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Customer Impulse Purchasing BehaviorAuthor(s): David T. Kollat and Ronald P. WillettSource: Journal of Marketing Research, Vol. 4, No. 1 (Feb., 1967), pp. 21-31Published by: American Marketing AssociationStable URL: http://www.jstor.org/stable/3150160.
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8/10/2019 Customer Impulse Buying Behaviour.pdf
2/12
DAVIDT.
KOLLATnd
RONALDP.
WILLETT*
In
past
studies
of
impulse
buying,
the
customer
usually
was
ignored.
This
article
attempts
to
explain
customer
differences n
unplanned
purchasing
behavior.
Thus
serious
questions
are
raised
about
the
meaning
and
significance
of
impulse
buying.
us tomer
I m p u l s e
Purchas ing
e h a v i o r
Impulse purchasing
is not
confined to
any
type
of
marketing
institution,
but it
probably
most
frequently
refers to food purchasing decisions. Many studies have
used
impulse purchasing
to
view a
segment
of
consumer
behavior. Studies
by
du Pont
[8]
have
measured
the
incidence of
impulse purchasing
and have shown
how
different
kinds
of
products
are affected
by
it.
Other stud-
ies
have
investigated
how
type
of
store
[7,
20],
shelf
location
[16],
shelf
space
[9],
and
display
location
[14]
affect
impulse purchasing.
Others
[5,
15] purport
to
have
identified
and measured various
reasons for
im-
pulse purchasing,
while
another
[19]
has
hypothesized
circumstances that
appear
to be
associated
with
the
occurrence of the
behavior.
Customers
make
impulse purchases,
and it is sur-
prising
that most studies
did not have the
shopper
as
an
independent variable.' Does impulse purchasing truly
represent
an
impulsive
choice
by
the
shopper,
or
is the
purchase
merely
unplanned.
Does
unplanned
purchasing
occur with
equal
frequency among
all
customers,
or
are
certain
shoppers
more
likely
to make
unplanned
pur-
chases? What kinds
of customers
are most
susceptible
to
unplanned
purchasing?
The
objectives
of
the
present
study
were:
(a)
to deter-
mine
the
degree
to
which customers differ
in
their
sus-
ceptibility
to
unplanned
purchasing;
(b)
to discover
what
customer
characteristics
are associated
with
differential
susceptibility
to
unplanned
purchasing;
and
(c)
to iden-
tify
some of the
precipitating
conditions that lead to
an
unplanned purchase.
METHODOLOGY
Conceptualization
f
Unplanned
Purchasing
An
unplanned purchase
results from
a
comparison
of
alternative
purchase
intentions with actual
outcomes.
Accordingly,
an
intentions
typology,
an outcomes
typol-
ogy
and the
categorization
that results from
a
pairing
of
the
typologies
were used
to structure
the
research.
The intentions
typology
consists
of
the
major
stages
of
planning
that
presumably
exist
before
the
customer
is exposed to instore stimuli.2The major intentions are:
1. Product and
brand-Before
entering
the
store
the
shopper
knows
both
the
product
and
brand of
product
to be
purchased.
2. Product
only-Before
entering
the
store
the
shopper
knows
which
product
she
wants,
but has not
decided
on the
brand,
e.g.,
a
plan
to
buy
potato
chips
but
not
a
particular
brand.
3. Product
class
only-Before
entering
the
store
the
shopper
knows
the class of
product
that she
intends
to
purchase,
but has not decided
on the
products
n
that
class;
e.g.,
intention
to
buy
meat but
must
decide on
steakor
hamburger.
4.
Need
recognized-Before
entering
the
store
the
shopper
recognizes
he existence of
a
problem
or
need,
but has not decided which
product
class,
product
or brand
that
she
intends
to
purchase,
e.g.,
a need
for
something
or
dinner.
5. Need not
recognized-Before
entering
the
store
the
shopper
does not
recognize
the
existence of
a
need,
or
the need
is latent
until
she is in
the
store and
has
been
exposed
to its stimuli.
The
basis of
the
intentions
typology
is
to
specify
the
customer's
planning
prior
to
going
to
a
supermarket.
Or,
the
various
stages
indicate the
kind
and
extent of
in-store
decision
making.
The
outcomes
typology
consists
of
the
major
kinds
*
DavidT. Kollat s assistant rofessor f business rganiza-
tion,
the Ohio State
University.
Ronald
P.
Willett
s
associate
professor
of
marketing,
Graduate
School
of
Business,
ndiana
University.
1
There
are
isolated
exceptions
to the
tendency
not
to in-
vestigate
differential
customer
susceptibility
to
unplanned
pur-
chasing.
For these
exceptions
see
[5, 11, 12,
15,
17].
2
Major
refers
to the
presence
or
absence
of a
product
or
brand decision
prior
to
entering
the store.
A more
sophisticated
typology
would be N
dimensional
to
reflect
pre-shopping
de-
cisions
concerning
the
amount
to
be
purchased,
the
size
and
kind of
package
or
container to be
purchased,
etc.
Journal
of
Marketing
Research,
Vol. IV
(February
1967),
21-31
21
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22
JOURNAL
F
MARKETING
ESEARCH,
EBRUARY
967
Figure
1
AN
OPERATIONAL
NTENTIONS-OUTCOMES
ATRIX
Outcomes
Product
Intentions Product
N o
purchased;
and brand purchase
Brand not
purchased purchase Brandnot
purchased
Product and brand
men-
1
2 3
tioned
Product
only
mentioned
4
5
Product class mentioned
6 7
Need
recognized
8
Need
not
recognized
9
of behavior
that could
result
from
shopping;
he
out-
comes are:3
1.
product
and
brand
purchased;
2.
product
and
brand not
purchased,
.e.,
no
pur-
chase;
3. product purchased, brand not purchased, i.e.,
brand
substitution;
Conceptually
here
are
15
categories
hat resultfrom
the
pairing
of
the
above
ntentions
and outcomes.
Fortu-
nately,
this
categorization
an
be
compressed
ince
sev-
eral
categories
are
not
empirically
dentifiable.
