1. Planned Versus Unplanned Grocery Shopping Behaviour: An
Empirical Study
Dr Michael Bourlakis
School of Agriculture, Food & Rural Development
University of Newcastle upon Tyne
Agriculture Building, Newcastle upon Tyne, NE1 7RU
UNITED KINGDOM
Dr Spyridon Mamalis
Department of Marketing and Quality Control of Agricultural Products
Technological Educational Institute of Western Macedonia
62045, Alistrati Serron
GREECE
Jessica Sangster
School of Agriculture, Food & Rural Development
University of Newcastle upon Tyne
Agriculture Building, Newcastle upon Tyne, NE1 7RU
UNITED KINGDOM
Abstract: - This study is being carried out in order to identify what affects unplanned behaviour, which
accounts for a significant amount of supermarket purchases. This should have a significant affect on the way
products are marketed given that most purchasing decisions would appear to be made in the store itself. The
empirical work combined qualitative and quantitative methods and the results found that consumers paid
emphasis to specific attributes that, in turn, influence their purchasing behaviour, either planned or unplanned.
The above will support retail managers during the development of store strategies with the ultimate aim to
influence consumer behaviour towards increased product purchasing.
Key- Words: Shopping Behaviour, Planned - Unplanned Purchasing, Grocery Retailing, U.K.
1. Introduction
Unplanned behaviour can be difficult to establish
after purchases have been made as customers will
readily justify that they needed a product. The aim
of this paper is to shed light on the above.
Specifically, the authors aim to examine
consumers’ planned purchases prior to shopping by
asking for an oral or written version of what they
intend to buy. In addition, we intend to identify
participants unplanned purchases by asking to see
their receipt after their shopping trip and to
question respondents immediately after shopping to
identify reasons for unplanned purchases being
made. Finally, we investigate what aspects of the
retail environment have the most significant affects
on unplanned purchases. The work in this study is
organised as follows; the next section includes the
relevant literature review and a subsequent section
introduces the methodology employed. Once this
has been done, the results are presented and the
paper concludes by providing specific
recommendations.
2. Literature Review
Several models have been developed to provide a
theoretical framework for consumer decision
making. Omar (1999) suggests that there are five
basic variables affecting that process: shopping
experience, the shopper’s lifestyle, retail
promotion, the price and point of sale. Phillips
(1993) identifies visual perception as an important
variable affecting the shopping process and states
that it is through peripheral processes that
consumers first filter out what is and what is not
relevant to them. Beatty and Ferrel (1998)
highlight that there is little known about the process
Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)

2. of impulse buying and the variables affecting its
enactment. The study highlights severable
variables that affect impulse buying and therefore
unplanned purchases. Mood, especially positive
mood was identified as a variable that influenced
impulse buying. The results of the study found that
in store browsing was positively affected by time
available and the customer’s perceived amount of
available money and time produced positive
feelings and positive influences on actual
purchasing. Several researchers tried to come up
with alternative models that are more specific to
different types of shopping. Phillips (1993) aimed
to provide an alternative model of buyer behaviour
which stated that customers are in a continuous
state of interaction with their environment; the
intent to purchase is far from fixed and can
continue to be modified right up to the point of
purchase. This means that consumers do not go
through stages like previous models but are
continually deciding what they want to buy.
Aylott and Mitchell (1998) show that some
stressors such as crowding and queues that appear
too long can force consumers walk out of the stores
leaving all their potential purchases behind.
Therefore, consumer decision making is affected
by the retail environment and such as an effect can
emanate from atmospherics. Atmospherics have a
huge influence on the way in which the consumer
perceives their environment and therefore the
amount of both planned and unplanned purchases
they make. Kotler (1973) defined store atmosphere
as the effort to design buying environments to
produce specific emotional affects in the buyer that
enhance purchase probability. According to Tai
and Fung (1997), the atmosphere design is
important for the retailer when the number of
competitive outlets increases, when product and
price differences are small or when product entries
are aimed at distinct social classes or lifestyle buyer
groups.
