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Planning to Make Unplanned Purchases? The Role of In-Store Slack in Budget Deviation

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We propose that consumers have mental budgets for grocery trips that are typically composed of both an itemized portion and in-store slack. We conceptualize the itemized portion as the amount that the consumer has allocated to spend on items planned to the brand or product level and the in-store slack as the portion of the mental budget that is not assigned to be spent on any particular product but remains available for in-store decisions. Using a secondary data set and a field study, we find incidence of in-store slack. Moreover, we find support for our framework predicting that the relationship between in-store slack and budget deviation (the amount by which actual spending deviates from the mental trip budget) depends on factors related to desire and willpower. (c) 2010 by JOURNAL OF CONSUMER RESEARCH, Inc..
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2010 by JOURNAL OF CONSUMER RESEARCH, Inc. Vol. 37 August 2010
All rights reserved. 0093-5301/2010/3702-0011$10.00. DOI: 10.1086/651567
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
Planning to Make Unplanned Purchases? The
Role of In-Store Slack in Budget Deviation
KAREN M. STILLEY
J. JEFFREY INMAN
KIRK L. WAKEFIELD
We proposethat consumers havementalbudgetsforgrocery tripsthataretypically
composed of both an itemized portion and in-store slack. We conceptualize the
itemized portion as the amount that the consumer has allocated to spend onitems
planned to the brand or product level and the in-store slack as the portion of the
mentalbudgetthatisnotassignedto bespent onany particularproductbutremains
available for in-store decisions. Using a secondary data set and a field study, we
find incidence of in-store slack. Moreover, we find support for our framework pre-
dicting that the relationship between in-store slack and budget deviation (the
amount by which actual spending deviates from the mental trip budget) depends
on factors related to desire and willpower.
R
esearchers and practitioners alike have commonly as-
sumed that unplanned purchases are largely due to
consumers’ susceptibility to in-store stimuli (Heilman, Nak-
amoto, and Rao 2002; Park, Iyer, and Smith 1989) and that,
as a result, unplanned purchases represent unplanned spend-
ing (i.e., Mukopadhyay and Johar 2007). On the other hand,
two major studies have reported the surprising finding that
actual spending closely approximated spending intentions
despite the fact that over 50% of purchases were unplanned
(Kollat and Willett 1967; POPAI 1995). In this research, we
draw upon mental budgeting to provide an explanation for
this apparent paradox.
While economists have traditionally assumed that money
is fungible, research has shown that consumers use a form
of mental budgeting where they allocate money to mental
accounts and attempt to resist further purchases when the
Karen M. Stilley (kstilley@katz.pitt.edu) is a postdoctoral fellow and
J. Jeffrey Inman (jinman@katz.pitt.edu) is the AlbertWesley FreyProfessor
of Marketing and Associate Dean of Research and Faculty at the Joseph
M. Katz Graduate School of Business, University of Pittsburgh, PA 15260.
Kirk L. Wakefield (Kirk_Wakefield@baylor.edu) is professor of marketing
at the Hankamer School of Business, Baylor University, Waco, TX 76798.
Correspondence: Karen M. Stilley. The authors thank the Point of Purchase
Advertising Institute for providing the data used in the preliminary study.
Our field study was funded through the support of the University of Pitts-
burgh and Baylor University. The authors would also like to thank the
editor, associate editor, and the reviewers for their helpful feedback and
support throughout the review process. This article is based on the first
author’s dissertation.
John Deighton served as editor and Brian Ratchford served as associate
editor for this article.
Electronically published February XX, 2010
budget is depleted (Heath and Soll 1996; Thaler 1985). Al-
though studies have found that consumers have budgets for
groceries in general (Heath and Soll 1996; Heilman et al.
2002), we take this further to propose that consumers have
a mental budget, even if implicit, at the shopping trip level
that includes room for unplanned purchases. We posit that
consumers anticipate the occurrence of unplanned purchases
in their spending expectations because they realize they have
neither the time (Zeithaml 1985) nor the cognitive resources
to fully plan (Bettman 1979) and/or because they want to be
able to make spontaneous decisions while in-store (Stern
1962).
Formally, we propose that consumers’ shopping trip men-
tal budgets are typically composed of an itemized portion
and in-store slack. We conceptualize the itemized portion
as the amount of money that the consumer has allocated to
spend on items planned to the brand or product level and
the in-store slack as the portion of the mental budget that
is not assigned to be spent on any particular product before
the shopping trip begins. Instead, the funds remain available
for in-store decisions. We first provide evidence of in-store
slack. We then examine the question of whether consumers’
strategy of allowing themselves in-store slack is an effective
tactic for adhering to their overall total budget or whether
having in-store slack leads to overspending or underspend-
ing. To accomplish this goal, we present and test a frame-
work describing how the relationship between in-store slack
and budget deviation is contingent on consumer character-
istics (impulsiveness and income) and trip characteristics
(aisles shopped and trip length).
This study makes several important contributions to the
literature. First, we find incidence of in-store slack using
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both secondary data and a field study. That is, consumers’
mental budgets for the shopping trip include room to make
unplanned purchases. Using free response data, we show
that consumers employ this strategy both because they an-
ticipate “forgotten needs” as well as because they realize
that they will encounter “unplanned wants”—with some re-
spondents even explicitly indicating that they expect tomake
impulse purchases. In contrast to research that shows that
consumers fail to predict future behavior (e.g., Khan and
Dhar 2007; Simonson 1990; Zauberman 2003), our research
suggests that the average consumer correctly anticipates un-
planned purchases. Additionally, we contribute to the mental
budgeting literature by showing that consumers’ spending
on grocery trips is remarkably close to their plan to spend
and that this difference does not depend on whether the
consumer has a formal or implicit grocery budget.
We also make contributions to the dual process literature
that depicts self-control as a battle between desire and will-
power (Hoch and Loewenstein 1991; Shiv and Fedorikhin
1999; Vohs and Faber 2007). Specifically, we find that bud-
get deviation depends on a three-way interaction between
in-store slack, aisles shopped, and impulsiveness.Whenonly
select aisles are shopped, our results indicate a negative
relationship between slack and budget deviation and suggest
that consumers are not spending all of the money in their
in-store slack. When most aisles are shopped, however,slack
has no impact on budget deviation for shoppers low in im-
pulsiveness but leads to overspending for highly impulsive
shoppers. This suggests that exposing a shopper to more
environmental cues creates enough desire for items that she
ultimately needs to exert self-control to stay within her men-
tal budget but that highly impulsive individuals have in-
sufficient willpower to do so. We also show that in-store
slack attenuates the relationship between trip length (i.e.,
time spent in-store) and budget deviation, suggesting that
making unplanned purchases using the in-store slack may
reduce the self-control depletion that is likely to occur as
the trip progresses.
The remainder of this article is organized as follows. We
first describe our conceptualization of in-store slack and
leverage a large existing field study to provide evidence of
in-store slack. We then develop our conceptual framework
and associated hypotheses regarding the relationship be-
tween in-store slack and budget deviation. Next, we test our
hypotheses via a field study with over 150 respondents
where we measure the amount of in-store slack. Addition-
ally, we present free response data that examines the reasons
why consumers have in-store slack. We close with a dis-
cussion of theoretical contributions as well as implications
for managers and consumers.
IN-STORE SLACK
Thaler argues that consumers use mental budgets in order
to “facilitate making rational trade-offs between competing
uses for funds” (Thaler 1999, 11), and Thaler and Shefrin
(1981) propose that consumers use mental budgets as a form
of self-control to ensure that they stay within aggregate
spending limits. Grocery shopping is an example of a con-
sumer domain where budgeting is commonly found. While
studies have found that consumers have mental budgets for
groceries in general (Heath and Soll 1996; Heilman et al.
2002), we argue that consumers have a mental budget for
the amount of money that they plan to spend on a specific
grocery shopping trip and that this trip mental budget in-
cludes room for unplanned purchases. Grocery shopping is
a routine activity, and consumers’ shopping patterns tend
to display a weekly cycle (Kahn and Schmittlein 1989). As
a result, a shopper with an explicit weekly budget should
have a mental budget for each shopping trip. Even if a
consumer does not maintain an explicit budget, she will still
have experience with the average amount of money that she
has spent on similar trips due to the routinized nature of
grocery shopping. Therefore, she will use spending levels
from past trips as a basis for future spending expectations,
as is commonly done by organizations (Cyert and March
1963; Wildawsky 1964).
There is a large body of work arguing that consumer
decisions are made with regard to reference points and that
expectations are a source of reference points (i.e., Kahneman
and Tversky 1979; Thaler 1985; Tversky and Kahneman
1991). Just as consumers derive negative utility from paying
more than a reference price for a specific item (i.e., Grewal,
Monroe, and Krishnan 1998; Kalyanaram and Winer 1995;
Thaler 1985; Winer 1986), consumers should also derive
negative utility from exceeding spending expectations for
the trip. In this article, we refer to the trip spending expec-
tation as a mental budget regardless of whether the spending
expectation originates from explicit budgeting practices or
is a more implicit budget based on prior spending behavior.
