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Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Impact of Store Layout Design on Customer Shopping
Experience: A Study of FMCG Retail Outlets in Hyderabad,
India
V V Devi Prasad Kotni
The aim of the study is to find out the impact of store layout design on customer
shopping experience. If there is any impact found, what are the store layout
design characteristics that provide good shopping experience to the
customers? What are the store layout design characteristics that are not
expected by the customers? The study identifies various characteristics of
store layout design from review of related literature. This study is organised in
an Indian city of Hyderabad and only FMCG retail outlets are considered for this
study. The primary data was collected from 300 customers who shopped in any
FMCG retail outlets in the study area. Factor analysis was performed on the
data of customer perception towards store layout design characteristics to find
out most important factors and to eliminate some characteristics which are not
important for good shopping experience according to the respondents. Finally
a regression model is adopted to find out most important store layout design
characteristics (independent variables) that influence customer shopping
experience (dependant variable).
Conference Track: Marketing / Retailing
1. Introduction
Store layout design contributes to the uniqueness of a store. The exterior and interior of a
store convey several messages about the store to the consumers. Managing space is the
first and foremost concern of almost every retailer, when it comes to designing the store's
interior. There are two areas in which the entire space of the outlet can be divided into. One
is selling area and another is non-selling area. Selling area is the area where the
merchandise of the retail outlet can be showcased with the help of fixtures like racks, tables
and others. Non-selling area is the area which is left for customer movement inside the store.
A good store layout design must find a balance between these two areas. Area/Space is
always an expensive and scarce resource. Retailers always try to maximize the return on
sales per square foot. Planning a layout for the store's interior is the first step in designing
the store's interior.
A successful store should keep a consumer interested and finally convert the window
shopper into the actual customer. From the customers’ point of view, they would like the
shopping process to be easy and satisfying. They prefer a pleasant shopping environment
where the aisles are wide, the view of the merchandise is clear, the merchandise is easy to
find and that there are sufficient items such that customers won’t experience stock-outs. The
retailer should have effective merchandising and displays in order to increase the satisfaction
of customers.
Layout design specifies the relative location of departments in a retail store. Most of the
previous research on retail management is from a strategy point of view, including situation
analysis, targeting customers, choosing a store location, managing a retail business,
developing customer service, and planning for the future. However, the design of the store
layout and the detailed facility layout haven’t received much attention.
Assistant Professor, Department of Management Studies, Gayatri Vidya Parishad College for Degree and
PG Courses (A), Rushikonda, Visakhapatnam, India. Email: devi_kvv@yahoo.com
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Based on an overall consideration of the principles and characteristics in designing a retail
area layout, this research is focuses on determining the store layout design characteristics
from customer point of view. The main difference between previous research and this
proposed research is the formulization of statistical models that can be specifically applied in
the retail sector.
2. Review of Related Literature
In the last twenty years, different aspects of retail management and customer behaviour have
been intensely studied. In general, these studies have been more qualitative in nature than
quantitative. The table 1 presents some of the important contributions that were made in past
years related to the research problem.
Table 1: Previous Studies authors and their contributions
S.No
Author(s)
Contribution
1
Stevens (1980)
Retailers have claimed that they have influenced
customer’s buying behaviour by manipulating store
atmospheric via layout, colour, lighting and music.
2
Lewison (1994)
The selling floor layout can strongly influence in-store
traffic patterns, shopping atmosphere, shopping
behaviour and operational efficiency.
3
Donovan, Rossiter,
Marcoolyn and
Nesdale (1994)
Store environment is viewed as fantasy environment
providing a range of entertainment: musical, visual and
theatrical for today’s consumer.
4
Erdem (1999)
Store image is an important factor affecting customer
behaviour.
5
Simonson (1999)
Store layout design can play a key role not only in
satisfying buyers’ requirements but also in influencing
their wants and preferences.
6
Merrilees and Miller
(2001)
Store layout design is one of the most important
determinants for store loyalty.
7
Baker (2002)
Store layout design is a critical determinant affecting the
creation of that store image.
8
Postrel (2003 )
Shopping malls are pursuing aesthetic to attract
consumer who seek an entertaining experience. Store
environment is viewed as fantasy environment providing
a range of entertainment.