A report by Mintel (2004a) shows specific aspects
which can be used to help create atmosphere such
as colour, lighting, sound and multimedia, scent
and taste, materials and texture, movement and
interactivity and accessories. Aylott and Mitchell
(1998) report that consumers spend significantly
less time in stores when music is loud compared to
when it is soft but there were no significant
difference in sales or the consumers’ reported level
of satisfaction. They also reported that the pace of
in-store traffic flow was significantly slower and
sales significantly higher with slow-tempo music.
Bakamitsos and Siomkos (2004) reviewed the basic
effects on consumers’ affective state (mood) on
attitude formation and noted that the affective state
of consumers at the time they process information
may affect their judgement; therefore, atmospherics
could have considerable impact on sales as well as
the level of unplanned purchases. This suggests
that manipulation of the physical environment may
have a powerful effect on consumer behaviour.
This is further advocated by Turley and Chebat
(2002) who indicated that the environment has the
capacity to influence purchasing behaviour of
shoppers in a wide variety of types and
classifications of stores; hence, relatively small
changes in a number of elements in the retail
environment can have an impact on sales and
purchasing behaviour. Iyer (1989) explains that
impulse buying “occurs when a consumer
experiences a sudden, often powerful and persistent
urge to buy something immediately”. On the other
hand, unplanned purchasing includes “items for
which the purchasing decision was made in the
store and not prior to entering the store”. Thus, all
impulse buying is unplanned, but all impulse
purchases are not necessarily bought on impulse.”
Beatty and Ferrell (1998) define impulse buying as
being “a sudden and immediate purchase with no
pre-shopping intentions either to buy the specific
product category or to fulfil a specific buying task.
The behaviour occurs after experiencing an urge to
buy and it tends to be spontaneous and without a lot
of reflection.”
Further research indicates that the definition of
unplanned purchasing is still unclear
(www.marketingpower.com). Overall, Kollat and
Willet (1969) mention that there are a plethora of
definitions of unplanned and impulse purchasing
depending on the viewpoint taken and similar
research denotes the confusion created
(www.marketingpower.com).
Based on the above, the authors will refer to
unplanned purchasing and impulse purchasing as
being interrelated as there appears to be very little
definable difference between them.
3. Methodology Employed and
Empirical Setting
A combination of qualitative and quantitative
methods was employed. At the start, an exploratory
focus group was carried out which assisted with the
design of the initial questionnaire and was
subsequently pre-tested. The final questionnaire
consisted of both open ended questions, allowing
for more in-depth information to be collected, and
of a set of importance scale questions allowing for
an information provision on, inter alia, the
Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)

3. importance respondents place on supermarket store
features. The empirical work involved dealing with
a sample of consumers who shop at a leading U.K.
food retailer named in the paper as “Retailer X”
and permission was granted by that retailer before
embarking on the research.
According to Mintel (2004b), food retailing is the
largest sector of UK retailing. These firms provide
convenience, low prices and product innovation.
Currently, within the U.K. food retailing sector,
there are four main retailers: Tesco, Asda,
Sainsbury’s and the Morrisons/Safeway group that
account for 65% of the total food retailing sector
sales. The authors implemented convenience
sampling and 100 consumers participated
altogether. We aimed to achieve an equal amount
of male and females and a range of ages to make
the sample as unbiased as possible. Respondents
were asked before their shopping trip what they
intend to buy and an exact list was devised (see
Iyer, 1989). On completing their shopping,
respondents were asked to list their purchases and
this was compared to the list they gave before
shopping (see Block and Morwitz, 1999).
Unplanned purchases were noted along with any
planned purchases that were not made.
Subsequently, we aimed to establish the key
reasons that modified respondents’ behaviour and
13 questions are designed using a 5-point
importance scale which will gather data on
participants’ importance ratings of store features.
This will allow for a later factor analysis. Data was
collected and inputted into SPSS and a frequency
distribution technique was used to analyse the
results for participants’ characteristics. Factor
analysis is a multivariate statistical method whose
primary purpose is to define the underlying
structure in a data matrix. It addresses the problem
of analysing the structure of the correlations among
a large number of variables by defining a common
set of underlying dimensions known as factors
(Hair, 1998). It is also important to be able to
identify how many factors should be used; the latter
can be done by using either the eigenvalue criterion
or a scree test or the variance / cumulative variance
criterion. The objective of the analysis was to
identify the underlying dimensions related to data.