This terminology is consistent with Hauser and Urban’s
basic notion that “in a single period the consumer faces a
fixed budget that s/he must allocate” and “for some goods
s/he plans explicitly, for others s/he does not” (Hauser and
Urban 1986, 446) and with Novemsky and Kahneman’s
(2005) definition of a mental budget as a “consumer’s set
of intentions for money.” Additionally, in our field study,
we examine whether budget deviation varies depending on
whether the budget is explicit or implicit.
Most consumers have forgotten necessities on past trips
and may be aware that they have a tendency to succumb to
impulses in-store (Rook and Fisher 1995); therefore, we
expect that consumers learn to anticipate the occurrence of
unplanned purchases. It is well documented in the self-reg-
ulation literature that individuals employ techniques to help
resist temptation (Baumeister, Heatherton, and Tice 1994;
Loewenstein 1996; Wertenbroch 1998), and one potential tac-
tic is for consumers to attempt to avoid unplanned purchases
by setting a tight trip budget before they begin their shopping
trips. Instead, our thesis is that consumers manage this bal-
ancing act by leaving room in their mental budgets for un-
planned purchases. We refer to this amount as the in-store
slack. That is, we argue that consumers anticipate making
unplanned purchases and allocate in-store slack for this
purpose.
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IN-STORE SLACK IN BUDGET DEVIATION PROOF 3
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We argue that there are at least two reasons why con-
sumers would have in-store slack. First, some consumers
will have in-store slack because they realize they are unable
to completely plan all the items they need to buy. Consumers
tend to have difficulty retrieving all their grocery needs from
memory (Bettman 1979) due to limited processing capacity
(Miller 1956). Therefore, consumers often need to rely on
external cues that aid retrieval from memory (Lynch and
Srull 1982; Tulving and Psotka 1971). Since grocery shop-
ping is a common occurrence, shoppers will be aware that
in-store stimuli will trigger forgotten needs and will incor-
porate this expectation into their mental budgets. Second,
some consumers may purposefully leave themselves some
slack because they want the financial flexibility to sponta-
neously make decisions in-store. For example, Stern (1962)
finds that shoppers purposefully wait until they are in-store
to determine what they want to buy because they want to
get ideas for dinner. Likewise, Iyer (1989) reports that 42%
of study participants who made an unplanned purchase cited
“item required for a recipe made up in-store” as a reason.
Being able to make such spontaneous decisions has been
shown to contribute to the hedonic value of shopping (Babin,
Darden, and Griffin 1994). This leads to our main thesis
that consumers will leave room in their trip budgets to make
unplanned purchases.
PRELIMINARY STUDY
To provide preliminary evidence of in-store slack, we
employ data from the 1995 customer intercept study con-
ducted by the Point of Purchase Advertising Institute (PO-
PAI). In that study, over 2,000 customers were intercepted
as they entered grocery stores located in 14 cities across the
United States (see Inman, Winer, and Ferraro 2009). Before
they entered the store, respondents were asked what items
they planned to purchase and how much they intended to
spend. Planned items could be generally planned items such
as vegetables or specifically planned items such as Kellogg’s
Frosted Flakes. After consumers checked out, interviewers
recorded information regarding the actual items purchased
and the actual amount spent. Kollat and Willett (1967) have
previously found that this research format does not affect
the amount that consumers spend (we also provide evidence
of this in our field study).
One surprising finding from the POPAI (1995) data was
that, despite the fact that over 50% of the purchases were
unplanned, actual spending closely approximated spending
intentions. In fact, the average planned spend was $45.99,
while the average total amount spent was $49.82—the av-
erage budget deviation (defined as total amount spent
total planned spend) was only $3.83. These descriptive sta-
tistics strongly suggest that shoppers have a mental budget
for the trip that includes room to make unplanned purchases
without exceeding this budget. Although the POPAI study
did not directly investigate in-store slack, we use this data
set to provide preliminary evidence of in-store slack. Spe-
cifically, we estimate the relationship between number of
planned purchases and number of unplanned items while
controlling for the trip budget.
In lieu of a mental budget for the trip, one would expect
that the number of unplanned purchases would increase with
the number of planned purchases because larger trips are
associated with greater amounts of unplanned purchasing
(Kollat and Willet 1967). On the other hand, if consumers
have fixed budgets but varying amounts of in-store slack,
then the results should tell a different story. Individuals who
planned fewer items but had the same total trip budget (i.e.,
had more in-store slack) should make more unplanned pur-
chases. Conversely, those who planned a greater number of
items should make fewer unplanned purchases because they
have less room in their budgets to do so. To test this, we
employ the POPAI data to estimate the effect of the number
of planned purchases on the number of unplanned purchases,
both with and without the trip budget variable. In addition,
we include demographic variables and the covariates of aisles
shopped and trip length—which have been shown to be re-
lated to the likelihood of an unplanned purchase (Inman et
al. 2009).
The results are presented in table 1. As expected, there
is a positive relationship between the number of planned
items and the number of unplanned items p .13, p
!
.01) when the trip budget is not included. However, this
result reverses when the amount of the trip budget is in-
cluded. There is a positive relationship between the trip
budget and number of unplanned items p .18, p
! .01),
but there is now a negative relationship p .35, p
!
.01) between the number of planned purchases and the num-
ber of unplanned purchases. This result is consistent with
our conceptualization of in-store slack. Individuals who have
the same trip budget but planned a fewer number of planned
purchases made more unplanned purchases because they had
more in-store slack. Additionally, the fact that including the
trip budget variable increases the significantly from 30.7%
2
R
to 59.9% indicates the importance of including thetrip budget
when examining in-store decision-making behavior.
Given this initial support for our thesis that consumers’
mental budget for the shopping trip includes in-store slack,
the next logical question is whether this is an effective strat-
egy. More specifically, we consider how the size of the in-
store slack is related to budget deviation, which we define
as the difference between the total mental budget and the
actual total amount spent. That is, how does having in-store
slack influence a consumer’s tendency to overspend or
underspend relative to her overall budget for the trip?
HYPOTHESES
In the mental budgeting literature, Heath and Soll (1996)
argue that consumers will underconsume or overconsume
in an effort to stick to their total mental budgets. This sug-
gests that there will be no relationship between in-store slack
and budget deviation, but other streams of literature provide
conflicting predictions. On the one hand, shopping momen-
tum (Dhar, Huber, and Khan 2007) suggests that if the shop-
per allows herself to start making unplanned purchases, then
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TABLE 1
NUMBER OF UNPLANNED PURCHASES (POPAI STUDY)
Without trip budget With trip budget
Parameter estimate t-value Parameter estimate t-value
Intercept 1.17*** 2.09 .69*** 1.60
Number of planned items .13
*** 2.63 .35*** 8.79
Trip budget . . . . . . .18
*** 39.92
Aisles shopped (MAISLES) 2.18
*** 12.58 1.44*** 10.81
Trip length (TLENGTH) .18
*** 20.06 .09*** 12.53
Income (INC) .04
*** 5.91 .01 1.54
Household size (HH) 1.28
*** 10.17 .48 4.87
Model
2
R .307 .599
Model p-value
!.001 !.001
F-statistic F(5,2191) p 194.50 F(6,2190) p 545.48
***p ! .01.
momentum would take over and shewould continue to make
unplanned purchases (presumably to the point of exceeding
the total mental budget). On the other hand, a self-control
depletion argument (Muraven and Baumeister 2000) sug-
gests a negative relationship between slack and budget de-
viation. Specifically, we posit that if the consumer tries to
force herself to not make any unplanned purchases (i.e., has
no in-store slack), she would become so depleted by the end
of trip that she would ultimately make unplanned purchases
and exceed her total budget. Therefore, being able to make
unplanned purchases using in-store slack should reduce de-
pletion and decrease the tendency to exceed the total budget.
Furthermore, shoppers with in-store slack may be expecting
to have their needs and/or wants cued by in-store stimuli
such as displays or aromas (i.e., Bettman 1979; Inman et
al. 2009). If the shopper does not encounter sufficient in-
store stimuli or does not process the information, then she
may underspend relative to her total mental budget.
Given the conflicting predictions, it is important to con-
sider individual or trip factors that might influence the re-
lationship between in-store slack and budget deviation.
Therefore, we now present our hypotheses predicting how
the relationship between the size of the in-store slack and
budget deviation will vary according to consumer and trip
characteristics. Figure 1 summarizes the hypotheses that are
tested in the field study.
As the primary basis for our hypotheses, we draw upon
the conceptual model proposed by Hoch and Loewenstein
(1991), which asserts that self-control depends on the inter-
play between desire and willpower. As described in Hoch
and Loewenstein (1991), proximity is a key aspect of desire
(Faber and Vohs 2004; Mischel and Grusec1967). Likewise,
Laibson (2001) argues that exposure to environmental cues
increases the perceived marginal utility of consumption. Fur-
ther, environmental cues also aid in retrieval of forgotten
needs (Lynch and Srull 1982; Tulving and Psotka 1971). In
a grocery context, the number of aisles shopped will influ-
ence the shopper’s proximity to items and thereforeexposure
to environmental cues. As a result, shopping more aisles
should increase desire for a variety of items. Consistent with
these arguments, Inman et al. (2009) find that the number
of aisles shopped increased the probability of a given pur-
chase being unplanned. Consequently, we expect that budget
deviation will increase as more aisles are shopped.
