading to a greater proportion of the

respondents to purchase the CD. However, with clothing,

scarcity had a significant effect on the low-involvement

individuals while the high-involvement individuals were

affected by the level of discount. These counterintuitive

findings is addressed in the next section.

GENERAL DISCUSSION

Theoretical Implications

While demographics and personal characteristics certainly

influence purchase behavior in a retail setting, this research

asserts that the purchase scenario itself can have a major

impact on the decision. Mazumdar, Raj, and Sinha (2005)

suggest that context plays an important role in the purchaseprocess, and discount and scarcity certainly fall into that

category. Auction research has examined scarcity and future

price increases (Campbell 1999), but in this research we set

out to investigate the counteracting effect of scarcity and

a future price discount in the same scenario.

Theoretically, we demonstrate another instance in which

EUT fails to predict choice behavior. We find that prospect

theory better explains the way consumers react to the

counteracting variables. In Study 1, scarcity led people to

choose the certain modest gain (product at full price) over

some probability of a larger gain (product at discounted

price). One reason for this behavior is that consumers attach

emotional value to a product based on its availability and

use this heuristic in the purchase scenario. Our findings

show that not only will individuals pass up a discount

but they will even purchase a product at a higher price

than their upper limit. In Study 2, when discount was also

manipulated, individuals were more likely to risk a certain

modest gain for the chance of a larger one, depending on

the size of the discount.

Managerial Implications

Promotional discounts are effective because most consum

ers want to purchase products at the lowest price possible

However, firms generally want to sell products at the high

est price possible and as soon as possible. Our study show

that by framing the purchase scenario with the dual aspects

of scarcity and future discount, consumers will generally

forsake small discounts to avoid missing the opportunity

to purchase the product. This means that firms can either

manipulate their inventory, their pricing schedule, or both

in order to maximize profitability. Suppose a retailer has a

large stock of a product it needs to sell to make room for

incoming merchandise. When creating the pricing structurefor this item, our results suggest that the retailer will sell

more of that product at full price by advertising a low future

discount on the product. However, if that product is scarce

a larger discount will yield a higher full-price purchase

rate, and therefore maximize profit margin on the product

Furthermore, because individuals attach emotional value

to scarce products, managers can use this knowledge to lay

out the message in the most emotion-provoking way.

Table 3

Proportion of Subjects Who Chose to

Purchase for Study 2

Discount

Scarcity

Total

(Percent)

Low

(Percent)

High

(Percent)

Low 66.2 71.2** 68.8Moderate 56.5 72.4** 64.5

High 48.5+ 60.3 54.6

Total 57.2 67.9 62.6

+p < 0.05: represents a significant difference in relative to the total

average cell. **p < 0.01: represents a significant difference relative to

the low-scarcityhigh future discount cell.


10/16


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Fall 2013 451

low involvement magnified the effect of scarcity when the

decision involved clothing. Jeans, and clothing in general,

are a more visible product, and consequently, they imply a

greater social risk (White 1966). Low involvement entails a

low knowledge (Brucks 1985) and low motivation to process

(Rothschild 1984). Brannon and Brock (2001) found that

when motivation and knowledge are low, scarcity results

in consumers paying more attention to task-relevant cues.Those with a lack of motivation and knowledge about cloth-

ing and fashion are more likely to use the availability as a

task relevant cue of its value and desirability, and ultimately,

as a driver of purchase.

High-involvement consumers, by contrast, are not as

reliant on that cue. They may also shop more often, follow

the trends, and know whether or not the product is worth

the price. These individuals tend to be product experts.

They pay more attention to product information, spend

more time processing that information, and put forth more

effort to the search process (Forehand and Deshpand2001) to ensure that they make a wise choice (Celsi and

Olson 1988). A wise choice, in this case, means evaluating

all the alternatives, perhaps looking online, and making

sure they get the best price for the product. After all, even

high-involvement individuals seek low prices (Lichtenstein,

Bloch, and Black 1988), which could be why the discount

had a greater effect on this group.

The consumers level of involvement is therefore a

vital consideration for retailers looking to implement the

SID pricing schedule. While involvement is an individual

characteristic, some product categorieslike clothingare

intrinsically more involving than others. Lichtenstein,

Bloch, and Black point out: A buyer may feel quite differ-

ently about purchasing a higher-priced brand of peas than

about purchasing a higher-priced television (1988, p. 245).

These highly visible product categories are more likely to

have high-involvement shoppers who are less susceptible

to scarcity but are very conscious of the pricing structure.