This was conducted on the importance respondents
placed on 13 supermarket features and a varimax
solution was used to help derive the factors. Out of
the 100 respondents sampled, 96 agreed to
complete the questionnaire, a 96% response rate.
4. Key Findings
The results show that the spread of male and
female respondents was fairly even and the age
groups varied dramatically with over half the
respondents questioned being under 34. This could
be for a number of reasons; for example, nearby
was a student residence which would have
accounted for the large number of students
questioned. Very few people over 65 were
questioned that again could be down to the location
of the supermarket. Although near a residential
area, it could still be deemed to be far away by
some elderly people.
The results for social class and income groups
reflected each other; over 40% of respondents
belonged to the lower social class DE and over
50% of respondents earned between £0-19,999.
The research was carried within an area with the
lowest participation from the top socioeconomic
group (AB) and the 2nd
highest long term
unemployment in the U.K.
(http://www.statistics.gov.uk). This could account
for the uneven spread on AB, C1C2 and DE social
groups. Respondents were asked for an exact
purchasing list. Specifically, 74% of respondents
came to the store with the intention of buying 4 or
less products; however the majority of people
questioned were carrying baskets rather than
pushing trolleys which would account for the low
level of planned purchases. The fact that people
were using baskets rather than trolleys could also
reflect the low level of unplanned purchases, as
only 4.1% made over 7 unplanned purchases. Over
43% of respondents made between one and two
unplanned purchases and 70% of respondents made
between one and six unplanned purchases. It is also
possible that as many people were questioned
during lunch time hours that they did not want to
buy anything unnecessary to take back to work
with them.
Factor analysis was also carried out to allow for the
results to be analysed along side the results for
unplanned and planned purchasing. This may
provide further information as to why unplanned
purchases are made and what factors affect them.
Using Bartlett’s test for Sphericity, it is possible to
test whether the data is correlated.
Hο: The data is not correlated
H: The data is correlated
At 5% significance level, the null hypothesis can be
rejected as the Bartlett’s test for sphericity contains
value of 286.441 and the output produced a result
of almost 0.7 (rounded) classified as ‘middling’.
Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)

4. It is therefore concluded that the variables are
correlated.
Table 1: Rotated Component Matrix for importance
of store attributes
Component
1 2 3 4 5 h²
Pleasant
enviro-
nment
.450 .643 -.166 8.877
E-02
.150 .674
Availa-
bility of
well
known
brands
.107 .802 6.206
E-02
-.145 9.939
E-02
.689
Availa-
bility of
own
brands
7.830
E-03
4.250
E-02
-1.019
E-02
.793 2.998
E-02
.631
High
product
quality
.495 .351 9.555
E-02
-2.731
E-02
-.574 .707
Value for
money
.160 2.740
E-02
.848 1.838
E-02
-.197 .785
Low
prices
-.162 -9.759
E-02
.693 .174 .200 .587
Promo-
tions
-.146 .472 .410 .322 .396 .673
Clean
store
.764 .143 -9.265
E-02
.298 -4.734
E-03
.702
Friendly/
helpful
staff
.864 4.890
E-02
8.422
E-02
-.105 6.817
E-02
.772
Checkout
queue
times
2.242
E-02
.640 -8.122
E-02
.384 -.236 .619
Loyalty
points
.184 .154 4.414
E-02
7.521
E-02
.860 .805
Money
off offers
7.113
E-04
4.676
E-02
.448 .503 .380 .600
2 for 1
offers
.167 3.550
E-02
.351 .649 7.235
E-02
.579
The column h² represents communality and 80.5%
of the variance is explained by loyalty points. All
of the communalities have been explained, with the
lowest percentage of 57.9% being for 2 for the
price of 1 offers. Total variance explained can also
be used to identify goodness of fit and shows the
proportion of the variance of all variables explained
by a specific factor. Table 2 shows the total
variance explained.