Beyond the main effect of the number of aisles shopped,
we also predict that aisles shopped will interact with in-
store slack. Research shows that consumers’ marginal pro-
pensity to consume varies depending on the mental account,
with current income accounts being more readily spent than
savings accounts (Shefrin and Thaler 1988). Likewise, we
expect that consumers will have a high marginal propensity
to consume from their in-store slack because they are men-
tally prepared to spend their in-store slack on the current
trip. Even with a higher marginal propensity to consume,
consumers should not make a purchase unless a need be-
comes salient. If a consumer only shops a few select aisles,
she may not encounter enough stimuli to spend all of the
funds allotted to in-store slack. On the other hand, a con-
sumer who shops more aisles will be inundated with en-
vironmental cues. In this case, the shopper may find enough
items to purchase on an unplanned basis that she will con-
sume her in-store slack or even exceed it. Thus,
H1: There will be a positive interaction between in-
store slack and number of aisles shopped such
that in-store slack will be associated with greater
budget deviation when most aisles are shopped
than when only needed aisles are shopped.
As the shopper is exposed to more in-store cues, she may
need to exert self-control to stay within her trip mental bud-
get. Therefore, we also need to consider factors that influ-
ence willpower. Impulsiveness is an individual difference
variable that is of obvious relevance in this regard. Ac-
cording to Puri (1996), impulsiveness is characterized by
low availability of cognitive thoughts related to impulse
behaviors, so the individual is more likely to engage in such
behaviors. Consequently, impulsiveness has been shown to
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FIGURE 1
CONCEPTUAL MODEL
increase the tendency to make hedonic choices (Shiv and
Fedorikhin 1999) and impulse purchases (Rook and Fisher
1995; Vohs and Faber 2007).
Based on this prior research, we expect a positive rela-
tionship between impulsiveness and budget deviation, but
we also posit that impulsiveness will qualify the moderating
effect of aisles shopped on the relationship between slack
and budget deviation. As already discussed, shopping more
aisles will lead to exposure to more environmental cues. If
only select aisles are shopped, there may not be sufficient
cue exposure to tempt the shopper to make enough unplan-
ned purchases to exceed her mental budget. As long as in-
store slack is available for unplanned purchases, there will
be no need to exert self-control to stay within the budget,
and, consequently, impulsiveness will play a lesser role. On
the other hand, shopping all aisles may tempt the individual
to make unplanned purchases beyond the amount of the in-
store slack. In this case, the shopper will need to exert self-
control to stay within her mental budget. Highly impulsive
individuals will be less able to exert this control and there-
fore will be more likely to exceed their budgets when shop-
ping most aisles. Therefore,
H2: There will be a positive three-way interaction
between in-store slack, aisles shopped, and im-
pulsiveness for the dependent variable of budget
deviation. Specifically, higher impulsivenesswill
be associated with a more positive relationship
between slack and budget deviation when all
aisles are shopped than when only needed aisles
are shopped.
Another factor that influences willpower is depletion of
self-regulatory resources, which are conceptualized as a
global, general pool of resources (Baumeister et al. 1998;
Muraven and Baumeister 2000; Vohs and Faber 2007). In
laboratory studies, numerous manipulations have been
shown to decrease self-control performance, such as atten-
tion control, mental control, and emotional-behavioral con-
trol (Muraven, Tice, and Baumeister 1998; Vohs and Faber
2007; Wegner 1989). In addition, exposure to environmental
factors, such as noise (Cohen et al. 1980; Glass, Singer, and
Friedman 1969; Hartley 1973), crowding (Evans 1979;
Sherrod 1974), and proximity to a tempting product (i.e.,
Vohs and Heatherton 2000), deplete self-regulatory re-
sources and lead to decreased self-control performance (see
Muraven and Baumeister 2000 for a review). In this re-
search, we examine a naturally occurring behavior that is
correlated with depletion—shopping trip length. The longer
the shopper spends in the store, the longer she will be ex-
posed to tempting products, as well as to noise and crowds.
Therefore, self-regulatory depletion should increase as the
trip length increases, reducing the tendency to stay within
the mental budget. Therefore, we posit that
H3: The longer the shopping trip, the greater the bud-
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TABLE 2
FIELD STUDY SUMMARY STATISTICS
Shopper characteristic %
Income level:
Less than $20,000 20.9
$20,000–39,999 31.4
$40,000–59,999 22.9
$60,000–$79,999 9.8
$80,000–$99,999 9.2
$100,000–119,999 2.6
$120,000+ 3.3
Shopping pattern:
Select aisles 52.9
Most aisles 37.3
All aisles 9.8
Have (explicit) grocery budget 30.7
get deviation.
Although prior research suggests that shoppers’ self-reg-
ulatory resources will become more depleted as the trip
progresses, we argue that having in-store slack should re-
duce the degree to which this occurs. According to Muraven
and Baumeister (2002), acts of self-control deplete self-reg-
ulatory resources and therefore reduce the individual’s sub-
sequent ability to exert self-control. Therefore, we posit that
if the consumer has no in-storeslack and tries to force herself
to not make any unplanned purchases, she will frequently
have to exert self-control and will become so depleted by
the end of trip that she will ultimately make unplanned
purchases and exceed her mental budget. On the other hand,
being able to make unplanned purchases using in-store slack
should lessen the depletion of self-regulatory resources that
will occur as the trip progresses. As a result, the individual will
have more willpower to stay within her budget. Therefore,
H4: The positive relationship between trip length and
budget deviation will be attenuated by in-store
slack.
Finally, we consider the moderating role of income on
the relationship between in-store slack and budget deviation.
As discussed earlier, a shopper will have a high marginal
propensity to consume using funds in her in-store slack.
However, even if the shopper is mentally prepared to spend
the money, the shopper will not buy an item if she perceives
the price to be too high. Higher-income households tend to
be less price sensitive (Ainslie and Rossi 1998; Mulhern,
Williams, and Leone 1998; Wakefield and Inman 2003) and
therefore should be less discriminating as to which items
they purchase with their in-store slack. Further, budgets tend
to be less constraining for higher-income individuals (Thaler
1999), which suggests that they will not be as motivated to
stay within their mental budgets. Therefore, we predict that
H5: The greater the shopper’s income, the more pos-
itive the relationship between in-store slack and
budget deviation.
FIELD STUDY
While the POPAI data provided initial support for our
thesis that a consumer’s mental budget for the shopping trip
includes in-store slack, an obvious limitation of this data
set is that we did not have a measure of each consumer’s
in-store slack. Therefore, we conducted a field study to more
directly assess the occurrence of in-store slack as well as to
examine the relationship between in-store slack and budget
deviation. In this study, we replicate the procedure of the
POPAI study while addressing some of its limitations. In
the fall of 2006, 175 customers were systematically inter-
cepted as they entered three different grocery stores located
in a southwestern U.S. city. We selected every tenth shopper,
or one every 5 minutes, whichever came first. Respondents
were offered a $10 incentive, which was given to them at
the end of the survey for use on future shopping trips to
mitigate a windfall effect on the current trip (Heilman et al.
2002). As in the POPAI study, respondents were asked what
items they planned to purchase before they entered the store.
One key difference between this study and the POPAI study
is that, in addition to the total they planned to spend, we
also asked respondents to estimate the cost of the items they
planned to purchase (i.e., the itemized portion of their bud-
gets). The order of these two questions was counterbalanced.
This approach allows us to measure the respondents’ in-
store slack by subtracting the itemized portion from the total
planned spend amount. After the respondents checked out,
they reported how many aisles they had shopped, indicated
whether they had a grocery budget, answered demographic
questions, and responded to questions designed to measure
their impulsiveness. Finally, the interviewer made a copy
of the respondent’s receipt so that we had a record of the
items purchased, amount spent, and price of each item pur-
chased. Respondents also provided their frequent shopper
card number, which allows us to compare the amount spent
on the present trip to other similar trips made by each in-
dividual. These data serve as a benchmark to examine
whether our methodology influenced the amount spent.
Sample
Due to missing responses or missing receipts for 22 re-
spondents, the usable sample of respondents was 153, 84%
of whom were female. The average household size was 3.11
people. Table 2 summarizes the sample statistics. The mea-
sures used for each construct in our model are summarized
below, and table 3 reports the correlation matrix.
Measures
Trip Mental Budget (TBUDGET). Respondents were
asked to estimate how much they expected to spend on the
trip.