For less visible products, involvement may not be as high,

meaning individuals will place more weight on availability

and use scarcity as a decision cue. Managers should use

this information to cater a SID schedule to their specific

product.

Finally, the successful implementation of the SID pricing

schedule requires a change in all aspects of the business.

Zaras success stems from the integration of its pricing

model to each function of the supply chain (Ferdows, Lewis,

and Machuca 2004). Their distribution centers have to be

responsive, their databases accurate and current, their staff

well-trained, and their designers cutt ing-edge and prolific.

It is easier to carry a large volume of the same product than

it is to carry a large assortment of products; but to truly

work in the retail sector, an extensive product breadth

must balance the lack of product depth. That way, when a

rack with three dresses runs out, customers will not leave

the store; they will find another rack with three different

dresses. Being out of stock is no longer a liability, but may

actually become an asset. Pricing promotions also typicallyincrease store traffic (Pan and Zinkhan 2006), and this has

been true in the case of Zara. The constant turnover of new

and unique products attracts more frequent visits than a

store with low product turnover.

One word of caution. Often when consumers miss a

bargain, they decide not to purchase that product at the

normal price because it would serve as a reminder of the

missed opportunity (Sevdalis, Harvey, and Yip 2006). While

Tsiros and Mittal (2000) found that regret positively influ

ences repurchase intentions, a fine line separates positively

influencing repurchase behavior and negatively affectingattitudes toward the company. A key component of the SID

model is transparency and truthfulness. Managers must be

up front about the discount as well as the stock of products

For instance, customers may feel manipulated if a retailer

carried dozens of a style of jeans but only placed a few on

the sales floor at one time. The unique cues of this pricing

schedule make it relevant only for products that will not be

replenished in the near future. Finally, the SID model shares

a foundation with Tsiros and Hardestys (2010) SDD model

and could effectively dovetail to this strategy to create a

comprehensive promotional pricing schedule.

Limitations and Future Research

While academic journals have validated the student

recruitment method and student data in general, it poses

a limitation in this study. Future research might test the

SID model in an actual retail setting so that real choices

could be observed. In this study, we isolated the effects of

scarcity and future discount into one purchase decision

respondents had a choice of buying the product now or a

the later date. Thus, this study is a snapshot of one interva

in the SID schedule. To fully assess the applicability of the

SID model, purchase decisions would have to be assessed

and measured at different intervals throughout the course

of the promotion.

Because the effect of involvement differed based on

the product type, this construct and how it relates to scar

city and future discount warrants further investigation

Lichtenstein, Bloch, and Black (1988) note that involvemen


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is product specific and varies across products and situa-

tions, and clearly this was the case in our study. A study

could be developed where the effects of scarcity and future

discount are measured across several industries/product

types to determine if involvement is industry or product

type specific.

Other moderators may be especially pertinent to the SID

model. For instance, the same discount can yield differentresponses when presented as either dollar-off or percentage-

off (Estelami 2003). In both the EUT and prospect theory,

how the message is framed and interpreted largely deter-

mines the behavioral response. Future research should

consider message framing and conflict of decision, as well as

deal proneness, need for uniqueness, regret, and repurchase

intentions. Finally, while the SID model may prove effective,

by its very nature of promoting sales that may never come

to fruition, it warrants ethical consideration.

REFERENCES

Anderson, James C., Mark Wouters, and Wouter van Rossum

(2010), Why the Highest Price Isnt the Best Price, Sloan

Management Review,51 (2), 6976.

Baron, Jonathon (1992), The Effect of Normative Beliefs on Antici-

pated Emotions,Journal of Personality and Social Psychology,

63 (2), 320330.

Baron, Reuben M., and David A. Kenny (1986), The Moderator-

Mediator Variable Distinction in Social Psychological

Research: Conceptual, Strategic, and Statistical Consider-

ations,Journal of Personality and Social Psychology,51 (6),

11731182.

Bell, David E. (1982), Regret in Decision Making Under Uncer-tainty, Operations Research,20 (5), 961981.

Bettman, James (1973), Perceived Risk and Its Components: A

Model and Empirical Test, Journal of Marketing Research,

10 (2), 184190.

Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetrault

(1990), The Service Encounter: Diagnosing Favorable and

Unfavorable Incidents,Journal of Marketing,54 (1), 7184.

Bloch, Peter H., and Marsha L. Richins (1983). A Theoretical

Model for the Study of Product Importance Perceptions,

Journal of Consumer Research,47 (3), 6981.