When the
conceptual
ntentions-outcomes
matrix is
modified o reflect the
operational
requirements
f
the
study,
the
resulting
matrix can
be
collapsed
into
nine
categories, (Figure
1).
Using
this
intentions-outcomes
matrix,
Category
9
becomes
the definition
of
unplanned
purchasing.
Research
Design
Themethodologyn this studyrepresents modifica-
tion
and
expansion
of
the
du Pont
[8]
and
West
[20]
approaches.
The
research
plan
consistedof two
phases:
(a)
store
interviewing,
nd
(b)
home
interviewing.
The
present nvestigation
s
a
field
study
rather han
a
survey
[13].
Therefore,
t
is more concernedwith a
comprehensive
account of
the
investigated
processes
than
with their
typicality
n
a
larger
universe.
Since
asking
respondents
o
itemize
purchase
nten-
tions
might
affect
subsequent
shopping
behavior,
a
Pretest-Postest,
eparate
Sample
Postest
Only
Control
Group design
was
used
[3]. Sampling
ractions
were
used to
identify
those
shoppingparties
eligible
for the
study
and to
assign
the
eligible shopping parties
to
an
experimental
r control
group. Shoppers
n the ex-
perimental
roup
wereasked what
they
planned
o
pur-
chase at
the time
they
entered
a
supermarket,4
while
shoppers
n the control
group
were not
questioned
about
purchase
ntentions.
Shoppers
n both
groups
conducted
their
shopping,
and
purchases
were recorded at
the
checkout.
A 4
x 4 Latin
square
design
was used
to
balance
out
systematic
variationn
unplanned
purchas-
ing
attributableo
type
of
store,
time
of
day,
and
day
of week. Eight units of a nationalsupermarket hain
were
paired
nto
four
groups,
and
randomlyassigned
o
Treatments
A
through
D. In each
cell,
the
stores
were
randomly assigned
for either
morning
or afternoon-
evening
interviewing.
nterviewswere
done
on
Friday,
Saturday,Sunday
and
either
Tuesday
or
Wednesday,
with the
occurrence f
Tuesday
or
Wednesday
andomly
determined.A total of
596
interviews
was obtained
n
a four-week
period.5
Home
interviewing
was
conducted
o
obtain
the de-
tailed information
hat could
not be
gathered
during
store interviews.
This
phase
involved 196
follow-up
interviewsof the 596
original
shopping
parties.
These
respondents
were nterviewed
within
wo
days
after
their
original nterview.
Effects
of
the
Store
Interview
Since
shoppers
were
systematicallyassigned
to ex-
perimental
and control
groups
and
only
experimental
grouprespondents
were asked
o tell
purchaseplans,
dif-
ferences
in
purchasing
behavior
between the
groups
might
be
attributed
rimarily
o the
influence
f the
entry
interview.
The
experimental
and control
groups
were
compared
by using
three ndices
of
purchasing
ehavior:
(a)
grocery
bill; (b)
numberof
different
products
pur-
chased;6
and
(c)
mixture
of
products
purchased.
The differences
etween he
experimental
ndcontrol
groupgrocery xpenditures renot significant t the .05
probability
evel.
The
entry
interview did not
appear
to affect the amount
spent
during
the
shoppingtrip.
Since the
groceryexpenditure
ategories
used in the
study
consist of
$3
to
$5
intervals,
he
entry
interview
could
actually
ause
an increase
n
groceryexpenditures
up
to
$5
and
still
not
appear
n the data. To
overcome
this
problem
a more
sensitivemeasureof transaction
ize
was
used-number
of different
products
purchased.
'
Again
we are
concerned with the
major
types
of
outcomes
that
occur.
Consequently,
the
observations made in Footnote
2
are
applicable.
An additional
type
of
outcome
would be:
product
not
purchased;
brand
purchased.
This
kind
of
outcome
was omitted
because it
infrequently
occurs.
'
Experimental
group
respondents
were
first asked if
they
had
a
shopping
list.
If
shoppers
had
a
list,
the interviewer
copied
it;
and
if
a
brand were
not
mentioned
he
asked if
the
re-
spondent
had decided on a
specific
brand. After the interviewer
finished
copying
the
list,
she asked the
shopper
if
there
were
anything
else
that
she
planned
to
purchase
that was not in-
cluded in the
shopping
list.
If
the
respondent
did not have a
shopping
list,
the interviewer continued
to
ask the
respondent
for the productsand brands that she planned to purchase until
the
shopper presumably
exhausted her
purchase
intentions. A
technique
was used that minimized
the
probability
that
shoppers
would know that their
purchases
would
later
be
recorded.
'The
number
of
experimental
and control
group
interviews
conducted in each
store on each
interviewing day
was
propor-
tional to that
store's customer traffic
on
the
day
relative to the
total traffic of
all
eight
stores
during
all
interviewing days.
SNumber
of different
products purchased
differs from num-
ber of
products
purchased
in that
it does not
reflect
multiple
purchases
of the
same
product.
For
example,
if
a
shopper
purchased
two
quarts
of
milk
and
one
loaf of bread the
number of different
products
purchased
would be two.
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CUSTOMER
MPULSE
URCHASING
EHAVIOR
23
The mean
number
of
products
purchased
by experi-
mentaland
control
group shoppers
was 13.1 and
12.9.
This difference
s
not
significant
at the .05
probability
level.
Thus,
the
entry
nterview
did not
appear
o
affect
the
number
of different
products
purchased.
Although
the
entry
nterview
did not affectthe trans-
actionsize, it couldhaveprecipitated n increase n the
incidence
of
purchase
of
some items and
a
decrease
n
others.A
final
test assessed he effectsof
the
entry
nter-
view on the mixture
of
products
purchased.
Purchase
requencies'
were
computed
or 64
product
categories.
For
each
product
category,
he
experimental
group
purchase requency
was
compared
with
the
con-
trol
group frequency.
The
coefficientof correlation
be-
tween the
product
purchase requencies
of the
experi-
mental
and control
groups
is .91. It
appears
that
the
entry
interview
could have
only
slightly
distorted the
mixtureof
products
hat
customers
purchased.