Itemized Portion (ITZ). After reporting the items that
they planned to purchase, respondents were asked to esti-
q4
q5
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IN-STORE SLACK IN BUDGET DEVIATION PROOF 7
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
TABLE 3
CORRELATION MATRIX
123456789
1. Budget deviation 1.00
2. In-store slack .27 1.00
3. Aisles shopped .18 .17 1.00
4. Impulsiveness .28 .07 .17 1.00
5. Trip length .05 .42 .28 .03 1.00
6. Income .03 .19 .04 .02 .02 1.00
7. Household size .05 .03 .06 .01 .13 .00 1.00
8. Trip budget .24 .73 .17 .01 .45 .18 .08 1.00
9. Have grocery budget .04 .09 .03 .01 .07 .19 .08 .01 1.00
N
OTE
.—N p 153; all correlations above .16 or below .16 are statistically significant at p ! .05.
mate how much they expected to spend on the list of planned
items.
In-Store Slack (ISS). This measure was calculated by
subtracting the itemized portion from the trip mental budget.
Household Size (HH). Respondents were asked to in-
dicate the number of people in their household.
Income (INC). During the exit interview, respondents
were asked to indicate their annual household income. To in-
crease the response to such a personally sensitive question,
respondents were provided with seven choices:
!$20,000;
$20,000–39,999; $40,000–59,999; $60,000-$79,999;
$80,000–99,999, $100,000–119,999, and $120,000+. Using
this approach, there was a 95% response rate for the income
question. A continuous income variable was then created
by taking the midpoint income for each of the categories.
The income variable equals $130,000 for the respondents,
who indicated that their annual income was above $120K
(a sensitivity analysis using $140K and $160K as the mid-
point yielded substantively identical results).
Aisles Shopped (MAISLES). Consistent with the mea-
sure employed by Inman et al. (2009), respondents were
asked to indicate whether they shopped “only those aisles
or sections where I planned to buy something,” “most aisles
or sections of the store,” or “each aisle or section of the
store.” We then collapsed shoppers who responded that they
“shopped each aisle” into the “most aisles shopped” cate-
gory due to the fact that fewer than 10% of the respondents
indicated that they shopped all aisles. The low percentage
of respondents shopping all aisles was most likely due to
the fact that this study was conducted at relatively large
grocery stores with aisles devoted to specialty categories,
such as automotive accessories, baby accessories, and photo
services. In our model, aisles shopped is measured using
effects coding such that the variable is equal to 1 if most
or all aisles were shopped and 1 if only needed aisles were
shopped.
Impulsiveness (IMP). This was measured using a five-
item, 7-point scale (a p .63) adapted from Puri (1996).
Specifically, we asked respondents how frequently the fol-
lowing adjectives typically describe them: impulsive, ex-
travagant, self-controlled, responsible, and restrained, on a
scale where 1 p seldom and 7 p usually. The last three
adjectives are reverse coded.
Trip Length (TLENGTH). We determined the trip start
time based on the time recorded at the end of the entry
interview. We determined trip end time based on the check-
out time provided on the receipt. Trip length isthe difference
between the start time and end time and is measured in
hours.
Have Grocery Budget (GBUD). This variable is coded
1 if respondents indicated that they have a grocery budget
and 1 otherwise. This question was asked during the exit
interview and was designed to assess whether the shopper
maintains an explicit grocery budget or whether the planned
spend represents a more implicit mental budget.
Trip Spend (SPEND). Using the respondent’s receipt,
we determined her actual total spend.
Model
To test our hypotheses, we estimate equation 1 below
using OLS regression. In addition to the variables indicated
by our hypotheses, we also include household size and trip
mental budget as covariates.
1
Further, we also include the
variable that indicates whether the shopper has a grocery
budget (GBUD). We include this variable to assess whether
budget deviation depends on whether the shopper maintains
an explicit grocery budget. All continuous variables, in-
cluding income, are mean centered so as to reduce multi-
1
Given that BUDDEV and ISS are both a function of TBUDGET, there
is the possibility of a spurious negative correlation between BUDDEV and
ISS. As suggested by Peter, Churchill, and Brown (1993), wealso estimated
an alternate model where the dependent variable is total spend and trip
mental budget appears on the right-hand side. The results are substantively
identical to the results for eq. 1. Therefore, we report the budget deviation
results for consistency with our hypotheses.
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PROOF 8 JOURNAL OF CONSUMER RESEARCH
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
TABLE 4
RESULTS FOR PROPOSED MODEL (EQUATION 1)
Parameter
estimate t-value VIF
Intercept 3.25* 1.96 0
In-store slack (ISS) .44
*** 4.24 3.73
Aisles shopped (MAISLES) 5.43
*** 3.56 1.23
Impulsiveness (IMP) 6.41
*** 4.01 1.22
Trip length (TLENGTH) 8.95
* 1.68 1.45
Income (INC) .07 1.44 1.17
Household size (HH) 1.01 1.02 1.13
Trip budget (TBUDGET) .03 .60 2.71
Grocery budget (GBUD) 2.10 1.33 1.12
ISS X MAISLES .57
*** 6.65 2.47
ISS X IMP .00 .04 2.46
IMP X MAISLES 3.77
** 2.35 1.20
ISS X MAISLES X IMP .22
** 2.51 2.14
ISS X TLENGTH .36
*** 3.44 3.08
ISS X INC .01
*** 3.15 2.22
Model
2
R .456
Model p-value
!.001
F-statistic F(14,138) p 8.27
N
OTE
.—DV p budget deviation; VIF p ______________________.
*p
! .10.
**p
! .05.
***p
! .01.
collinearity (Aiken and West 1991) and to facilitate inter-
pretation of main effects.
BUDDEV p b + b # ISS + b
i 01 i 2
#MAISLES + b # IMP + b # TLENGTH
i 3 i 4 i
+b # INC +b # ISS X MAISLES + b
5 i 6 ii7
#ISS X IMP + b # MAISLES X IMP (1)
ii8 ii
+b # ISS X MAISLES X IMP + b
9 iii10
#ISS X TLENGTH + b # ISS X INC + b
ii11 ii13
+b # HH # GBUD b # TBUDGET + .
12 ii14 i 1
Results
Control Analysis. To assess whether our survey meth-
odology influenced spending, we compare each shopper’s
spending on the survey trip to that shopper’s previous spend-
ing behavior. To facilitate relevantcomparisons, we compare
each shopper’s amount spent on the survey trip to the av-
erage trip of the same type (major vs. fill-in) over the 6
months preceding our survey. Following Kahn and Schmitt-
lein (1989, 1992), we characterize a trip as a major trip or
a fill-in trip based on each individual’s spending distribution.
Due to the fact that we screened for shoppers to be picking
up more than “a couple items,” we eliminated any com-
parison shopping trips with fewer than three items or a
basket size of less than $10.00. After removing six respon-
dents who had inadequate shopping records, we find no
significant difference between the amount spent on the sur-
vey day (M p $58.42) and the 6-month mean (M p $59.16;
t p .30, p
1 .10).
Descriptive Results. Although the POPAI analysis pro-
vided indirect support for the existence of in-store slack,
this study aims to provide more direct evidence. Therefore,
we first examine whether consumers’ mental budgets include
room for unplanned purchases.
2
The average mental budget
for the trip was $58.46. Of this amount, consumers expected
to spend an average of $41.11 on the items planned to the
brand or product level. Therefore, the average remaining
amount of $17.35 represents in-store slack. Hence, consum-
ers’ mental budgets contain ample room to make unplanned
purchases. Further, we find that the average amount spent
2
Our survey question asked respondents to estimate their spending ex-
pectations for the trip. To support our claim that this planned spend func-
tions as a mental budget, we leverage data from a related study that had
a similar procedure but also had respondents scan the order of their pur-
chases. Using this data, we estimate a hierarchical model (Raudenbush and
Bryk 2002) where the dependent variable is the probability of shopper j
making another purchase after purchase i. In contrast to what would be
expected if the planned spend functions as a “mere expectation,” we find
that a shopper is significantly less likely to make another purchase after
exceeding her planned spend even when controlling for total spending at
that point. Results are available from the authors upon request.
was $58.93, so the average budget deviation was only $0.47.
Over 75% of the shoppers surveyed reported a nonzero in-
store slack (115 of 153).
Base Model. A key contribution of this article is the
introduction of the in-store slack construct. In order to pro-
vide empirical support for the usefulness of this construct
in predicting budget deviation, we compare our proposed
model to a base model. Specifically, we compare our pro-
posed model to a model that includes all the variables spec-
ified in equation 1, excluding slack and the slack interac-
tions. An incremental F-test indicates that the proposed
model explains significantly more variance than the base
model (F(6, 138) p 9.34, p
! .01). This test indicates the
utility of in-store slack in predicting budget deviation.
Proposed Model. Having established that including the
construct of in-store slack explains significant additionalvar-
iance, we now present the results of our proposed model in
table 4. All VIFs are less than four, suggesting that multi-
collinearity is not a major concern (Stevens 2002). Before
examining the results for hypotheses 1–5, we first assess
whether having an explicit grocery budget influences budget
deviation. We find that having an explicit grocery budget
does not have an impact on budget deviation (ß
13
p 2.10,
p
1 .10), which suggests that individuals without an explicit
grocery budget come just as close to their spending expec-
tation as individuals with an explicit grocery budget. This
provides further support for our argument that a consumer’s
spending expectation functions as a mental budget. The co-
variates of household size and trip budget are also not sig-
nificant (p
1 .10).
