Customer Impulse Buying Behaviour.pdf

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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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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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3/12

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.



4/12

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.



5/12

24

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


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.



6/12

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.



7/12

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.



8/12

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.



9/12

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


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



10/12

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.



11/12

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.

REFERENCES

1.

Wroe

Alderson,

Marketing

Behavior

and

Executive

Action,

Homewood,

ll.:

RichardD.

Irwin, nc.,

1957.

2. William

Applebaum,

Studying

Customer

Behaviorn Re-

tail

Stores,

Journal

of

Marketing,

16

(October

1951),

172-8.

3.

Seymour

Banks,

Experimentation

n

Marketing,

New

York:

McGraw-Hill

ook

Co.,

1965.

4.

James

A.

Bayton, Motivation,

Cognition,

Learning-Basic

Factors

n

Consumer

Behavior,

ournal

of

Marketing,

2

(January 1958),

282-9.

'

This statement

is

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.



12/12

CUSTOMER

MPULSEURCHASINGEHAVIOR

31

5. Brand

Switching

and

Impulse Buying,

New York: Point-

of-Purchase

Advertising

Institute,

1963.

6.

Orville

Brim,

et

al.,

Personality

and

Decision

Processes,

Stanford:

Stanford

University

Press,

1962.

7.

Vernon

T.

Clover,

Relative

Importance

of

Impulse Buy-

ing

in

Retail

Stores,

Journal

of

Marketing,

15

(July

1950),

66-70.

8. ConsumerBuyingHabits Studies,E. I. du Pont De Nemours

and

Company,

1945, 1949, 1954, 1959,

1965.

9.

Keith

Cox,

'The

Responsiveness

of Food Sales to

Shelf

Space Changes

in

Supermarkets,

ournal

of

Marketing

Re-

search,

1

(May

1964),

63-7.

10. John W.

French,

The

Description

of Personality

Measure-

ments

in

Terms

of

Rotated

Factors,

Princeton,

N.J.:

Educa-

tional

Testing

Service,

Princeton

University Press,

1953.

11. How

People

Shop

for

Food,

Market

Research

Corporation

of

America,

manuscript.

12.

Impulse

Buying,

Philadelphia:

The

Curtis

Publishing

Com-

pany

Research

Department,

February

1952.

13.

Daniel

Katz,

Field

Studies,

n

Leon

Festinger

and Daniel

Katz,

eds.,

Research Methods

in

the Behavioral

Sciences,

New York:

Holt, Rinehart,

and

Winston,

1953.

14. Robert F.

Kelly,

An Evaluation of Selected

Variables of

End

Display

Effectiveness, Unpublished

doctoral disserta-

tion,

Harvard

University,

1965.

15.

One

on the

Aisle,

New

York: Life

Marketing

Laboratory,

manuscript.

16. Lawrence W. Patterson, In-Store TrafficFlow, New York:

Point-of-Purchase

Advertising

Institute,

1963.

17. James D.

Shaffer,

The

Influence of

Impulse Buying

or In-

the-Store

Decisions on

Consumers'Food

Purchases,

Michi-

gan

Agricultural Experimental

Station

Journal

Paper

No.

2591.

18.

George

W.

Snedecor,

Statistical

Methods,

Ames,

Iowa:

Iowa

State

College Press,

1961.

19.

Hawkins

Stern,

The

Significance

of

Impulse

Buying

To-

day,

Journal

of

Marketing,

26

(April

1962),

59-62.

20.

C. John

West,

Results of Two

Years

of

Study

Into

Im-

pulse

Buying,

Journal

of

Marketing,

15

(January

1951),

362-3.

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