Thus,
asking
respondents
what
they
planned
to
pur-
chase
did
not affect either the
money
spent
in the
store
or the numberof different
productspurchased,
and had
little
effect
on the
mixtureof
productspurchased.
CUSTOMER
DIFFERENCES IN UNPLANNED
PURCHASING
BEHAVIOR
Number
of
Purchases
The
average
customer made
eight
unplanned pur-
chases while the
average
numberof
specifically
planned
purchases
was
only
2.5.
The mean
number
of
purchases
for
any
of
the other intentions-outcomes
ategories
was
less
than 1.0.
In
absolute terms then
unplannedpur-
chasing
was
by
far the more
frequent.
Table 1 gives the dispersionof respondents or two
major
intentions-outcomes
ategories.
The
maximum
numberof
unplannedpurchases
made
by
a
shopper
was
40,
the minimum0 and the standard deviation
9.2.
Both
the
ranges
and
standarddeviationsof the remain-
ing
intentions-outcomes
categories
are
considerably
smaller.
It is
apparent
hat the
incidence
of
unplanned
purchasing
varies
greatly
for
shoppers,
absolutely
and
relatively,
rom the customer
variation n
other
inten-
tions-outcomes
categories.
Percentage
of
Purchases
The intentions-outcomes
ategories
can also be
ex-
pressed
in
percentages.
The
percentage
refers to
the
number of purchases in a given intentions-outcomes
category
for one
respondent
divided
by
the
total
of
different
products purchased by
that
respondent.
In terms of relative
frequency,
the
average
customer
purchased
50.5
percent
of
the
products
on an
unplanned
basis. In
contrast,
the mean
percentage
of
specifically
Table 1
DISTRIBUTIONF
RESPONDENTS
Y
NUMBER ND
PROPORTION F
PURCHASESN
MAJOR
INTENTIONS-OUTCOMES
ATEGORIESa
Number of Intentions- Unplanned
purchases
outcome
urchasesc
planned
purchasesb
purchases
0-7
93.8%
66.0%
8-15
5.7
16.4
16-23
.5
10.0
24-31
-
4.7
32-40
-
1.9
Total
100.0
100.0
Percent
of
Intentions-
Unplanned
Purchases
outcome
planned
purchasesb
purchasesc
0-11
36.3%
18.8%
12-23
22.0
3.2
24-35 17.9 10.0
36-47
6.4
9.3
49-59 8.6
14.4
60-71
2.9
21
1
72-81
1.0
11.5
82-93
-
8.8
94-100
4.9
3.0
Total
100.0
100.0
a
596
respondents.
b
Corresponds
to
Category
1 in
Figure
1.
c
Corresponds
to
Category
9
in
Figure
1.
planned
purchases
s 25.9
percent,
and the
highest
mean
for
any
of the
remaining ategories
s
only
8.2
percent.
In
percentage
erms he
incidence
of
unplanned urchas-
ing is greaterthan the combinationof all other inten-
tions-outcomes
categories.
The wide
variation n the
percentage
of
unplanned
purchases
s
demonstrated
y
the
nearlyequal
distribu-
tion
of
shoppers
across
the
percentage ategories
Table
1).
Specifically
planned
and other
intentions-outcomes
categories
display considerably
less
variation
among
shoppers.
Overall,
hen
unplanned
purchasing
s the most
com-
mon
intentions-outcomes
ategory,
expressed
n
either
absoluteor
percentage
erms.
Also,
shoppersvary
widely
in
the
numberand
percentage
of
unplanned
purchases.
Only
the
proportion
of
unplanned
purchases
will
be the dependentvariable.In this mannerthe effects
of number
of
purchases
are
netted
out,
allowing
number
of
products
ought
o be
a
possibleexplanatory
ariable.
Two
stages
of
analysis
are
necessary
or
understand-
ing
customer
unplannedpurchasing
behavior.The first
stage
s to
determinewhich
variables
are associated
with
the
occurrenceof
different ates
of
unplannedpurchas-
ing,
but this
stage
does
not
explain
how
unplanned
pur-
chasing
occurs or
what
it involves.
The
second
stage
attempts
o
reconstruct
ome
of the
precipitating
on-
ditions that lead to an
unplanned
purchase.
Here, purchase
frequency
is the number of
purchases
of
a
product
divided
by
the
sample
size. Division
by sample
size
is
necessary
to
approximate
experimental
group-control
com-
parability
since the former
consisted
of 596
respondents
and
the latter
196
shoppers.
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JOURNAL F
MARKETING
ESEARCH,
EBRUARY
967
FINDINGS-CORRELA
TES OF IMPULSE
PURCHASING BEHAVIOR
Many
variables
were used in an
attempt
to
explain
customerdifferences
n
unplannedpurchasing
ehavior.
The
analysis
produced
three
major
kinds
of
variables:
(a) variables that are not related to unplanned pur-
chasing
and do not
affect
t;
(b)
variableshat are
related
to but
do not
affect
unplannedpurchasing;
nd
(c)
vari-
ables
that are related
to and affect
unplanned
purchas-
ing.
Variables
Not Associated with
Unplanned Purchasing
Figure
2
itemizesvariables hat are
statistically
nde-
pendent
of
customer
differences n
unplannedpurchas-
ing
behavior.
Economic and
demographic
variables-
income,
number
of
wage
earners,
occupation,
andedu-
cation-do
not influence he rateof
unplanned
urchas-
ing.
The
personality
ariables
used in the
study
have
been
usedby Brim
[6]
andwere derived rom French's
[10]
factor
analytic
review
of
personality
ests. These
per-
sonality
variables are
statistically independent
of
un-
planned
purchasing
on the basis
of
chi-square
and
cor-
relation
coefficient
tests
of
significance.
The
highest
correlation
oefficient
s
only
.09.
Figure
2
VARIABLES
OT
ASSOCIATED ITH
UNPLANNED
URCHASING
A.
Economic
and
Demo-
graphic
Variablesa
1.
Income of the house-
hold
2. Number
of full-time
wage
earners
in the
household
3.