Hypothesis 1 predicts that the relationship between in-
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IN-STORE SLACK IN BUDGET DEVIATION PROOF 9
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
FIGURE 2
INTERACTION BETWEEN SLACK, AISLES SHOPPED, AND IMPULSIVENESS
(PANEL A, ONLY NEEDED AISLES SHOPPED; PANEL B, MOST AISLES SHOPPED)
store slack and budget deviation increases as more aisles
are shopped. Interestingly, we find a significant, negative
relationship between in-store slack and budget deviation (ß
1
p 0.44, p ! .01). As predicted, we also find a main effect
of aisles shopped
2
p 5.43, p ! .01) and a significant,
positive interaction between slack and aisles shopped (ß
6
p
0.57, p
! .01), which supports hypothesis 1. Recall that
aisles shopped is coded using effects coding, while the re-
maining variables are mean centered. Therefore, the results
indicate that when only needed aisles are shopped, the av-
erage relationship between in-store slack and budget devi-
ation is 1.01 (0.44–0.57). That is, for every dollar in her
in-store slack, the average shopperunderspends her totalmen-
tal budget by approximately a dollar. On the other hand, the
average relationship between slack and budget deviation is
.13 (0.44 + 0.57) when most or all aisles are shopped. This
slope is not significantly different than zero, which suggests
that slack has no impact on budget deviation when most or
all aisles are shopped. The shopper spends the money in her
in-store slack but does not tend to exceed that amount.
Hypothesis 2 predicts that the two-way interaction be-
tween slack and aisles shopped will be further qualified by
impulsiveness. Consistent with prior research that finds that
impulsive individuals are more likely to make impulse pur-
chases (Rook and Fisher 1995; Vohs and Faber 2007), we
find a significant relationship betweenimpulsivenessand bud-
get deviation
3
p 6.41, p ! .01). We do not find a significant
interaction between in-store slack and impulsiveness
7
p
.004, p
1 .10) but do find a significant interaction between
impulsiveness and aisles shopped
8
p 3.77, p ! .05). More
important, we find a significant, positive three-way interaction
between in-store slack, aisles shopped, and impulsiveness (ß
9
p .22, p ! .05) in support of hypothesis 2.
To further explore this interaction, we follow the post hoc
probing procedure recommended by Aiken and West (1991).
Specifically, we first calculate high- and low-impulsiveness
levels by adding or subtracting the standard deviation from
the mean (M p 3.02; SD p 0.95). We then conduct simple
slope analysis, which examines the relationship between
slack and budget deviation at the four possible combinations
of impulsiveness (high vs. low) and aisles shopped (only
needed vs. most). The results are depicted in figure 2. When
only needed aisles are shopped, the relationship between
slack and budget deviation is 1.19 for highly impulsive
shoppers and 0.95 for low-impulsiveness shoppers. There
is not a significant difference between these slopes (p
1
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PROOF 10 JOURNAL OF CONSUMER RESEARCH
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
FIGURE 3
INTERACTION BETWEEN SLACK AND TRIP LENGTH
.10), which indicates that, regardless of the shopper’s im-
pulsiveness, slack leads to underspending when only needed
aisles are shopped.
As seen in figure 2B, the relationship between slack and
budget deviation is .35, which is significantly different than
zero (p
! .05) when most aisles are shopped and the in-
dividual is highly impulsive. When most aisles are shopped
by low impulsiveness individuals, however, there is no re-
lationship between slack and budget deviation p .04,
p
1 .10). In summary, when the highly impulsive individual
shops most aisles, then the slack creates a multiplier effect.
For each dollar of slack, the shopper spends $1.35 dollars.
This is consistent with a shopping momentum (Dhar et al.
2007) explanation, which argues that by allowing herself to
look for some unplanned items, the impulsive shopper be-
gins down a slippery slope. In contrast, our results suggest
that an individual who is low in impulsiveness is able to
exert enough self-control to refrain from making unplanned
purchases that exceed her total budget regardless of the
amount of slack or the increased purchase opportunity as-
sociated with shopping most aisles.
Hypothesis 3 predicts that trip length will be positively
related to budget deviation based on previous research that
indicates that depletion of cognitive or self-regulatory re-
sources increases the tendency to make impulsive decisions
(i.e., Shiv and Fedorikhin 1999; Vohs and Faber 2007).
While we find marginal support for this hypothesis
4
p
8.95, p
! .10), we also find that this result is qualified by
a significant, negative interaction between trip length and
slack
10
p .36, p ! .01), supporting hypothesis 4. This
suggests that slack attenuates the impact of trip length on
budget deviation. As before, we further explore this inter-
action using the approach advocated by Aiken and West
(1991). As shown in figure 3, we find that when slack is
low ($0), trip length is significantly related to budget de-
viation p 15.36, p
! .01). This indicates that every
additional 15 minutes spent in the store is associated with
an additional $3.84 (15.36 # .25 hours) in budget deviation.
On the other hand, there is no significant relationship be-
tween trip length and budget deviation when slack is high
($40; ß p 0.86, p
1 .10).
Hypothesis 5 predicts that the relationship between slack
and budget deviation increases with income. Surprisingly,
there is not a significant relationship between income and
budget deviation
5
p .07, p 1 .10),
3
but there is a positive
interaction between slack and income
11
p .01, p ! .01).
Recall that income is mean centered in our model, so the
relationship between slack and budget deviation is 0.44
for average-income individuals ( $46K). For high-income
individuals in our sample ( $75K), this rate increases to
approximately 0.15 (0.44 + 29 # .01). Although it is
somewhat surprising that the relationship between slack and
budget deviation is still negative for high-income individ-
uals, one needs to keep in mind the other additive effects
in our model—such as aisles shopped. For example, a high-
income individual who shops most aisles and has $40 in
slack is predicted to spend $8.53 over her mental budget
compared to a low-income individual in the same situation
who is predicted to spend $1.81 under her mental budget.
4
Free Response Analysis. So far, we have provided
evidence of in-store slack and demonstrated that in-store
slack predicts budget deviation depending on consumer and
3
One potential explanation is that since income and in-store slack are
weakly correlated (p p .19), income may have an indirect effect on budget
deviation via in-store slack. To investigate this issue, we first regressed in-
store slack on income and then used the resulting residuals as the measure
of in-store slack in eq. 1. These results mirror the results reported in table
4 and are therefore not discussed further.
4
For exploratory purposes, we investigated whether there was a three-
way interaction between slack, number of aisles shopped, and income. We
did not find a significant interaction.
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IN-STORE SLACK IN BUDGET DEVIATION PROOF 11
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
TABLE 5
REASONS FOR HAVING IN-STORE SLACK
No. respondents Percentage
Forgotten items: 24 38.1
See things you forgot 12 19.0
List was not complete 10 15.9
See things you need 2 3.2
Wants: 33 52.4
See extra items 19 30.2
Impulse Items 6 9.5
See things you want 5 7.9
Browse 3 4.8
Price oriented: 7 11.1
Take advantage of sales 4 6.3
Uncertain prices 3 4.8
Don’t know 3 4.8
Miscellaneous 3 4.8
Total of categories 63
N
OTE
.—Seven respondents gave two reasons.
trip characteristics, but we have not yet examined the reasons
that shoppers have in-store slack. We addressed this issue
by collecting open-ended responses from the last 65 re-
spondents who had in-store slack. Specifically, we asked
them the following question at the conclusion of the exit
interview: “Before you began shopping, you told us that
you expected to spend more than the cost of the items that
you were planning on buying. Please explain why.” Re-
sponses from two respondents were eliminated because they
misunderstood the question. Two research assistants, who
were blind to the hypotheses, coded the responses from the
remaining 63 respondents. Interrater reliability was 0.94,
with disagreements resolved by discussion. Table 5 provides
a summary of responses to the open-ended question.
Earlier we argued that consumer’s have in-store slack for
at least two reasons. First, the routine nature of grocery
shopping means that shoppers are aware that in-store stimuli
(i.e., merchandise, displays, signage, etc.) will trigger for-
gotten needs (Bettman 1979; Lynch and Srull1982). Beyond
forgotten needs, shoppers also have experience that they get
new ideas while in-store (Iyer 1989; Stern 1962) or that they
may make impulse purchases. Therefore, seven of the re-
sponse categories were combined into two major categories:
“forgotten needs” and “unplanned wants.” Ninety percent
of respondents indicated a response that fit into one of these
categories, with 38.1% indicating that the in-store slack was
for “forgotten needs” and 52.4% indicating that it was for
an “unplanned want.” For example, one respondent from
the unplanned want category indicated that she had in-store
slack because “you see other things you want, like the candy
aisle and cookie aisle.” Interestingly, some respondents ac-
tually used the term “impulse,” despite the negative con-
notations typically associated with this term. While the ex-
planation that the in-store slack was for “extra items” does
not allow us to ascertain the degree to which individuals
ultimately consider their purchase motives, clearly the in-
store slack accounts for more than an inability to retrieve
all needed items. Over half the respondents indicated that the
in-store slack was available for any extra items that they saw
while walking around the store, including impulse items.