9
Karolefski (2003)
Consumer wants products, communications,
entertainment, and marketing efforts that pique their
senses, evoke emotion, and stimulate their thinking—
they expect and respond best to experiences and want
shopping to be fun.
10
Raymond R. Burke
and Alex Leykin
(2007)
The retail shoppability as the ability of the retail
environment to translate consumer demand into
purchase with the various determinants for it as store
layout , navigation, product profilation and presentation,
defining the shopping attitude i.e intentions for store
entry and purchase but retailers (often mistakenly)
believe stocking more products means selling more
products.
Source: Review of Related Literature
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
The retailers must make sure your store is in a prime location and is easily accessible to the
end-users. Earlier the layout design referred to as floor plan where there were five types of
floor plans as presented in the table 2 (Source: http://managementstudyguide.com/store-
design-and-layout.htm).
Table 2: Types of Floor plans
S.No
Type of floor plan
Description
1
Straight Floor
Plan
The straight floor plan makes optimum use of the walls,
and utilizes the space in the most judicious manner. The
straight floor plan creates spaces within the retail store for
the customers to move and shop freely. It is one of the
commonly implemented store designs.
2
Diagonal Floor
Plan
According to the diagonal floor plan, the shelves or racks
are kept diagonal to each other for the owner or the store
manager to have a watch on the customers. Diagonal floor
plan works well in stores where customers have the liberty
to walk in and pick up merchandise on their own.
3
Angular Floor
Plan
The fixtures and walls are given a curved look to add to
the style of the store. Angular floor plan gives a more
sophisticated look to the store. Such layouts are often
seen in high end stores.
4
Geometric Floor
Plan
The racks and fixtures are given a geometric shape in
such a floor plan. The geometric floor plan gives a trendy
and unique look to the store.
5
Mixed Floor
Plan
The mixed floor plan takes into consideration angular,
diagonal and straight layout to give rise to the most
functional store lay out.
Source: http://managementstudyguide.com/store-design-and-layout.htm
In conventional retailing, there are several common store layouts used, including grid,
freeform, racetrack and serpentine layouts as described in the table 3. However in real life,
the retail area would combine these types of layouts rather than being restricted to using only
one type for the entire retail setting.
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Table 3: Types of Store layout designs
S.No
Type of Layout
Description
1
Grid store
layout
The grid layout is a rectangular arrangement of displays
and long aisles that generally run parallel to one another.
It has been shown that the grid layout facilitates routing
and planned shopping behaviour, providing consumers
with flexibility and speed in identifying pre-selected
products which appear on their shopping list.
2
Freeform store
layout
The freeform layout is a freeflowing and asymmetric
arrangement of displays and aisles, employing a variety of
different sizes, shapes, and styles of display. In this
pattern, the customer enjoys considerable freedom to
move in any direction within the store. The freeform layout
has been shown to increase the time that consumers are
willing to spend in the store
3
Racetrack store
layout
The selling floor is divided into individual areas along a
circle or rectangular main aisle in the middle of the store.
Each individual area or sub-area is built for a particular
shopping theme. The racetrack store layout leads the
customers along specific paths to visit as many store
sections or departments as possible because the main
aisle facilitates customers moving through the store.
4
Serpentine
layout
There are some papers focusing on serpentine, hub and
spoke layouts, which are variants of the grid store layout.
Serpentine layouts and their corresponding aisle
representation. The advantage of the serpentine layout is
that there is only one path for customers to follow that
traverses all the floor space. Profit can be maximized by
extending the shopping distance of the customer.
Source: Levy, M. and Weitz, B. A. (1998), Retail Management, 3rd edition, McGraw-Hill.
After reviewing the related literature, the following characteristics of store layout design are
identified as shown in the table 4 which are used as variables in the study. The customers
are expected to respond on a five point scale to provide their perception on store layout
design basing on their shopping experience.
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Table 4: Store Layout Design Characteristics
S.No.