, Daniel L. Sherrell, and Nancy M. Ridgway (1986), Consumer

Research: An Extended Framework, Journal of Consumer

Research,13 (1), 119126.

Bozzolo, Anita, and Timothy C. Brock (1992), UnavailabilityEffects on Message Processing: A Theoretical Analysis and

Empirical Test,Basic and Applied Social Psychology,13 (1),

93101.

Brannon, Laura A., and Timothy C. Brock (2001), Limiting Time

for Responding Enhances Behavior Corresponding to the

Merits of Compliance Appeals: Refutations of Heuristic-

Cue Theory in Service and Consumer Settings,Journal of

Consumer Psychology,10 (3), 133146.

Brock, Timothy C. (1968), Implications of Commodity Theory

for Value Change, inPsychological Foundations of Attitudes,

Anthony G. Greenwald, Timothy C. Brock, and Thomas M.

Ostrum, eds., New York: Academic Press, 243275.

Brucks, Merrie (1985), The Effects of Product Class Knowledge on

Information Search Behavior,Journal of Consumer Research,

12 (1), 116.

Burton, Scot, Donald Lichtenstein, Richard Netemeyer, and Judith

Garretson (1998), A Scale for Measuring Attitude Toward Pri-

vate Label Products and an Examination of Its Psychological

and Behavioral Correlates,Journal of the Academy of Marketing

Science,26 (4), 293306.

Campbell, Margaret C. (1999), Perceptions of Price Unfairness:

Antecedents and Consequences,Journal of Marketing Research,

36 (2), 187199.

Celsi, Richard L., and Jerry C. Olson (1988), The Role of Involve-

ment in Attention and Comprehension Processes,Journal of

Consumer Research,15 (2), 210224.

Cialdini, Robert B. (1993),Influence: Science and Practice,New York:

HarperCollins.

DeGraba, Patrick (1995), Buying Frenzies and Seller-Induced Excess

Demand,Journal of Economics,26 (2), 331342.

Dhar, Ravi, and Stephen M. Nowlis (1999), The Effects of Time

Pressure on Consumer Choice Deferral,Journal of Consumer

Research,6 (4), 389405.

Estelami, Hooman (2003), The Effect of Price Presentation Tactics

on Consumer Evaluation Effort of Multi-Dimensional Prices,

Journal of Marketing Theory and Practice,11 (1), 115.

Ferdows, Kasra, Michael A. Lewis, and Jose A.D. Machuca (2004),

Rapid-Fire Fulfillment, Harvard Business Review,82 (11),

104110.

Folkes, Valerie S. (1988), The Availability Heuristic and Perceived

Risk,Journal of Consumer Research,15 (1), 1323.

Forehand, Mark R., and Rohit Deshpand (2001), What We

See Makes Us Who We Are: Priming Self-Awareness and

Advertising Response,Journal of Marketing Research,38 (3),

336348.

Frank, Robert H. (2007), The Natural Economist: In Search of Explana-

tions for Everyday Enigmas,New York: Basic Books.Hall, Robert E. (1978), Stochastic Implications of the Life Cycle-

Permanent Income Hypothesis: Theory and Evidence,Journal

of Political Economy,86 (6), 971988.

Holmes, Elizabeth (2011), Retailers to Rein in Discounts, Wall

Street Journal,July 8 (available at http://online.wsj.com/article/

SB10001424052702303544604576431572289761998.html).

Inman, J. Jeffrey, Anil C. Peter, and Priya Raghubir (1997), Framing

the Deal: The Role of Restrictions in Accentuating Deal Value,

Journal of Consumer Research,24 (1), 6879.

International Council of Shopping Centers (2009), How the

Recession Has Impacted Consumer Shopping Habits, 2009

Shopping Habits Report, New York.

Jacobson, Robert, and Carl Obermiller (1990),The Formation of

Expected Future Price: Reference Price for Forward-LookingCustomers,Journal of Consumer Research,16 (4), 420432.

Kahneman, Daniel, and Amos Tversky (1979), Prospect Theory:

An Analysis of Decision Under Risk,Econometrica,47 (2),

263291.

Laurent, Gilles, and Jean Nol Kapferer (1985), Measuring Con-

sumer Involvement Profiles,Journal of Marketing Research,

22 (1), 4153.

Lichtenstein, Donald R., Peter H. Bloch, and William C. Black

(1988), Correlates of Price Acceptability,Journal of Consumer

Research,15 (2), 243252.


13/16

Fall 2013 453

Lynn, Michael (1989), Scarcity Effects on Desirability: Mediated

by Assumed Responsiveness?Journal of Economic Psychology,

10 (2), 257274.