Occupation
of the
household head
4. Formal education
of
the
household
head
B.
Personality
Variablesb
1.
Impulsiveness
2. Dominance
3.
Optimism
4.
Self-confidence
5.
Self-sufficiency
6. Belief in fate
7. Future
time orientation
8.
Desire for
certainty
9. Belief in the predicta-
bility
of
life
10.
Belief
in
multiple
causation
of events
C. General
Food
Shopping
Behavior
Variablesc
1. Size
of
shopping party
2. Existence of a food bud-
get
3.
Frequency
of food
bud-
get
revision
4.
Role of
wife in determin-
ing
food
budget
5.
Use
of food
coupons
6.
Use
of
trading stamps
7.
Recalled
exposure
to
newspaper
advertise-
ments for
grocery
products
8.
Frequency
of
discussion
about
grocery products
a
596
Respondents.
Variables are
independent
of
the
per-
centage
of customer
unplanned
purchases
at
the .05 level of
probability (chi square).
b
196
Respondents.
Variables are
independent
of the
per-
centage
of
customer
unplanned
purchases
at
the .05
level
of
probability
(chi-square
and
correlation
coefficients).
e
196
Respondents.
Variables
are
independent
of
the
per-
centage
of
customer
unplanned
purchases
at the .05 level
of
probability
(chi
square).
Finally,
an
array
of
general
ood
shopping
variables
are
independent
of customer differences
n
unplanned
purchasing.
The
presence
of food
budgets
andthe use of
food
coupons
and
trading tamps
do not
affect
customer
rates
of
unplannedpurchasing.
Variables Associated with Unplanned Purchasing
Several
variablesare related to
customer
differences
in
unplanned
purchasing
only
because
they
are
related
to
another
variable,
the
number of
different
products
purchased.
When
the numberof
different
products
pur-
chased
is
held
almost
constant,
these variables
do
not
influence he
percentage
of
unplannedpurchases.8
Al-
though
hese
variablesare
related o
customervariations
in
unplannedpurchasing,
hey
do not affect the
be-
havior.These variablesare:
A.
Demographic
variables
1.
Number
of
people
living
in
the
household
2.
Sex
of
the
shopper
B.
General
food
shopping
behavior
variables:
1. Number of
shopping trips
made
per
week
2.
Distance
traveled
to
the store
3.
Day
of
week
4.
Time
of
day
5.
Size
of
store
The
shopper's
sex does not affect
unplannedpur-
chasing
behavior.
Women
purchase
a
higher
percentage
of
products
on
an
unplanned
asis,
because
hey
usually
make more
purchases.
When the number
of
purchases
is
held
constant,
men and
women
have the same
degree
of
susceptibility
o
unplannedpurchasing.
Day
of week does not
affect
unplanned
purchasing.
In-store
promotional
activities
are,
of
course,
more
intensive
on
Thursday,Friday,
and
Saturday.
Percent-
ages
of
unplanned
purchases
are
higher
on
Friday
and
Saturday, only
because
more
products
are
pur-
chased
on these
days;
when
the number
of
products
purchased
s
held
constant,
day
of
week
is not related
to
unplanned
purchasing.
Variables
Affecting
UnplannedPurchasing
Three
categories
of
independent
ariablesaffect
cus-
tomer
unplannedpurchasing
nd are
related
o it.
They
are:
(a)
transaction
ize
variables, b)
transaction
truc-
ture variables
and
(c)
characteristics
f
the
shopping
party.
This
study
used two
measures
of transaction
size:
numberof different roductspurchased ndgrocerybill.
Figure
3
depicts
the
approximate
area
containing
the
8
The
analytical
strategy
of
holding
transaction
size
approxi-
mately
constant
as
to remove
one source
of concomitant
varia-
tion
involved
the
following:
(a)
total number of
different
prod-
ucts
purchased
were divided
into
quartiles; b) contingency
tables
and
the
resultant
chi
squares
were derived
for
the
relationship
between
the
independent
variables
and the
percentage
of un-
planned purchases
for each of
the four
quartiles.
Since
this
procedure
leaves some
intracell
variation in
the number
of
different
products purchased,
transaction size
has
been
con-
trolled
rather than left as
a continuous
variable.
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CUSTOMERMPULSE
URCHASINGEHAVIOR
25
Figure
3
CONFIGURATION F
THE SCATTERIAGRAM
OF
THE
RELATIONSHIPETWEEN HE
NUMBER
OF
DIFFERENT
PRODUCTSPURCHASED ND
THE
PERCENTAGEF
UNPLANNED
URCHASESa
Percent of
unplanned
purchases
100
-
90
-
80
70
60
50
40
30
20
10
I
,
I
,
I I I I
I
I
0
5
10
15
20
25 30
35
40
45 50
55
Different
products
purchased
aCoefficient of
correlation of
the
two variables
equal
to
.44 with n
at
559.
559 coordinates
of number of different
products pur-
chasedandthe percentage f unplannedpurchases.The
relationship uggests
that
when
the numberof different
productspurchased
s
low,
the
proportion
of
unplanned
purchases may
be either
high
or
low,
but when the
number
of
different
products purchased
is
high,
the
percentage
of
unplannedpurchases
s
also
high.
Gen-
erally,
the
greater
the
number
of different
products
purchased,
he
greater
he
percentage
f
unplanned
pur-
chases.
Since the
percentage
of
unplanned
purchases
s
actu-
ally
the numberof
unplanned
purchases
divided
by
the
total
number
of different
productspurchased,Figure
3
shows
the
relationship
etween he
number
f
unplanned
purchases
and
the
number
of
different
products pur-chased.9
If
the
numberof different
products
purchased
deter-
mined
all variation
n the number of
unplanned
pur-
chases,
then
the
relationship
would
be Line
segment
1
in
Figure
3.1o
Given
any
number of different
products
purchased,
he
verticaldistancebetweenthe actual
per-
centage
of
unplanned
urchases
and
the
percentage
ndi-
cated by Line segment 1 indicates the variation in
the number
of
unplanned
purchases
that is not
ac-
counted
for
by
the
number
of different
products
pur-
chased.