Store Layout Design Characteristics
1
Store Front Design
2
Security Check cabin
3
Aesthetic Look
4
Lighting and Music
5
Space of Stair Cases
6
Accessibility to Lift Facility
7
Space for customer shopping
8
Easy Movement of customer
9
Arrangement of fixtures
10
Arrangement of Point of Sale
11
Arrangement of product Categories Departments
12
Arrangement of Wash Rooms
13
Drinking Water facility
14
Space for movement of Trolley
15
Packing and Delivery of goods
16
Store Exit
Source: Review of Related Literature
3. Objectives of the Study
The aim of the study is to determine the impact of layout design of the store on customer
shopping experience in the store. The study identifies some characteristics of retail outlet
layout design from review of related literature and attempts to find out how those
characteristics are making the customers to shop in the outlet conveniently.
4. Research Methodology
The study is descriptive in nature and an empirical one, the variables used are quantitative
and the study is based on primary data. The primary data was collected from 300 customers,
purposively selected, who shopped in different leading FMCG retail outlets in Hyderabad,
India like Big Bazaar, More, Spencer’s, Reliance Fresh, Heritage etc. The customers with
different age groups, income levels, occupations, qualifications, experience and gender are
considered for this study from the study area (profile of the study area is presented in
Appendix 1).
A structured questionnaire has been designed specifically to elicit the opinions of
respondents depending on objectives of the study. The questionnaire focuses on
demographic profile of the customers, shopping patterns of the customers and finally
measuring the customer perception on a five point likert scale towards certain aspects of
layout design of the outlet which were identified from the literature review. After assigning
appropriate coding to the questions as variables, the data was fed into statistical software
SPSS for data analysis.
The reliability test (cronbach alpha) was performed on the data collected for the study and
found to be =0.709 which indicates that the data collected for the study is most reliable. The
value was calculated for the questionnaire administrated in order to determine the reliability
of the data where the alpha value is greater than 0.70 is the recommended level: (Bernardi,
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
1994). Statistical tool factor analysis was performed on the data of perceptions of customers
towards layout design of the outlets. In order to find out the appropriateness of factor analysis
for the aspects (variables) of store layout and design, Kaiser-Meyer-Olkin (KMO) and
Bartlett's Test of Sphericity is used. KMO measures the magnitude of observed correlation
coefficients to the magnitude of partial correlation coefficients. Bartlett's Test measures the
correlation of variables. A probability of more than .05 is desirable: (Akansha Anchaliya et
al., 2012).
5. Demographic Profile of the Respondents
Demographic analysis of the study enables the retailers to know that the profile of the
customers in terms of age, gender, income, education, occupation etc. This analysis enables
the marketers and retail managers to draw the STP strategies and Retail Marketing Mix
strategies for better business performance.
Table 5: Demographic Profile of Respondents
Variable
Categories of variable
Frequency
%
Gender
Male
346
58%
Female
254
42%
Age
13 - 19 years (teenagers)
24
4%
20 - 30 years (youngagers)
384
64%
31 - 40 years (early middleage)
94
16%
41 - 50 years (late middleage)
80
13%
above 50 years (oldage)
18
3%
Occupation
Unemployed / Students
32
5%
Employed
429
72%
Business people
139
23%
Education
Primary Education
9
2%
Secondary Education
27
5%
Higher Secondary / Diploma / ITI
108
18%
Graduation (UG)
240
40%
Post Graduation (PG)
189
32%
Higher than PG
27
5%
Income
Less than Rs.15,000/-
152
25%
Between Rs.15,000/- and
Rs.30,000/-
170
28%
Between Rs.30,000/- and
Rs.50,000/-
139
23%
More than Rs.50,000/-
139
23%
Size of
Family
Two
144
24%
Three
162
27%
Four
238
40%
Five
47
8%
Six
9
2%
Source: field study
In this section an attempt has been made to analyse the demographic characteristics of
respondents as presented in table 5. Out of total 300 sample respondents, 58% are male
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
and 42% are female. The respondents are categorised into five groups basing on their age.