Weighted Analysis. The explicitness of mental budgets
tends to vary across individuals (Thaler 1985), so it may
be important to consider that individuals vary in the degree
of certainty regarding their mental budgets for the trip. Ad-
ditionally, shoppers may differ in the ability to accurately
estimate costs of planned items. To rule out the possibility
that either budget uncertainty or estimation error drove our
results, we conducted two weighted least squares (WLS) anal-
yses.
In our first WLS analysis, we conducted weighted least
squares analyses where the weight represents a shopper’s
budget certainty. Although one approach would be to have
the respondents directly estimate how certain they were
about their trip budgets, individuals tend to have difficulty
calibrating confidence judgments (Fischer, Luce, and Jia
2000; Lichenstein, Fischoff, and Phillips 1982). Therefore,
we instead estimate each respondent’s mental budget un-
certainty using variability in trip size based on the frequent
shopper data from the 6 months preceding the survey. Spe-
cifically, we calculate the budget uncertainty to be the co-
efficient of variation of trips that match the individual’s trip
type (major vs. fill-in) on the day of the survey. As indicated
in equation 2, we then subtract the coefficient of variation
from one so as to place greater weight on those individuals
with greater budget certainty:
j
i
W p 1 . (2)
i
m
i
In equation 2, W
i
is the weight placed on household i,and
j
i
and m
i
are the standard deviation and mean of household
i’s spending for the previous 6 months, respectively. The
results of this weighted analysis closely mirror the un-
weighted results presented in table 4 with one exception. In
the weighted analysis, the main effect of income becomes
significant (b p .11, p ! .05). It appears that under con-
ditions of greater budget certainty, higher-income individ-
uals are generally more likely to exceed budgets, consistent
with our earlier discussion that budgets are less constraining
for high-income households.
In our second WLS analysis, the weight represents a shop-
per’s accuracy in estimating the cost of her itemized budget.
More specifically, we calculate the weight as specified in
equation 3 with SPEND_P
i
representing the amount spent
on planned items by shopper i and ITZ
i
representing the
size of the itemized portion of the mental budget for shopper
i. Using this weight, the WLS places greater weight (W
i
)
on individuals with greater estimation accuracy:
SPEND PITZ
ii
W p 1 . (3)
i
()
FF
ITZ
i
The results of the weighted analysis indicate support for our
hypotheses that is generally consistent with the results re-
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PROOF 12 JOURNAL OF CONSUMER RESEARCH
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
ported in table 4. One exception is that the relationship
between trip length and budget deviation (hypothesis 3)
shifts from being marginally significant (p
! .10) to not
significant (p
1 .10) in the weighted model. Importantly,
hypothesis 4 (which posited an interaction between trip
length and slack) continues to receive support. An additional
difference between the weighted and unweighted results is
that the effect of having an explicit grocery budget shifts
from being not significant to marginally significant (p !
.10). While it is not surprising that those who are high in
estimation accuracy and have explicit grocery budgets
would spend less than those without explicit grocery bud-
gets, the effect appears to be somewhat weak. Interestingly,
spending expectations function similarly regardless of
whether there is an explicit budget or not. More important,
the overall consistency of the weighted results mitigates
concerns that forecasting or estimation errors are driving our
results.
DISCUSSION
Using an existing study (POPAI) and a field study, we
provide evidence for our thesis that consumers have in-store
slack. That is, consumers’ spending expectations for the
shopping trip include room for unplanned purchases. In the
POPAI data, we infer the existence of the in-store slack
based on the negative relationship between number of
planned purchases and number of unplanned purchases
when accounting for the trip budget. A benefit of this ap-
proach is that the notion of in-store slack is not made salient
to the consumer. Additionally, the POPAI study was con-
ducted in 14 different cities, evincing the robustness of this
phenomenon. In our field study,we use more direct measures
of the itemized portion and in-store slack by asking re-
spondents to estimate both the total amount that they plan
to spend and the expected cost of the planned items. This
approach improves the face validity of our measures and
also indicates that shoppers realize that they plan to spend
more than the expected cost of the planned items.
Further, both the POPAI study and the new field study
show that the amount shoppers actually spend on grocery
trips is surprisingly close to their spending expectations for
the trip. Additionally, we show that, on average, this lack
of a difference between actual spend and planned spend does
not depend on whether the shopper has an explicit grocery
budget—shoppers without explicit grocery budgets come
just as close to their spending expectations for the trip.
Jointly, these findings provide strong support for our ar-
gument that spending expectations function as an implicit
mental budget for the trip. Using real field data, this article
improves the external validity of mental budgeting theory
typically studied using hypothetical lab studies (e.g., Chema
and Soman 2006; Heath and Soll 1996). These findings also
contribute more broadly to the existing literature on mental
accounting. While much attention has been paid to item-
level spending expectations (i.e., reference prices), our find-
ings suggest that more aggregate-level spending expecta-
tions are also strongly predictive of behavior.
Our field study provides evidence that the amount of in-
store slack influences an individual’s tendency to overspend
or underspend relative to one’s total mental budget. There-
fore, we also contribute to the mental budgeting literature
by showing that the nature of the mental budget is related
to budget deviation. While the mental budgeting literature
argues that consumers will generally overconsume or under-
consume in an effort to stick to their mental budgets (i.e.,
Heath and Soll 1996), we show that the nature of the trip
budget (i.e., amount of in-store slack) influences budget de-
viation even when controlling for the trip budget. By then
examining how the impact of slack on budget deviation
varies depending on consumer (impulsiveness and income)
and trip characteristics (aisles shopped and trip length), we
contribute to the shopper marketing literature as well as to
the self-control literature.
First, we find that when only needed aisles were shopped,
the average consumer had a negative relationship between
in-store slack and budget deviation. This indicates that when
consumers shop only needed aisles, there is money that
consumers are mentally prepared to spend on the shopping
trip that they are not ultimately spending. At first glance,
underspending appears to be advantageous for the consumer,
but this result could also suggest negative consequences for
the consumer. The free response analysis data suggest that
one major reason for having in-store slack is to have funds
available to purchase forgotten items. Therefore, individuals
who shop only those aisles where they realize they need
something may in fact be forgetting to purchase needed
items. Further research should explore whether failure to
spend the in-store slack contributes to the consumer making
an additional fill-in trip.
In contrast, there is not a significant relationship between
in-store slack and budget deviation when most aisles are
shopped. The positive interaction between in-store slack and
aisles shopped provides support for the idea that proximity
to items increases desire (i.e., Faber and Vohs 2004; Hoch
and Loewenstein 1991; Mischel and Grusec 1967) using
field data. This interaction is further qualified by impul-
siveness, identifying the only condition in which in-store
slack is positively related to budget deviation. Specifically,
we find that in-store slack is positively related to budget
deviation when a highly impulsive individual shops most
aisles. In all other cases, we find either no relationship or
a negative relationship between in-store slack and budget
deviation. This suggests that although most consumers are
able to exert sufficientself-control to stay within their mental
budgets, impulsive individuals have difficulty doing so. For
the majority of consumers, having in-store slack appears to
be a rational way to use the store to cue needs and preserve
self-control.
While most consumers have small budget deviations, hav-
ing in-store slack may also create a self-fulfilling prophecy
where consumers buy unplanned items that they do not re-
ally need. Even if people subconsciously intend to use their
slack for “forgotten needs,” many consumers are susceptible
to temporary visceral urges, such as hunger, that may result
q15
IN-STORE SLACK IN BUDGET DEVIATION PROOF 13
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
in behaviors that are inconsistent with self-interests (Loew-
enstein 1996). As a result, they may ultimately spend in-
store slack on unneeded or unhealthy items. If in-store slack
leads to the purchase of more unhealthy items, this would
suggest that individuals trying to restrict their eating should
consider making the effort to fully plan every item that they
intend to purchase before going to the grocery store. Con-
versely, mental budget constraints could prevent consumers
from taking advantage of specials, such as volume discounts,
that would result in savings over the long term.
For retailers, this research suggests that consumers who
shop only needed aisles are not spending money that they
are mentally prepared to spend on the current trip. In ad-
dition to highlighting the importance of encouraging con-
sumers to shop more aisles, this article also affirms practices
that retailers employ to encourage consumers to spend all
of their mental budgets, such as to offer samples (increase
desire) or reminder placards as they approach the checkout
lines (cue forgotten needs). On the other hand, our mental
budgeting perspective suggests that brands may be vying
for a fixed amount of money that consumers have allocated
to be spent on unplanned purchases. The fact that most
consumers do not exceed their mental budgets despite mak-
ing unplanned purchases suggests that different product cat-
egories function as substitutes (i.e., should I spend my in-
store slack on ice cream or Parmesan cheese?). Therefore,
future research should further examine whether in-store
stimuli may simply serve to redirect what items consumers
purchase rather than generate incremental spending. This
would suggest that while manufacturers like P&G can ben-
efit from in-store initiatives such as First Moment of Truth
(Nelson and Ellison 2005) by attracting consumers to their
specific product, grocery retailers need to carefully evaluate
whether in-store stimuli are actually generating incremental
sales at the store level.