In
Figure
3 all
observations
ie in the
area
formed
by
Line
segments
1 and
2. As the number
of
different
products
purchased
ncreases,
he
vertical distance
be-
tweenLine
segments
1
and
2
decreases.
Therefore,
s the
number
of different
products purchased
ncreases,
the
unaccounted
ariation
n
the number
of
unplannedpur-
chases
decreases.
Grocery
bill is
also
a measureof
transactionize.
Fig-
ure 4
depicts
the
relationship
between
unplanned
pur-
chasing
and
grocery
bills. The
percentage
f
respondents
purchasing
ver 55
percent
of their total
purchases
on
an
unplanned
basis
increases
as the
grocery
bill in-
creasesuntil the bill
exceeds
$20,
then
the
percentage
declines
slightly.
Transaction
tructure efers
to
the
mixture
of
prod-
ucts
purchased.
Two measures
of
transaction
tructure
affect customer
unplanned
purchasing
and are
related
In
Figure
3 the
y
axis
is
equal
to
a/b,
and the
x
axis
is
equal
to b where
a
is the
number
of
unplanned purchases,
b
is
the number of different
products
purchased
and
a/b
is the
percentage
of
unplanned
purchases.
0o
In Line
segment
1
of
Figure
3,
the absolute
change
in
the
number
of
unplanned
purchases equals
the
absolute
change
in the
number of
different
products
purchased;
that
is,
the
num-
ber of different
products
purchased
accounts
for all
of
the
variation
in the
number
of
unplannedpurchases.
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26
JOURNAL
F MARKETING
ESEARCH,
EBRUARY
967
Figure
4
RELATIONSHIP
ETWEENGROCERY
BILLSAND
THE
PERCENTAGE F UNPLANNED
PURCHASES
Percent
of
respondents
purchasing
over
55
percent
of
products
on an unplanned
basis
100
-
90
80
70
60
50
40
30
20
10
0
Under
$2.01
$5.01
$10.01 $15.01
over
$2.00
to to to
to
$20.00
$5.00
$10.00 $15.00
$20.00
Grocery
bill
to the behavior:
(a)
kind
of
shopping trip
and
(b)
prod-
uct
purchase
frequencies.
Kind
of
shopping
trip
may
measure
some of the
things
that
transaction
size
measures,
but some
it does
not.
When transaction
size is
held
constant,
kind of
shopping
trip
is still
significantly
related
to
the
percent-
age
of
unplanned
purchases.
As
Figure
5
indicates,
major
shopping
trips
are
generally
characterized
by
a
larger
percentage
of
unplanned purchases
than
are
fill-
in
trips.
Additional empirical measures of transaction struc-
ture
are
not
available;
thus
further
study
of the rela-
tionship
between
unplanned
purchasing
and
transaction
structures
requires
an indirect
approach.
One
approach
uses
the
unplanned purchase percentage
for each
prod-
uct
category
as the
dependent
variable
and
attempts
to
find
product
characteristics
that affect this
percentage.
Using
some of
the
insights
advanced
by
Stern
[19],
four
product
characteristics
were tested:
(a)
product
purchase
frequencies,
(b)
price,
(c)
amount
of
product
advertising,
and
(d)
ease
of
product
storage.
Only product purchase
frequencies
are
significantly
related
to
product
unplanned
purchase
rates.
The
linear
correlation coefficient
of the 63
product purchase
fre-
quencies
and
product
unplanned
purchase
rates
is -.60.
Products such as
milk,
bread,
eggs,
etc.,
which
have a
high
frequency
of
purchase,
tend
to
have a
relatively
low
unplanned purchase
percentage.
In
contrast,
prod-
ucts
having
a low
frequency
of
purchase
like
drugs,
toiletries,
and
dessert
items,
tend to
have
a
relatively
high unplanned
purchase percentage.
Given two customer transactions of the same size,
one would
expect
an inverse
relationship
between the
purchase
frequencies
of the
products
included
in the
transaction and the
percentage
of
unplanned
purchases
that
comprise
the transaction.
For
example,
if the
shopper
purchased products
having high purchase
fre-
quencies,
she would
be
expected
to
be
a
relatively
low
percentage unplanned
purchaser.
If
she
purchased
the
same number of
products,
but
the
products
purchased
are
not
purchased frequently,
she would
be
expected
to
make
a
higher percentage
of
unplanned purchases.
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CUSTOMER
MPULSE
URCHASINGEHAVIOR
27
Figure
5
RELATIONSHIP
ETWEEN IND
OF
SHOPPING
TRIPAND
THE
PERCENTAGE
F UNPLANNED
URCHASES
Percent
of
respondents
in each
unplanned
purchase percentage
category
40
35
30
Major
Trip
25
15
10
5
I0
0
I
I
I
I
0
0-33 34-55 56-71 72-100
Percentage
of
unplanned purchases
Only
two of
the
shoppingparty
characteristics
ffect
customer
unplanned
purchasing.
These characteristics
are: (a) presenceof a shopping ist and (b) numberof
years
the
shopping
party
has
been married.
The effect
of
a
shopping
ist on
unplanned
purchase
percentages
s uncertain.
In
fact,
the mean
percentage
of
unplannedpurchases
or
customers
having
a
shopping
list
is
the same
as
for
those without
a
shopping
ist--51
percent.
Further
analysis
ndicates that
the effect of
a
shopping
ist
on
unplanned
purchasing
depends
on
the
transaction
size. When
more than
15 or 20
products
are
purchased,shoppers
having
a
list
make
a
smaller
percentage
of
unplanned
purchases.
However,
when
less
than
15
or
20
products
are
purchased,
he
shopping
list
does not
affect
the
percentage
of
unplanned pur-
chases.
Finally,
couples
married ess than 10
years
have the
lowest
rate
of
unplanned
purchasing.
Generally,
the
percentage
f
unplannedpurchasing
ncreases
rregularly
as
length
of
marriage
ncreases.
Composite
Determinants
of
UnplannedPurchasing
Thus
far
the
effects of
only
one
independent
ariable
on
unplanned
purchasing
have
been
considered.