MacKinnon, David P., and James H. Dwyer (1993), Estimating

Mediated Effects in Prevention Studies,Education Review,

36 (4), 144158.

Mattioli, Dana (2012), Lululemons Secret Sauce Wall Street

Journal,March 22 (available at http://online.wsj.com/article/

SB10001424052702303812904577295882632723066.html).

Mazumdar, Tridib, S.P. Raj, and Indrajit Sinha (2005), Reference

Price Research: Review and Propositions,Journal of Market-

ing,69 (4), 84102.

Mittal, Banwari (1995), A Comparative Analysis of Four Scales

of Consumer Involvement,Psychology & Marketing,12 (7),

663682.

Mongin, Philippe (1997), Expected Utility Theory, inHandbook

of Economic Methodology,John Bryan Davis, D. Wade Hands,

and Uskali Maki, eds., London: Edward Elgar, 342350.

Monroe, Kent B. (1973), Buyers Subjective Perceptions of Price,

Journal of Marketing Research,10 (1), 7080.

(2003), Pricing: Making Profitable Decisions, 3d ed., Burr

Ridge, IL: Irwin.

Neubert, Mitchell J., K. Michelle Kacmar, Dawn S. Carlson, Law-

rence B. Chonko, and James A. Roberts (2008), Regulatory

Focus as a Mediator of the Influence of Initiating Structure

and Servant Leadership on Employee Behavior,Journal of

Applied Psychology,93 (6), 12201233.

Pan, Yue, and George Zinkhan (2006), Determinants of Retail

Patronage: A Meta-Analytic Perspective,Journal of Retailing,

82 (3), 229243.

Rao, Akshay, and Wanda A. Sieben (1992), The Effect of Prior

Knowledge on Price Acceptability and the Type of Infor-

mation Examined, Journal of Consumer Research, 19 (2),

256270.

Richins, Marsha L., and Peter H. Bloch (1986), After the New

Wears Off: The Temporal Context of Product Involvement,

Journal of Consumer Research,13 (2), 280285.Rook, Dennis W. (1987), The Buying Impulse,Journal of Con-

sumer Research,14 (2), 189199.

, and Robert J. Fisher (1995), Normative Influences on

Impulsive Buying Behavior,Journal of Consumer Research,

22 (3), 305313.

Rothschild, Michael L. (1984), Perspectives on Involvement:

Current Problems and Future Directions, in Advances in

Consumer Research,vol. 11, Thomas C. Kinnear, ed., Provo,

UT: Association for Consumer Research, 216217.

Samli, A. Coskun, and Laurence W. Jacobs (1993), Internationa

Pricing Decisions: A Diagnostic Approach,Journal of Market

ing Theory and Practice,1 (4), 2941.

Sevdalis, Nick, Nigel Harvey, and Michelle Yip (2006), Regret

Triggers Inaction Inert iaBut Which Regret and How?Brit

ish Journal of Social Psychology,45 (4), 839853.

Sivakumar, K. (1996), Tradeoff Between Frequency and Depth of

Price Promotions,Journal of Marketing Theory and Practice

4 (1), 18.

Sobel, Michael E. (1982), Asymptotic Confidence Intervals for

Indirect Effects in Structural Equations Models, in Socio

logical Methodology, Samuel Leinhart, ed., San Francisco

Jossey-Bass, 290312.

Suri, Rajneesh, and Kent B. Monroe (2003), The Effects of Time

Constraints on Consumers Judgments of Prices and Prod

ucts,Journal of Consumer Research,30 (1), 92104.

, Chiranjeev Kohli, and Kent B. Monroe (2007), The Effects

of Perceived Scarcity on Consumers Processing of Price

Information,Journal of the Academy of Marketing Science

35 (1), 89100.

Sweeney, Jillian C., and Geoffrey N. Soutar (2001), Consume

Perceived Value: The Development of a Multiple Item Scale,

Journal of Retailing,77 (2), 203220.

Tsiros, Michael, and David M. Hardesty (2010), Ending a Price

Promotion: Retracting It in One Step or Phasing It out Gradu

ally,Journal of Marketing,74 (1), 4964.

, and Vikas Mittal (2000), Regret: A Model of its Antecedent

and Consequences in Consumer Decision Making,Journa

of Consumer Research,26 (4), 401417.

Tversky, Amos, and Daniel Kahneman (1973), Availability: A

Heuristic for Judging Frequency and Probability, Cognitive

Psychology,5 (2), 201232.

von Neumann, John, and Oskar Morgenstern (1947), Theory o

Games and Economic Behavior,2d ed., Princeton: Princeton

University Press.