While our findings offer novel insights into shoppers’
unplanned purchasing behavior, in-store slack is still a nas-
cent construct, and there remains a lot to learn. As with any
consumer process, there is likely to be heterogeneity in shop-
pers’ in-store slack behavior. First, shoppers may have vary-
ing degrees of certainty about their trip budget. While our
WLS analysis shows that our results are not driven by those
individuals who have high-budget uncertainty, future re-
search should further investigate how shopping behavior
varies with budget certainty. Second, our analyses show that
the amount of slack does influence budget deviation and
that these results are not driven by estimation error, but
future research is needed to increase our understanding of
preshopping processes and their impact on budget deviation.
While our field study offers external validity, there are
inherent limitations due to the survey nature of our data.
All of the variables were measured instead of manipulated,
so it is a possibility that there are other unmeasured deter-
minants of budget deviation that are correlated with slack
or the other independent variables. However, the use of two
data sets (from the POPAI study and our new field study)
and the robustness checks reported throughout the article
increase confidence in the findings. Further, one might argue
that the number of aisles shopped could be influenced by
the amount of one’s self-regulation, but we largely control
for this relationship by the inclusion of impulsiveness in our
model. Another weakness of our measure of aisles shopped
is that it is a self-report. The emerging technology of radio
frequency identification (RFID) that was recently employed
by Hui, Bradlow, and Fader (2009) offers significant op-
portunity for increased granularity and clarity. While our
results for trip length are consistent with our self-control
explanation, it is possible that there are several mechanisms
at work, such as differences across consumers in the amount
of in-store information processing that occurred. To provide
more direct evidence that shoppers with low slack become
depleted of self-control as the trip progresses, future research
could intercept different shoppers at varying times during
their trip and offer them a choice that would assess their
amount of self control.
Future research should also explore how the amount and
impact of in-store slack varies depending on the product
category and retail format. On the one hand, the forward-
looking anticipation of unplanned purchases may be specific
to grocery shopping due to its uniquely routine nature. On
the other hand, it is easy to see how a consumer who is
shopping for back-to-school clothes would have a mental
budget for the trip that includes both an itemized portion
and in-store slack. For example, a shopper with a mental
budget of $150 for the shopping trip may plan to spend
$100 on a new pair of jeans and a new pair of sneakers
(itemized portion) but also intend to wait until she is in the
store to decide what else to purchase with the remaining
$50 (in-store slack). This suggests that items that are typi-
cally perceived to be complements (e.g., shirt and skirt)
could function as substitutes that are competing for a fixed
amount of in-store slack. While this article offers initial
insight into the role that in-store slack plays in grocery
shopping behavior, there is clearly significant opportunity
to further explore the occurrence and impact of in-store slack
in this and other domains.
REFERENCES
Aiken, Leona S. and Stephen G. West (1991), Multiple Regression:
Testing and Interpreting Interactions, Newbury Park, CA:
Sage.
Ainslie, Andrew and Peter E. Rossi (1998), “Similarities in Choice
Behavior across Categories,” Marketing Science,17(2),
91–106.
Babin, Barry, William R. Darden, and Mitch Griffin (1994), “Work
and/or Fun: Measuring Hedonic and Utilitarian Shopping
Value,” Journal of Consumer Research, 20 (March), 644–56.
Baumeister, Roy F., Todd F. Heatherton, and Dianne M. Tice
(1994), Losing Control: How and Why People Fail at Self-
Regulation, San Diego, CA: Academic Press.
Baumeister, Roy F., Ellen Bratslavsky, Mark Muraven, and Dianne
M. Tice (1998), “Ego Depletion—Is the Active Self a Limited
Resource? Journal of Personality and Social Psychology, 74,
1252–65.
PROOF 14 JOURNAL OF CONSUMER RESEARCH
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
Bettman, James R. (1979), “Memory Factors in Consumer Choice:
A Review, Journal of Marketing, 43 (Spring), 37–53.
Cohen, Sheldon, Gary W. Evans, David S. Krantz, and Daniel
Stokols (1980), “Physiological, Motivation and Cognitive Ef-
fects of Aircraft Noise on Children,” American Psychologist,
35, 231–43.
Cyert, Richard M. and James G. March (1963), A Behavioral
Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.
Dhar, Ravi, Joel Huber, and Uzma Khan (2007), “The Shopping
Momentum Effect,” Journal of Marketing Research,44(No-
vember), 370–78.
Evans, Gary W. (1979), “Behavioral and Physiological Conse-
quences of Crowding in Humans, Journal of Applied Social
Psychology, 9 (1), 27–46.
Faber, Ronald J. and Kathleen D. Vohs (2004), “To Buy or Not
to Buy? Self-Control and Self- Regulatory Failure in Purchase
Behavior,” in Handbook of Self-Regulation: Research, Theory
and Applications, ed. Roy F. Baumeister and Kathleen D.
Vohs, New York: Guilford, 509–24.
Fischer, Gregory W., Mary Frances Luce, and Jianmin Jia (2000),
“Attribute Conflict and Preference Uncertainty: Effects on
Judgment Time and Error,” Management Science,46(1),
88–103.
Glass, David C., Jerome E. Singer, and Lucy N. Friedman (1969),
“Psychic Cost of Adaptation to an Environmental Stressor,”
Journal of Personality and Social Psychology, 12 (July),
200–210.
Grewal, Dhruv, Kent B. Monroe, and R. Krishnan (1998), “The
Effects of Price Comparison Advertising on Buyers’ Percep-
tions of Acquisition Value, Transaction Value, and Behavioral
Intentions,” Journal of Marketing,62(2),4659.
Hartley, L. R. (1973), “Effect of Prior Noise or Prior Performance
on Serial Reaction,” Journal of Experimental Psychology, 101
(2), 255–61.
Hauser, John R. and Glen L. Urban (1986), “The Value Priority
Hypotheses for Consumer Budget Plans,” Journal of Con-
sumer Research, 12 (March), 446–62.
Heath, Chip and Jack B. Soll (1996), “Mental Budgeting and Con-
sumer Decisions,” Journal of Consumer Research, 23 (June),
40–52.
Heilman, M. Carrie, Kent Nakamoto, and Ambar G. Rao (2002),
“Pleasant Surprises: Consumer Response to Unexpected In-
Store Coupons,” Journal of Marketing Research, 34 (May),
242–52.
Hoch, Stephen J. and George F. Loewenstein (1991), “Time In-
Consistent Preferences and Consumer Self-Control,” Journal
of Consumer Research, 17 (4), 492–507.
Hui, Sam K., Eric T. Bradlow, and Peter S. Fader (2009), “Testing
Behavioral Hypotheses Using an Integrated Model ofGrocery
Store Shopping Path and Purchase Behavior,” Journal of Con-
sumer Research, 36 (October), 478–93.
Inman, J. Jeffrey, Russell S. Winer, and Rosellina Ferraro (2009),
“The Interplay between Category Characteristics, Customer
Characteristics, and Customer Activities on In-Store Decision
Making,” Journal of Marketing, 73 (September), 19–29.
Iyer, Easwar S. (1989), “Unplanned Purchasing: Knowledge of
Shopping Environment and Time Pressure,” Journal of Re-
tailing, 65 (Spring), 40–57.
Kahn, Barbara E. and Schmittlein, David C. (1989), “Shopping
Trip Behavior: An Empirical Investigation,” Marketing Let-
ters, 1 (December), 55–69.
——— (1992), “The Relationship between Purchases Made on
Promotion and Shopping Trip Behavior,” Journal of Retailing,
68 (Fall), 294–315.
Kahneman, Daniel and Amos Tversky (1979), “Prospect Theory:
Analysis of Decision under Risk,” Econometrica, 47 (March),
263–92.
Kalyanaram, Gurumurthy and Russell S. Winer (1995), “Empirical
Generalizations from Reference Price Research,” Marketing
Science, 13 (3), 161–69.
Khan, Uzma and Ravi Dhar (2007), “Where There Is a Will, Is
There a Way? The Effect of Future Choices on Self-Control,”
Journal of Experimental Psychology, 136 (2), 277–88.
Kollat, David T. and Ronald P. Willett (1967), “Customer Impulse
Purchasing Behavior,” Journal of Marketing Research,4,
21–31.
Laibson, David (2001), “A Cue-Theory of Consumption,” Quar-
terly Journal of Economics, 116 (February), 81–119.
Lee, Leonard and Dan Ariely (2006), “Shopping Goals, Goal Con-
creteness and Conditional Promotions,” Journal of Consumer
Research, 33 (June), 60–70.
Lichtenstein, Donald R., Richard G. Netemeyer, and Scot Burton
(1990), “Distinguishing Coupon Proneness from Value Con-
sciousness: An Acquisition-Transaction Utility Theory Per-
spective,” Journal of Marketing, 54 (July), 54–67.
Loewenstein, George (1996), “Out of Control: Visceral Influences
on Behavior,” Organizational Behavior and Human Decision
Processes, 65 (3), 272–92.