Out of total sample, 4% are teenagers (13 – 19 years), 64% are from young age (20 – 30
years), 16% are from early middle age (31 – 40 years), 13% belong to late middle age (41 –
50 years) and 3% are from old age (above 50 years). Based on occupation, the respondents
are categorised into three groups, unemployed/students (5%), employed (72%) and business
people (23%). Basing on the education, 2% respondents completed primary education, 5%
have secondary education, 18% completed higher secondary education, 40% are graduated,
32% have post graduation qualification and 5% are higher post graduates. Basing on the
income levels, the respondents are categorised into four groups. 25% are having monthly
income less than Rs.15,000/-, 28% have income between Rs.15,000/- and Rs.30,000/-, 23%
have income between Rs.30,000/- and Rs.50,000/-, another 23% respondents have income
more than Rs.50,000/-. The family size of respondents are also analysed, 24% have family
size two, 27% have size three, 40% are having family size four, 8% have five and 2% of
respondents are having size six.
6. Shopping Behaviour of Customers
The shopping behaviour of customers refers to the activities and actions of the customers
before shopping, during shopping and after shopping the goods in the outlets. The shopping
behaviour in terms of activities and actions like frequency of shopping, most preferred time
of shopping, amount spent per month, distance from home to outlet, family life cycle stage
etc are studied. The study of shopping behaviour of customers also enables the marketers
and retail managers to draw the STP strategies and Retail Marketing Mix strategies for better
business performance.
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Table 6: Shopping Behaviour of Respondents
Variable
Categories of variable
Frequency
%
Frequency
of
shopping
Daily
32
5%
Weekly
183
31%
Biweekly
118
20%
Monthly
132
22%
Bimonthly
42
7%
as per requirement
93
16%
Most
preferred
Time of
shopping
first week of month
251
42%
2nd week of month
138
23%
last week of month
51
9%
as per requirement
160
27%
amount
spent
per month
less than Rs.1000/-
155
26%
between Rs.1000/- to Rs.5000/-
243
41%
between Rs.5000/- to Rs.10000/-
151
25%
more than Rs.10000/-
51
9%
distance
from
home
to outlet
less than 1 k.m.
216
36%
between 1 k.m. - 3 k.m.
204
34%
between 3 k.m. - 5 k.m.
78
13%
5 k.m. - 10 k.m.
54
9%
more than 10 k.m.
48
8%
Family
Life Cycle
Stage
Young Couple with no children
135
23%
Couple with children
229
38%
Couple with working children
196
33%
Old Couple-working children with
kids
33
6%
Old Couple staying away from
children
7
1%
Source: field study
The shopping behaviour of the respondents is analysed as shown in the table 6. The
frequency of shopping observed as daily 5%, weekly 31%, biweekly 20%, monthly 22%,
bimonthly 7% and 16% of respondents are visiting the outlet as per requirement of goods.
Most preferred time for shopping is analysed as first week of the month 42%, second week
23%, last week 9% and 27% of the respondents preferred time of shopping is as per
requirement of goods. The amount spent per month for shopping by the customers is less
than Rs.1000/- for 26%, 41% of the customers are spending an amount between Rs.1000
and Rs.5000, 25% are spending an amount between Rs.5000/- to Rs.10000/- and 9% are
spending an amount more than Rs.10000/-. The distance between outlet and customer
household is analysed, 36% of the respondents are shopping in the retail outlets which are
in less than 1 k.m. radius, 34% are shopping in the outlets which are situated in the distance
is in between 1 k.m. and 3 k.m. from home, 13% are shopping in distance between 3 k.m.
and 5 k.m., 9% are shopping in distance ranging from 5 k.m. to 10 k.m. and 8% of the
respondents are shopping in the outlets which are more than 10 k.m. away from their
households. The family life cycle stages of the respondents are categorised into six groups
i.e. young-couple-without-children 23%, couple-with-children 38%, couple-with-working-
children 33%, old-couple-working-children with kids 6%, old-couple-staying-away-from-
children 1%.
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
7. Performing Factor Analysis on Store Layout Design Characteristics
In this section an attempt has been made to analyse the characteristics of store layout design
to be measured. The customers were asked to respond on a five point likert scale (Strongly-
Agree [5], Agree [4], Slightly-Agree [3], Disagree [2], Strongly-Disagree [1]) regarding sixteen
variables which were designed on the basis of previous studies and interviews. To determine
the data reliability, Reliability test was performed on the data of customer response towards
store layout and design. The value of the Cronbach's Alpha is found to be 0.889, which shows
the data of Store Layout Design Characteristics is 88.9% reliable which ensures to proceed
for further analysis.