Using
the
percentage
of
unplanned
purchases
made
by
cus-
tomers
as
the
dependent
variable,
the effects of
all
combinations
of four
independent
variables
are
now
examined.
Sincethreeof thesefourvariablesarediscreterather
than
continuous,
the
analytical
device
is
analysis
of
variance.
However,
analysis
of
variance
cannot
be
used
in its most usual
manner
because
the data
violate as-
sumptions
of the
procedure.12
This, however,
s not too
debilitating
ince
analysis
of
variance
s
not
being
used
to
test
the
significance
of these
variables,
as
this has
already
been
accomplished
using
other statistical
ech-
niques.
Rather,
analysis
of
variance
used here assesses
the different ffects
of variouscombinations
f
variables.
Accordingly,
withincell
or
unexplained
ariation
an
be
used as
the criterion
for
determining
which combina-
tion
of
independent
variables
accounts or
the
greatest
Product purchase frequencies cannot be included in this
analysis
since it
requires
the use of another
dependent
variable
-product
unplannedpurchase
rates. Number
of
different
prod-
ucts
purchased
rather than
grocery
bill
will
be
used
as
the
measure of transactionsize.
12The
various
classifications
of
the
four
independent
vari-
ables result in
a
48-cell table. When the 586
respondents
are
assigned
to
appropriate
cells,
cell sizes are neither
equal
or
proportional.
The
addition theorem
for sum of
squares
does
not hold in the four variable
case
when
cell
sizes are
both
unequal
and
disproportionate.Consequently
the common
sig-
nificance test
and estimation of
components
of
variance are
not
possible
[18, p. 379].
This limitation was overcome
by
separatelycalculating
each
within cell
sum of
squares.
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28 JOURNAL F MARKETING
ESEARCH,
EBRUARY
967
portion
of the variation
n
unplanned urchasing.
There-
fore,
the smaller
he
within
cell
variation,
he morethe
given
ndependent
ariable
ombination ccounts
or the
variation
n
unplannedpurchasing.
Table
2
presents
he
within cell or
unexplained
ari-
ation
for each
possible
combinationof the four
inde-
pendent variables.The numberof differentproducts
purchased
accounts
for
more variation in
unplanned
purchasing
han
any
other
variable.Numberof
years
married
appears
o
be
the
second
strongest
variable
ol-
lowed
by
the kind
of
shopping
rip.
The
fact that
shop-
ping
lists do not
produce
any
variation
n
unplanned
purchasing
s
consistent
with
the
earlier
findings
that
shopping
ists
affect
unplanned
purchasing
only
when
more than
15 or
20
products
are
purchased.
First
and second order combinations
again
demon-
strate
the
relative
strength
of
number
of
different
prod-
ucts
purchased.
The
percentage
of
accounted-for aria-
tion
is increased
urther
as the other
three variables
are
combined
with the
number of different
products pur-
chased.The least amountof unaccounted-for ariation
Table 2
ANALYSIS
F VARIANCE PPLIED
O INDEPENDENT
VARIABLES
IGNIFICANTLY
ELATEDO THE
PERCENTAGE
F UNPLANNED
URCHASESa
Within cell
variation
Independentvariable
combinations
mean
ean
squareb
Number
of different
products
purchased
693
Major
or
fill-in
shopping trip
841
Presence of shopping list 861
Number
of
years
shopping
party
has
been
married 793
1st
order
combinations
Number
of
products
purchased;
major
or fill-in
627
Number
of
products purchased;
shopping
list 615
Number
of
products
purchased;
years
married 641
Major
or
fill-in;
shopping
list 772
Major
or
fill-in;
years
married
784
Shopping
list;
years
married
799
2nd order
combinations
Number
of
products
purchased;
major
or
fill-in;
605
shopping
list
Number
of
products
purchased;
major
or
fill-in;
607
years
married
Number
of
products
purchased;
shopping
list;
600
years
married
Major
or
fill-in;
shopping list;
years
married
768
3rd order combination
Number
of
products purchased;
major
or
fill-in;
574
shopping
list;
years
married
Total
861
a
Significantly
related
means:
(a)
relationships
with
chi-
square
tests
of
significance
equal
to or less than
.05,
or linear
correlation
coefficients
that
are
significantly
different from
zero
at the
.05 level
of
probability
and
(b)
relationships
that
ap-
parently
are not
attributable
to concomitant
variation.
b
Mean
square
is
the
within cell
sum of
squares
divided
by
the
appropriate
degrees
of freedom.
in
customer
unplanned
purchasing
esults
when all four
variablesare
used
together.
FINDINGS--CUSTOMERS'
PRE-SHOPPING
PURCHASE
SITUATIONS
AND
UNPLANNED PURCHASES
The
discussion of the
relationships
between un-
planned
purchasing
nd other
variables
s
based on the
usual
definitionof
unplannedpurchasing; purchase
s
unplanned
f the
respondent
did
not indicate
a
plan
to
purchase
it.
Thus,
all
unplanned
purchasing
s
con-
sideredas
homogeneous
ehavior.
However,
as
some
writers
19]
have
pointed
out,
there
may
be
several
kinds
of
unplanned
purchases.
The
classification sedin this
study
s
an abbreviated
ersion
of
Alderson's
[1]
classificationof
purchase
situations.
An
unplanned
purchase
is
classified
on
the basis of
whether
he
product
was
purchased
before,
then further
classified
according
o
whether t
represents
ut-of-stock
or inventoryadditionpurchases,and then according o
whether
the
brand
purchased
s the same
as
the
last
brand
purchased.
The
classification onsists of five
categories
of
un-
planned
purchases.
Each of
187
shoppingparties,
nter-
viewed in
Phase
II,
was
asked
to
indicate
he
appropri-
ate
category
or
earlier
unplanned
purchases.
Pre-Shopping
Need
and
Experience
Table 3
gives
an
analysis
of
unplanned
purchases
or
the
purchaser's
xperience
with
product
and brand
and
his
pre-shopping
nventory
ituation.