Table 2: Total Variance Explained
Initial
Eigenvalues
Component Total % of
Variance
Cumulative
%
1 2.997 23.057 23.057
2 2.309 17.765 40.822
3 1.369 10.527 51.350
4 1.130 8.693 60.043
5 1.017 7.821 67.864
6 .804 6.182 74.046
7 .649 4.992 79.038
8 .618 4.753 83.791
9 .542 4.166 87.957
10 .498 3.834 91.791
11 .414 3.186 94.976
12 .365 2.809 97.785
13 .288 2.215 100.000
The five factors together explain 67.864% of total
variance. Date reduction rate is 61.5% and total
variance explained is 68%; in total, there was an
information loss of 32%.
Table 3: Interpreting the solution
Factor and
Item
Loading Interpretation
Factor 1:
Checkout
queue
times
0.864 Queuing times
Factor 2:
Availability
of well-
known
brands
0.802 Choice
Factor 3:
Value for
money
0.848 Value
Factor 4:
Availability
of own-
brands
0.793 Price
awareness
Two for
one offers
0.649
Factor 5:
Loyalty
points
0.860 Loyalty Points
The rotated factor matrix enables the researcher to
identify the variables that are most strongly
correlated with the five factors.
Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)

5. Table 3 shows the final five factors which were
derived by the factor analysis. Factor one being the
most important and five the least important. The
interpretation of the factors resulted in the
following underlying constructs being found;
‘queuing times’, ‘choice’, ‘value’, ‘price
awareness’ and ‘loyalty points’. These show that
consumers are primarily aware of the financial side
of shopping as three of the factors (‘value’, ‘price
awareness’ and ‘loyalty points’) have a financial
aspect to them. Respondents felt queuing times to
be more important than loyalty points; however,
respondents were questioned on leaving the store
and the length of time they had to queue for, would
be fresh in their mind. It is therefore possibly one
of the things that frustrated them most about their
shop; if they had been interviewed an hour after
their shop the results could have been different.
Choice was also derived as an important factor but
the level of unplanned purchases in this study
shows that 24% of respondents made no unplanned
purchases. Value was the third most important
factor derived that is not unusual for “Retailer X”
as it has higher prices than its competitors and 50%
of respondents had an income between £0-19,999.
This may suggest that many respondents were
using “Retailer X” due to convenience rather than
because it was their first choice of supermarket.
The fourth most important factor derived was Price
awareness that, again, could be the result of the
sample with 50% earning less than £20,000 per
annum. Loyalty points was the fifth factor to be
derived; given that 43.8% of respondents had
loyalty cards this would appear to have some
influence over shopping behaviour. However,
when respondents were questioned, many felt that
loyalty card points were not something they
actively sought to gain.
5. Concluding Remarks
Aylott and Mitchell (1998) found grocery shopping
to be the most stressful type of shopping, in
particular they found crowding and queuing
stressful. This relates back to the results found in
this study where queuing was found to be the most
important underlying construct from the factor
analysis output. This is related to work by Iyer
(1989) who found that time pressure and store
knowledge affect unplanned purchasing. This study
also found that work by Kelly et al. (2000)
produced similar results; they found 13.5% of
respondents entered a store without a purchase
planned and the current study found that 10
respondents (9.6%) also entered the store with only
a vague idea of the products they planned to buy.
Consumers’ planned lists tend not to be exact so
they were entering store not knowing exactly what
they plan to buy and there is potential for managers
to use store atmosphere to encourage unplanned
purchases as was also noted by Tai and Fung
(1997). Other aspects to consider are the adjusting
of retail strategy based on the factor results. Given
that queues were deemed an important factor by
respondents, it may be feasible to adjust the way
queuing is done by having more checkouts serving
people with baskets or opening up tills when
queues have more than three people in them.
Another possibility is to maximise the possibility of
unplanned purchases being made by offering to
consumers a bigger availability of well-known
brands and own brands (factors 2, 4), by providing
value for money products and similar offers
(factors 3, 4) and by promoting the opportunity for
getting extra loyalty points (factor 5) via increased
purchasing.
References
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Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)

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Proceedings of the 5th WSEAS Int. Conf. on DISTANCE LEARNING AND WEB ENGINEERING, Corfu, Greece, August 23-25, 2005 (pp1-6)