7.1 Reliability of Data: Kaiser Meyer Olkin (KMO) and Bartlett’s Test for Store Layout
Design Characteristics
Table 7: KMO and Bartlett's Test for Store Layout Design
Characteristics
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.794
Bartlett's Test of Sphericity
Approx. Chi-Square
827.537
df
120
Sig.
.000
Source: Factor Analysis Data Reduction (SPSS 21.0)
To determine the appropriateness of factor analysis on the identified characteristics of layout
design, Kaiser Meyer Olkin (KMO) and Bartlett’s Test was performed as shown in table 7.
The KMO measure is observed to be 0.861 which is higher than the threshold value of .5
(Hair et al. 1998). So it can be interpreted that there is no error in 86.1% of the sample and
remaining 13.9% there may occur some sort of error. Bartlett's Test of Spherincity (2
=6526516) is found to be significant (p < .001, df 190). Finally it can be concluded that the
data collected on store layout design characteristics is appropriate for factor analysis.
7.2 Factors – Store Layout Design Characteristics
Table 8 : Total Variance Explained in
Factors - Store Layout Design Characteristics
Extraction Sums of Squared Loadings
FACTORS
Total
% of
Variance
Cumulative
%
EASY MOVEMENT
2.143
13.393
13.393
FIXTURES
1.737
10.859
24.252
ENTRY
1.682
10.511
34.763
FACILITIES
1.543
9.645
44.408
EXIT
1.518
9.486
53.893
Source: Factor Analysis Data Reduction (SPSS 21.0)
Factor analysis was performed to study the store layout design characteristics influencing
the shopping experience in the study area. Factor analysis was used to remove the
redundant variables from the survey data and to reduce the number of variables into a definite
number of dimensions. The application was done in SPSS 21.0. The factor analysis was
performed using principle component extraction method with varimax rotation. After
performing factor analysis, the sixteen characteristics were reduced to five factor dimensions,
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
which explained 53.893% of cumulative variance which is indicating that the variance of
original values was captured by these five factors as shown in table 8. The five factors are
provisionally named Easy Movement, Fixtures, Entry, Facilities and Exit. The factor scores
of store layout design characteristics are presented in the table 9.
7.3 Factor Scores Matrix - Store Layout Design Characteristics
Table 9: Factors loadings - Store Layout Design Characteristics
ATTRIBUTES
EASY
MOVEMENT
FIXTURES
ENTRY
FACILITIES
EXIT
I found the space of staircase in
the outlet is good and
comfortable.
.729
I feel Accessibility to Lift Facility is
good.
.673
The Space for customer shopping
inside the store and across the
racks is good
.563
There is an easy internal
movement of customer which
makes chopping comfortable.
.517
I found the Arrangement of
product Categories/Departments
in the store is good.
.757
There is a good Arrangement of
fixtures which makes all the
products on the outlet is
accessible.
.601
The Arrangement of Point of Sale
is found to be good.
.581
The Arrangement of Security
Check cabin is good.
.712
The Store Front Design is good
and motivating for purchase.
.562
The space allocated for Drinking
Water facility is good.
.797
The space allocated for
Arrangement of Washroom facility
is comfortable.
.783
There is enough space allocated
for comfortable Packing and
Delivery of goods.
.722
The Store Exit is good.
.642
There is enough Space allocated
for movement of Trolley.
.599
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Source: Factor Analysis Data Reduction (SPSS 21.0) with alignment
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
The factor scores matrix of Store Layout Design Characteristics shows the associated
variables in all the five factors and their relative factor scores as presented in table 9. The
factor scores in the factor scores matrix represent the priority of Store Layout Design
Characteristics as opined by the customer. The first factor formed is Easy Movement with an
Eigen value of 2.143, variance of 13.393% and four associated variables. The associated
variables are Space of staircases (factor score .729), Accessibility to the lift facility (.673),
Space for customer shopping (.563) and Easy movement of the customer (.517). The second
factor formed is Fixtures with an Eigen value of 1.737, variance of 10.859% and three
associated variables. The associated variables are Arrangement of product
categories/departments (.757), Arrangement of fixtures (.601) and Arrangement of point of
sale (.581). The third factor formed is Entry with an Eigen value of 1.682, variance of 10.511%
and two associated variables. The associated variables are Security check cabin (.712) and
Store front design (.562). The fourth factor formed is Facilities with an Eigen value of 1.543,
variance of 9.645% and two associated variables. The associated variables are Drinking
water facilities (.797) and Arrangement of wash rooms (.783). The fifth factor formed is Exit
with an Eigen value of 1.518, variance of 9.486% and three associated variables. The
associated variables are Packing and Delivery of goods counter (.722), Store Exit (.642) and
Space for movement of trolley (.599). Two variables (store layout design characteristics,
namely aesthetic look and lighting/music are eliminated while performing factor analysis with
statistical package SPSS.