Of the
unplanned
purchases,
97
percent
involved
products
purchased
before. Of the unplannedpurchases represented by
products
hat had
been
purchased
before,
nearly
64
per-
cent
were
out-of-stock
same brand
purchases,
six
per-
cent
were
out-of-stock different
brand
purchases,
23
percent
were
inventory-addition
ame brand
purchases
and
four
percent
were
nventory-addition
ifferent
brand
purchases.
Nearly
86
percent
of the
unplanned
purchases
epre-
sent
situations n
which
both
product
and
brand have
been
purchased.
Slightly
over
10
percentrepresent
a
situation n which
the
product
but
not the
brand
has
been
purchased.
COMPETING
EXPLANATIONS
FOR
UNPLANNED PURCHASING
Only
competing xplanations
f the
relationships
will
be
discussed,
they
are:
(a)
the
exposure
to
in-store
stimuli
hypothesis
and
(b)
the
customer-commitment
hypothesis.
Withone
exception 2],
previous
nvestigations
f un-
planned
purchasing
ave
explained
t
as
exposure
o
in-
store stimuli.
In
fact,
unplanned
purchasing
eems
to be
the same as in-store
decisions
or
the effects
of in-store
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CUSTOMERMPULSE
URCHASINGEHAVIOR
29
suggestion.
n-store
stimuli
apparently
reate new
needs
or remind he
shopper
of
temporarily
orgotten
needs.
The customer-commitment
ypothesissuggests
that
differences
between
purchase
ntentions
and
actual
pur-
chases
are caused
by incomplete
measuresof
purchase
intentions.
Differences
exist between measured
and
actualpurchase ntentionsbecause the shopperis un-
willing
or unable to
spend
the
time
and effort
neces-
sary
o
itemizeher
purchaseplans.
The customer
may
be
unwilling
o
itemize
her
pur-
chase intentions because she does not want to
devote
the time
and
thoughtnecessary
o
give
the interviewer
comprehensive
ist of
her
purchase plans.
Instead
she
gives
the
interviewer
nly
an
incomplete
temization
of
her
purchase
plans.
Several
plausible
reasons
explain
why
the
shopper
may
be
unableto
itemizeher
purchase
ntentions.
First,
the
shopper
may
know what she
will
purchase
but
may
be unable
to
express
her
purchase
intentions
because
of the
nature of
the
interview. The
methodology
re-
quired
he
shopper,
withouta
shopping
ist,
to
rely
onher
memory
for
purchase
intentions.
Unaided
and
nearly
spontaneous
recall
is
used
to
measure
purchase
plans.
This
procedure
almost
guarantees
hat measured
pur-
chase intentions
will
deviate somewhat
from
actual
purchase
plans.
Also,
the
shopper
may
know what
she
will
purchase
but
be
unable to relate these
intentions,
regardless
f
the interviewer's
ssistance.
Without
expo-
sure
to
in-store
stimuli,
the
shopper
may
be unable to
tell the
interviewerwhat
she will
purchase.
The
validity
of these
hypotheses
s assessed
by
ex-
amining
he
degree
to
which each accounts
or
the find-
ings
of the
present
study
and other
nvestigations
f
un-
planned purchasing.
Transaction
Size
Figure
4 indicated
hat the
percentage
of
unplanned
purchases
ncreased
as the numberof
different
products
purchased
ncreased.
Further,
as the number
of
different
products purchased
ncreased,
it
accounted
for more
variation
n
the number of
unplanned
purchases.
For the
in-store
stimuli
hypothesis
to
apply,
it
is
necessary
to
assume that the
amount
of customer
ex-
posure
to in-store
stimuli
increasesas the numberof
different
products
purchased
ncreases.Then the
greater
the number
of
productspurchased,
he
greater
the ex-
posure
to in-store
stimuli
and,
hence,
the
greater
the
percentage f unplannedpurchases.
The
customer-commitment
ypothesisexplains
that
as the numberof
different
products
a customer ntends
to
purchase
ncreases,
he customer inds it
increasingly
more difficultand time
consuming
o itemize his
pur-
chase
intentions.
Consequently,
s the numberof
prod-
ucts
purchased
ncreases,
he differencebetween
actual
and measured
purchase
ntentionsalso increases.
If the
customer
commitment
explanation
has
any
validity,
it would seem that measured
purchase
nten-
Table 3
CUSTOMERS'
RE-SHOPPINGXPERIENCE
ND
NEEDFOR
UNPLANNED
URCHASESa
Composition
of unplanned
Numberof
Percent of
purchases
unplanned
unplanned
purchases purchases
Purchased
before
Out-of-stock;
same
brand 813
63.6%
Out-of-stock;
different brand
78
6.1
Inventory-addition;
same
297
23.2
brand
Inventory-addition;
different 52
4.1
brand
Not
purchased
before
39
3.0
Total
1279
100.0%
a
187
respondents
tions should
correspond
more
closely
to
actual
purchase
intentions when the customer'stime and effort are
minimized. n
order o
minimize
ustomer
commitment,
each
shopper
was
asked to
indicate
during
the
store
entry
nterview
how
much
she
planned
o
spend. Spend-
ing
intentions
were
then
compared
with
actual
grocery
expenditures.
There is
a
strong
endency
or
actual
expenditures
o
approximate
spending
intentions
(Table
4).
Shoppers
aremore
likely
to
spend
less
than
they
anticipated
han
they
are
to
spend
more
than
they
planned.
Shopping
Trip
Figure
5
shows
that the
percentage
f
unplanned
pur-
chases
was
larger
during
major
shopping
trips
than
during ill-intrips.The exposurehypothesis ustifies his
findingby
asserting
hat
during
ill-in
trips
the
shopper's
needs
are
more
clearly
identified
so
that
she is
less
susceptible
o
in-store
suggestion.
During
major
trips,
however,
he
shopper's
needs
are
not well
defined,
hus
the
shopper
s
more
receptive
o
in-store
stimuli.
The
customer-commitment
ypothesis
also
accounts
for
the
relationship.
Fill-in
trips
typically
satisfy
rela-
tively
urgent
needs.