Lynch, John G., Jr. and Thomas K. Srull (1982), “Memory and
Attentional Factors in Consumer Choice: Concepts and Re-
search Methods,” Journal of Consumer Research, 9 (June),
18–36.
Miller, George A. (1956), “The Magical Number Seven, Plus or
Minus Two: Some Limits on Our Capacity for Processing
Information,” Psychological Review, 63, 81–97.
Mischel, Walter and Joan Grusec (1967), “Waiting for Rewards
and Punishments: Effects of Time and Probabilityon Choice,”
Journal of Personality and Social Psychology,5(1),2431.
Mukopadhyay, Anirban and Gita Venkatramani Johar (2007),
“Tempted or Not? The Effect of Recent Purchase History on
Responses to Affective Advertising,” Journal of Consumer
Research, 33 (March), 445–53.
Mulhern, Francis J., Jerome D. Williams, and Robert P. Leone
(1998), “Variability of Brand Price Elasticities: Ethnic, In-
come and Brand Determinants,” Journal of Retailing,73(3),
427–46.
Muraven, Mark and Roy F. Baumeister (2000), “Self-Regulation
and Depletion of Limited Resources: Does Self-Control Re-
semble a Muscle?” Psychological Bulletin, 126 (2), 247–59.
Muraven, Mark, Ellen M. Tice, and Roy F. Baumeister (1998),
“Self-Control as a Limited Resource: Regulatory Depletion
Patterns,” Journal of Personality and Social Psychology,74
(3), 774–89.
Nelson, Emily and Sarah Ellison (2005), “Shelf Promotion: In a
Shift, Marketers Beef Up Ad Spending Inside Stores,” Wall
Street Journal, September 21, A1.
Novemsky,Nathan and Daniel Kahneman (2005), “TheBoundaries
of Loss Aversion,” Journal of Marketing Research, 42 (May),
119–28.
Park, C. Whan, Easwar S. Iyer, and Daniel C. Smith (1989), “The
Effects of Situational Factors on In-Store Grocery Shopping
Behavior: The Role of Store Environment and Time Available
for Shopping,” Journal of Consumer Research, 15 (4), 422–33.
Peter, J. Paul, Gilbert A. Churchill Jr., and Tom J. Brown (1993),
q16
q17
q18
IN-STORE SLACK IN BUDGET DEVIATION PROOF 15
Monday Feb 08 2010 10:51 AM jcr, v37n2, 370211, PWAYLAND
“Caution in the Use of Difference Scores in Consumer Re-
search,” Journal of Consumer Research, 19 (March), 655–62.
(POPAI) Point of Purchase Advertising Institute (1995), The 1995
POPAI Buying ConsumerHabits Study,Englewood, NJ: Point
of Purchase Advertising Institute.
Puri, Radhika (1996), “Measuring and Modifying Consumer Im-
pulsiveness,” Journal of Consumer Psychology, 5 (2), 87–113.
Progressive Grocer (2007), “Nielsen to Expand In-StoreMarketing
Initiative,” May 8.
Raudenbush, Stephen W. and Anthony S. Bryk (2002), Hierar-
chical Linear Models: Applications and Data Analysis Meth-
ods. 2nd ed. Newbury Park, CA: Sage.
Rook, Dennis W. and Robert J. Fisher (1995), “Normative Influ-
ences on Impulse Buying Behavior,” Journal of Consumer
Research, 22 (December), 305–13.
Sherrod, Drury R. (1974), “Crowding, Perceived Control and Be-
havioral After-Effects,”Journal of Applied SocialPsychology,
4, 171–86.
Shiv, Baba and Sasha Fedorikhin (1999), “Heart and Mind in Con-
flict: The Interplay of Affect and Cognition in Consumer De-
cision Making,” Journal of Consumer Research, 26 (Decem-
ber), 278–92.
Simonson, Itamar (1990), “The Effect of Purchase Quantity and
Timing on Variety Seeking Behavior,” Journal of Marketing
Research, 27 (May), 150–62.
Stern, Hawkins (1962), “The Significance of Impulse Buying To-
day,” Journal of Marketing (April), 59–62.
Stevens, James (2002), Applied Multivariate Statistics for the So-
cial Sciences. Mahwah, NJ: Erlbaum.
Thaler, Richard (1985), “Mental Accounting and Consumer
Choice,” Marketing Science, 4 (3), 199–214.
——— (1999), “Mental Accounting Matters,” Journal of Behav-
ioral Decision Making, 12, 183–206.
Thaler, Richard and H. M. Shefrin (1981), “An Economic Theory
of Self-Control,” Journal of Political Economy, 89 (April),
392–406.
Tulving, Endel and Joseph Psotka (1971), “Retroactive Inhibition
in Free Recall: Inaccessibility of Information Available in
the Memory Store,” Journal of Experimental Psychology,
87 (January), 1–8.
Tversky, Amos and Daniel Kahneman (1991), “Loss Aversion in
Riskless Choice: A Reference-Dependent Model,” Quarterly
Journal of Economics, 106 (November), 1039–61.
Vohs, Kathleen D. and Ronald J. Faber (2007), “Spent Resources:
Self-Regulatory Resource Availability Affects Impulsive Buy-
ing,” Journal of Consumer Research, 33 (March), 537–47.
Vohs, Kathleen D. and Todd F. Heatherton(2000),“Self-Regulation
Failure: A Resource-Depletion Approach,” Psychological Sci-
ence, 11 (May), 249–54.
Wakefield, Kirk L. and J. Jeffrey Inman (2003), “Situational Price
Sensitivity: The Role of Consumption Occasion, Social Con-
text and Income,” Journal of Retailing, 79, 199–212.
Wegner, Daniel M. (1989), White Bears and Other Unwanted
Thoughts: Suppression, Obsession, and the Psychology of
Mental Control, New York: Viking/Penguin.
Wertenbroch, Klaus (1998), “Consumption Self-Control by Ra-
tioning Purchase Quantities of Virtue and Vice,” Marketing
Science, 17 (4), 317–37.
Wildawsky, Aaron (1964), The Politics of the Budgetary Process,
Boston: Little, Brown.
Winer, Russell S. (1986), “A Reference Price Model of Brand
Choice for Frequently Purchased Products,” Journal of Con-
sumer Research, 13 (September), 250–56.
Zauberman, Gal (2003), “The Intertemporal Dynamics of Con-
sumer Lock-In,” Journal of Consumer Research, 30 (Decem-
ber), 405–19.
Zeithaml, Valarie A. (1985), “The New Demographics and Market
Fragmentation,” Journal of Marketing, 49 (Summer), 64–75.
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QUERIES TO THE AUTHOR
1 Au: Journal style omits most italics used for
emphasis.
2 Au: Please provide the page number for Novemsky
and Kahneman’s direct quotation.
3 Au: Please provide a citation for Shefrin and Thaler
1988 in the reference list or omit mention of it here.
4 Au: Is the correct date for Muraven and Baumeister
2002, as shown here, or 2000, as in the reference list?
5 Au: I’ve added column headings to table 2. Please
revise as needed.
6 Au: In table 3, the original table showed a single
quotation mark before “‘1.00” for the number in column
3, row 3. I’ve omitted it, but please let me know if it
should be put back in—-and provide an explanation of
what it signifies for the table note.
7 Au: Should “$140K and $160K” be shown as a
range, as in “$140K–$160K”?
8 Au: In the equation (and table 4), do the X’s shown
(as in ISS X MAISLES) indicate multiplication or some-
thing else? If they are intended to be multiplication sym-
bols, an # will be used throughout. And, if # is correct,
do the phrases need to be placed in parentheses, such as
# (ISS
i
# INC
i
)+b
13
”? Please advise.
9 Au: In equation 1, should there be an operation sign
after GBUDi and before b14 in the last two lines? Note
also that journal style is to use a multicross (x) rather
than an asterisk (
*
) to indicate multiplication. Please
check the equation carefully and revise as needed.
10 Au: Please write out VIF fully.
11 Au: In figure 2, how should the superscript aster-
isks (
***
) be defined in the caption? Please provide the
information needed.
12 Au: In figure 3, how should the superscript aster-
isks (
***
) be defined in the caption? Please provide the
information needed.
13 Au: There is no citation for Lichenstein, Fischoff,
and Phillips 1982 in the reference list. Please either pro-
vide one or omit mention of it in the text.
14 Au: Is the lowercase “b” correct, or should it be
beta (b), as shown in other places in the text?
15 Au: There is no citation for Chema and Soman
2006 in the reference list. Please either provide one or
omit mention of it in the text.
16 Au: For the Laibson 2001 entry, the vol. number
and page range were verified via Web search.
17 Au: Please indicate where Lee and Ariely 2006
should be mentioned in the text or omit it from the refer-
ence list.
18 Au: Please indicate where Lichtenstein, Netemeyer,
and Burton 1990 should be mentioned in the text or omit
it from the reference list.
19 Au: Note that the Puri date has been changed to
1996, rather than 2006, for this title (verified by a Web
search).
20 Au: Indicate where Progressive Grocer 2007 should
be mentioned in the text or omit it from the reference list.
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