White, Irving S. (1966), The Perception of Value in Products,

in On Knowing the Consumer,J.W. Newman, ed., New YorkWiley, 90106.

Winer, Russel S. (1986), A Reference Price Model of Brand Choice

for Frequently Purchased Products, Journal of Consume

Research,13 (2), 250256.

Zaichkowsky, Judith Lynn (1985), Measuring the Involvemen

Construct,Journal of Consumer Research,12 (3), 341352.


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APPENDIX A

Scenario and Items for Study 1

A lot of people have a favorite musical band or artist. Think about your favorite band or artist and write it here

__________________.

About how much would you be willing to spend for a live CD of this band or artists first-ever performance? Please

write the amount here __________________.

Now imagine that this Saturday morning you go your local flea market and find that CD [three of them in fact] except

it is priced $5 higher than what you wrote above. You know that the flea market has a policy of selling any unsold items

at half off the following Saturday, but you have no idea if [any of] the CD[s] will still be there next week. You cannot hide

a CD, place one on hold, or do anything of that nature. Would you purchase a CD that day or come back the following

Saturday with the hope that at least one was left and you could buy it for half off?

Table A1

Scale Items with Factor Loadings and Reliabilities for Study 1

Involvement with Product Class (Laurent and Kapferer 1985)

The clothes I buy are very important to me.For me, it matters what clothes I own.

Clothes are an important part of my life.

0.85

0.910.88

0.85

Perceived Emotional Value (Sweeney and Soutar 2001)

This CD is one that I would enjoy.

Buying this CD would make me want to listen to it.

Owning this CD would make me feel good.

Owning this CD would give me pleasure.

I would feel relaxed about owning this CD.

0.91

0.85

0.86

0.89

0.90

0.81

Risk Aversion (Burton et al. 1998)

I dont like to take risks.

Compared to most people I know, I like to gamble on things.

Compared to most people I know, I like to live on the edge.

I have no desire to take unnecessary chances on things.

0.76

0.74

0.82

0.85

0.60

Impulse Buying (Rook and Fisher 1995)I often buy things spontaneously.

Just do it describes the way I buy things.

I often buy things without thinking.

I see it, I buy it describes me.

Buy now, think about it later describes me.

Sometimes I feel like buying things spur of the moment.

I buy things according to how I feel at the moment.

I carefully plan most of my purchases.

Sometimes I am a bit reckless about what I buy.

0.910.80

0.85

0.86

0.86

0.80

0.67

0.72

0.56

0.72

Note: Items anchored by 1 = strongly disagree to 7 = strongly agree.


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Fall 2013 455

APPENDIX B

Scenario and Items for Study 2

Imagine that you need a new pair of jeans, and that this Saturday you go to the mall in search of a pair. A lot of people

have a favorite or go-to brand when it comes to jeans. Think about your favorite brand or a brand you like and write

it here ________________________.

Imagine that the brand you wrote above sells one pair of jeans that has the exact style, fit, color, etc., that you are

looking for. About how much would you be willing to spend for a pair of these jeans? Please write the amount

here ________________________. (If you cannot think of a specific brand, write how much youd pay for a really nice pair

of jeans with the exact style, fit, color, etc. you like)

Now imagine that you enter a store that is running a promotion on the brand of jeans that you wrote above. You soon

find a table display with 10 [2] pairs of the exact style, fit, color, etc., that you are looking for in your size. Above the table

hangs a sign that reads, All jeans 50% off [25% off, 10% off] next Saturday. Keeping in mind that this pair of jeans wil

not be available in other stores or online,you begin to debate purchasing the jeans today or coming back next Saturday for

the discount.

Table B1

Scale Items with Factor Loadings and Reliabilities for Study 2

Involvement with Product Class (Laurent and Kapferer 1985)

The clothes I buy are very important to me.

For me, it matters what clothes I own.

Clothes are an important part of my life.

0.91

0.85

0.85

0.82

Impulse Buying (Rook and Fisher 1995)

I often buy things spontaneously.

Just do it describes the way I buy things.

I often buy things without thinking.

I see it, I buy it describes me.

Buy now, think about it later describes me.

Sometimes I feel like buying things spur of the moment.

I buy things according to how I feel at the moment.Sometimes I am a bit reckless about what I buy.

0.94

0.79

0.86

0.85

0.86

0.83

0.80

0.820.78

Note: Items anchored by 1 = strongly disagree to 7 = strongly agree.


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