8. Finding Customer Shopping Experience Index
In this section an attempt has been made to identify overall customer perception towards
shopping experience in FMCG retail outlets with three variables. Factor analysis was
performed on the customer shopping experience which was recorded on a five point likert
scale (i.e. Highly Satisfied [5], Satisfied [4], Slightly Satisfied [3], Dissatisfied [2] and Highly
Dissatisfied [1]). To determine the data reliability, reliability test was performed on the data
of customer perception on shopping experience data. The value of the Cronbach's Alpha is
found to be 0.789, which shows the data of shopping experience is 78.9% reliable which
ensures to proceed for further analysis.
To determine the appropriateness of factor analysis for the customer shopping experience,
Kaiser Meyer Olkin (KMO) and Bartlett’s Test was performed. As shown in table 10, the KMO
measure is observed to be 0.540 which is higher than the threshold value of .5 (Hair et al.
1998). So it can be interpreted that there is no error in 54% of the sample and remaining 46%
there may occur some sort of error. Bartlett's Test of Spherincity (2 =8.993) is found to be
significant (p < .001, df 3). Finally it can be concluded that the data of customer perception
on shopping experience is appropriate for factor analysis. After performing factor analysis,
as shown in table 11, three variables are formed into only one factor, named after customer
experience index is formed with an Eigen value of 1.578, variance of 60.25% and three
associated variables convenient design (.691), rate of motivation (.602) and flackable look
(.601).
Table 10 : KMO and Bartlett's Test for
Customer Shopping Experience
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.540
Bartlett's Test of Sphericity
Approx. Chi-Square
8.993
Df
3
Sig.
.000
Source: Factor Analysis Data Reduction (SPSS 21.0)
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Table 11 : Total Variance Explained for Customer Shopping
Experience
Extraction Sums of Squared Loadings
Factors
Total Variance
% of Variance
Cumulative %
CUSTOMER
SHOPPING INDEX
1.202
40.068
40.068
Source: Factor Analysis Data Reduction (SPSS 21.0)
Table 12: Factors loadings - Customer Shopping
Experience
ATTRIBUTES
Experience
The store is designed conveniently so that
is attracts the customers for shopping.
.691
The Rate of motivation for shopping
provided to me is good.
.602
The store has Flackable look and feel that
provides good shopping experience.
.601
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Source: Factor Analysis Data Reduction (SPSS 21.0)
8. Model Specification and Discussion
In this section an attempt has been made to analyse the associations among customer
perception towards shopping experience and store layout design characteristics. To know
the impact of store layout design on customer shopping experience, the factors formed from
store layout design characteristics (from table 8) and factor formed from customer shopping
experience (from table 11) are used for this analysis. Considering customer shopping
experience index as dependent variable and the formed five factors (from store layout design
characteristics) as independent variables, a multiple regression model is proposed as
follows.
Customer Satisfaction Index (ICS) = f (Easy Movement, Fixtures, Entry, Facilities, Exit) [1]
Table 13: Model Summary
Model
R
R
Square
Adjusted
R
Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F
Change
df1
df2
Sig. F
Change
1
.374
.140
.125
.9352602
.140
9.565
5
294
.000
Source: Regression Analysis (SPSS 21.0)
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Table 14: ANOVAa Table
Model
Sum of
Squares
df
Mean
Square
F
Sig.