Moreover,
products
purchased
dur-
ing
fill-in
trips
probably
have
higher
purchase
fre-
quencies
and
a
longer
purchase
history
than
most
products
purchased
uring
major
rips.
Therefore,
ill-in
trips
probably
involve
smaller
effort and
time
com-
mitments
than
major
trips,
so
that
measured
purchase
intentionsdeviate ess from actualpurchase ntentions.
Frequency
of
Purchase
The
exposure
hypothesis gives
two
reasons
for
the
inverse
relationship
between
product
purchase
fre-
quencies
and
productunplanned
purchase
rates.
First,
products
with
high
purchase
requencies
usually
receive
less
promotional
mphasis
han
other
products.
Second,
customers
may
be
less
susceptible
o
in-store
promotions
for
products
with
high
purchase
requencies.
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30
JOURNAL
F MARKETING
ESEARCH,
EBRUARY
967
Table 4
SPENDING
NTENTIONSOMPAREDWITH
ACTUAL
XPENDITURESa
Grocery
billb
Spending
intentions Less The More
than same than
$
2.00
or Less
-
76.1% 23.9% 100.0%
2.01-$5.00
19.3%
68.6 12.1
100.0
5.01-10.00
32.1
54.5 13.4 100.0
10.01-15.00
38.3 30.0
31.7
100.0
15.01-20.00
37.8
33.3
28.9 100.0
20.01-25.00
32.3 47.1
20.6
100.0
25.01-30.00
50.0
25.0
25.0
100.0
Over
30.00 56.3 43.7
-
100.0
a
596
respondents
b
Shown
in
percent
of
respondents.
The
customer-commitment
ypothesis
uses
a
simple
learning heoryparadigmo account or therelationship
[4].
Products
having
high
purchase frequencies
also
usually
have an
extended
purchasehistory.
Thus
during
any
shopping
rip,
a customer s more
likely
to
purchase
products
with
higher
purchase
frequencies.
Thus,
fre-
quentlypurchased
products
have lower
unplanned
pur-
chase
rates;
t is easier
or
the
shopper
o remember
hat
she
plans
to
purchase
hem.13
Shopping
List
A
shopping
ist
influences
unplanned
purchasing nly
when
more than
15
products
are
purchased; hoppers
with a
list
have
lower
unplanned
rates.
The
exposure
hypothesis
assumes hat
a
shopper
who
expects
to
pur-
chase a small numberof itemshas clearlydefinedneeds
and
is
less
susceptible
o
in-store stimuli. A
shopping
list does
not
affect
this behavior.
However,
the
shopper
with
plans
to
purchase
a
large
number of
products,
according
to the
exposure
hypothesis,
uses
in-store
stimuli o
identify
hopping
needs.
According
to
the
customer-commitment
ypothesis,
when
few
products
are
purchased,
he time
and effort
commitments
nvolved
in
itemizingpurchaseplans
are
small
and
are
only
marginally
educed
by
a
shopping
list. If
a
large
number
of
products
are
purchased,
he
effort and
time
commitmentsare
high,
and are
greatly
reduced
by
a
shopping
ist.
Years Married
The
exposure hypothesis
can
account for the
in-
creased
rate of
unplanned
purchasing
as
years
married
increase.
First,
as
years
married ncrease and
the
chil-
dren
grow,
both
the
quantity
and
variety
of
their
food
consumption
ncrease.
Pre-planning
ecomesmore time
consuming
and
difficult,
o the
shopper
may rely
more
on in-store
stimuli.
Also,
other
householdmembers
may
accept
the housewife's
purchases
so that she can
make
more
in-store
purchase
decisions.
Finally,
older
shoppers
have
probably
had more
shopping
experience
and
may
feel
better
qualified
o evaluate
purchase
alter-
natives n thestore.
The
customer-commitment
xplanation
assumesthat
shoppers
married
for shorter times
can
give
a
more
accurate
itemization of
purchase
intentions. Since
younger
shoppers
usually
have
smaller
incomes,
they
may
plan grocery
expenditures.Younger
households
may
have
greater
husband-wife
participation
n deter-
mining
grocery
expenditures,
and their
purchasesmay
be
thought
out
before
the
shopping rip.
Since
the
num-
ber
and
variety
of
purchases
usually
ncrease
when
the
size
of
the
household
increases,
it
may
be
easier for
younger
couples
to
give
a more
complete
isting
of
pur-
chase
plans.
Types of
Unplanned
Purchases
Most
unplanned
purchases
represent
either
out-of-
stock
same
brandor
inventory-addition
ame
brand
pur-
chases.
In-store
stimuli
usually
remind
shoppers
of
present
or
future
needs
rather han
evoking
new
needs.
Out-of-stock
same
brand
unplanned
purchases
do
seem
consistent
with
the
customer-commitment
y-
pothesis.
That
is,
most
of
these
purchases
are
probably
routine,
so the
customer
ould
probably
dentify
hem
as
purchase
ntentions
given
an
appropriate
esearch de-
sign.
However,
inventory-addition
ame
brand
pur-
chases
may
have
actually
been
planned,
others
were
probably
precipitated y
in-store
timuli.
Unplannedpurchasingcan be described as a blend
of the
hypothesis.
Some
unplanned
purchases
are
prob-
ably
precipitated
y
exposure
o in-store
stimuli.
Others
are not
unplanned
at all
but are
caused
by
the
way
in
which
the behavior
is
usually
measured.
These
pur-
chases
are
classifiedas
unplanned
because measured
purchase
ntentions
deviate
from
actual
purchase
plans
because of
the
customer's
nability
or
unwillingness
o
give
the time
and
thought
necessary
to tell
the
inter-
viewer her
purchase
plans.
Unfortunately,
he
data
do
not
seem
to
permit
a
conclusion
about these
two ex-
planations
for
customer
unplanned
purchasing
be-
havior.
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Marketing
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This statement
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consistent
with
purchase
intentions data.
Frequently purchased
products
are more
likely
to
be
mentioned
as
purchase
intentions
regardless
of
whether
these
products
are
actually purchased.
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MPULSEURCHASINGEHAVIOR
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