1
Regression
41.835
5
8.367
9.565
.000b
Residual
257.165
294
.875
Total
299.000
299
a. Dependent Variable: SATISFACTION
b. Predictors: (Constant), EXIT, FACILITIES, ENTRY, FIXUTRES,
EASY_MOVEMENT
Source: Regression Analysis (SPSS 21.0)
The proposed model is statistically significant at 0.000 level with an f value 9.565 and degrees
of freedom 5 as shown in the table 13, R is found to be .374 and R2 is found to be .140 and
adjusted R2 found to be .125. Hence it can be concluded that all the independent variables
are able to explain only 12.5% in dependent variable as shown in table 13.
Customer Shopping Experience Index (ICS) = 1.109E-16 + 0.189 Easy Movement + 0.220
Fixtures + 0.118 Entry + 0.042 Facilities + 0.200 Exit) [2]
Table 15: Coefficients – Store Layout Design Characteristics
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std.
Error
Beta
1
(Constant)
1.109E-16
.054
.000
1.000
Easy Movement
.189
.054
.189
3.501
.001**
Fixtures
.220
.054
.220
4.067
.000*
Entry
.118
.054
.118
2.183
.030
Facilities
.042
.054
.042
.784
.434
Exit
.200
.054
.200
3.694
.000*
* Significant
Source: Regression analysis (SPSS 21.0)
From table 15, it can be observed that customer shopping experience will be increased by
18.9%, if there is easy movement in the store layout design. The associated
variables/characteristics with the factor (Easy Movement) are spacious staircases,
accessibility to lift facility, space for customer shopping, easy internal movement of
customers.
Customer shopping experience will be increased by 22%, if there are good fixtures in the
store layout design. The associated variables/characteristics with the factor (Fixture) are
Arrangement of product categories/departments, good arrangement of fixtures, and
arrangement of point of sales.
Customer shopping experience will be increased by 20%, if there is comfortable exit in the
store layout design. The associated variables/characteristics with the factor (Exit) are Space
for packing and delivery of goods, store exit and movement of trolley.
Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Out of five factors of store layout design characteristics, only three factors are found to be
significant i.e. Fixtures, Exit and Easy Movement. Remaining two characteristics Entry and
Facilities are not found to be statistically significant. It can be concluded that if the level of
good store layout design is increasing, the level customer shopping experience is also
increasing. It can be observed from the regression analysis model that the customers are
more motivated by easy movement in the store, good fixtures and comfortable exit.
Limitations and Further Scope of the Study
The present study is limited to only one city i.e. Hyderabad but it can be extended to other
cities in India and other countries also. The research topic is confined to only store layout
design, but it can be combined with other related topics like visual merchandising. The current
research is concentrated only on impact of store layout design on shopping experience, but
impact on customer satisfaction, impact on sales, impact on customer retention must also be
found out.
References
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Proceedings of Annual Australian Business and Social Science Research Conference
26 - 27 September 2016, Crowne Plaza Hotel, Gold Coast, Queensland, Australia
ISBN: 978-1-925488-17-3
Appendix 1
Profile of the Study Area: Hyderabad
Hyderabad is the capital city of the southern Indian state of Telangana. Occupying 650
square kilometres (250 sq mi) on the banks of the Musi River, it is also the largest city in the
state. Historically, Hyderabad was known for its pearl and diamond trading centres.
Industrialisation brought major Indian Manufacturing, R&D, and Financial Institutions to the
city, such as the Bharat Heavy Electricals Limited, the Defence Research and Development
Organisation, the Centre for Cellular and Molecular Biology and the National Mineral
Development Corporation. The formation of an Information Technology (IT) Special
Economic Zone (SEZ) by the state agencies attracted global and Indian companies to set up
operations in the city. The emergence of Pharmaceutical and Biotechnology industries during
the 1990s earned it the titles of "India's pharmaceutical capital" and the "Genome Valley of
India". The Telugu film industry is based in Hyderabad. As of 2011, the Hyderabad Urban
Agglomeration has a population of 7,749,334, making it the sixth most populous urban
agglomeration in the country. There are 3,500,802 male and 3,309,168 female citizens—a
sex ratio of 945 females per 1000 males, higher than the national average of 926 per 1000.
Among children aged 0–6 years, 373,794 are boys and 352,022 are girls—a ratio of 942 per
1000. Literacy stands at 82.96% (male 85.96%; female 79.79%), higher than the national
average of 74.04%.
