Layout Impact Consumer Buying

Ma Market Research Project Alliance University, Bangalore 2011

School of Business

MR: Marketing Research

Impact of Space layout on consumer buying
preference.
PREPARED BY:

GROUP-1

SUNAM PAL
CHANDRADEEP
BHATTACHATYA
Prepared by : SUNAM PAL CHANDRADEEP Page 1


Table of Contents

1.Introduction .............................................................................................................................. 5
2.Literature Review .................................................................................................................. 5
2.1 Solution Approaches to Automated Space Planning ......................................... 5
2.2 Additive Space Allocation ............................................................................................. 7
2.3 Structure of the program .............................................................................................. 7
2.4 A typical floor plan ........................................................................................................... 7
2.5 Floor plan graph with dual graph .............................................................................. 8
3 Methodology ............................................................................................................................. 9
3. 1 Overview of Work ........................................................................................................... 9
3.2 Sources of data................................................................................................................... 9
3.3 Sample design:- ................................................................................................................. 9
3.4 Sample size ....................................................................................................................... 10
3.5 Target Group ................................................................................................................... 10
3.6 Data collection:- ............................................................................................................. 10
3.7 Type of Research:-......................................................................................................... 10
3.8 Statistical tool used .................................................................................................... 10
4.Questionnaire........................................................................................................................ 15
5.ANALYISIS ................................................................................................................................ 21
5.1 Tools used ......................................................................................................................... 21
5.2 ASSIGNING VALUES TO EACH RATINGS & RANKS ......................................... 21
5.2 Reliability Test ............................................................................................................ 22
5.2.1 XPSS output ............................................................................................................. 22
5.2.2 Interpretation ......................................................................................................... 23



5.3 Linear Regression Analysis ......................................................................... 23
5.3.1 Independent Variable .......................................................................... 24
5.3.2 Dependent variable............................................................................... 24
5.3.3 Sample Size ............................................................................................... 24
5.3.4 XPSS OUTPUT .......................................................................................... 25
5.3.6 Interpretation.......................................................................................... 28
5.3.6.1 R square value ..................................................................................... 28
5.3.6.2 T-test ....................................................................................................... 28
5.3.6.3 Significance Level ............................................................................... 28
5.3.6.4 B value & C Value ............................................................................... 28
5.3.6.5 Linear Equations ................................................................................ 28
5.4 Correlation Analysis ...................................................................................... 29
5.4.1 Correlation Variable ............................................................................. 32
5.4.2 Sample Size ............................................................................................... 32
5.4.3 Correlation Matrix XPSS OUTPUT .................................................. 33
5.4.4 Interpretation.......................................................................................... 34
5.5 Kendal’s W-Test ............................................................................................... 34
5.5.1 Variable ...................................................................................................... 35
5.5.2 Sample Size ............................................................................................... 35
5.5.3 XPSS OUTPUT .......................................................................................... 35
5.5.4 Interpretation.......................................................................................... 35
5.6 Central Tendencies......................................................................................... 36
Mean ....................................................................................................................... 36
Median ................................................................................................................... 36
Mode ....................................................................................................................... 36
Range...................................................................................................................... 36



5.6.1 Variable ..................................................................................................................... 37
5.6.2 Sample Size .............................................................................................................. 37
5.6.3 XPSS OUTPUT ......................................................................................................... 37
5.6.4 Interpretation ......................................................................................................... 38
5.7 Graphical percentage Analysis & frequency table............................... 39
5.7.1 XPSS OUTPUT .......................................................................................... 39
5.7.2 Google Docs output ............................................................................... 44
AGE .......................................................................................................................... 44
GENDER ................................................................................................................ 44
PLACE..................................................................................................................... 44
Shopping center/Retail Malls have visited ............................................ 45
Favorite Mall ....................................................................................................... 46
MARITAL STATUS............................................................................................. 47
Cinema multiplex have you visited ........................................................... 47
6. FINDINGS ................................................................................................................................. 48
7. LEARNING OUTCOME………………………….……………………………………………40
8. Conclusion .............................................................................................................................. 50
APPENDIX-1................................................................................................................................ 51
APPENDIX-1................................................................................................................................ 50
References ................................................................................................................................... 59



1.Introduction
Now a days shopping mall attract lots of customer. Sales frequency has
increased in shopping mall recently. How does consumer make purchase
decision shopping mall? How does purchase in one shopping mall differ from
other shopping mall? So we consider two important aspect of consumer
decision making shopping mall; one is space and other is design .How does
space and design consumer decision making process in shopping mall, we
conducted a research on this matter and try to find out related finding
regarding this topic. For our research we choose selected shopping mall in
Bangalore and tried to find out consumer decision making relative to those
shopping mall.

2.Literature Review

2.1 Solution Approaches to Automated Space Planning

Kalay (2004) categorizes computational design synthesis methods as:

 Procedural Methods
 Heuristic Methods
 Evolutionary Methods

In this categorization, “Procedural Methods” are introduced as first methods
to be employed. They leverage our ability, as human designers, to specify local
conditions and the ability of the computer to apply or test for these
relationships over much larger sets of variables. The basic procedural
approach is the attempt to completely enumerate all the possible
arrangements of floor plans from a given set of rooms. Then, architects can
choose the most appropriate one from those alternatives for a given design
project. However, the numbers of possible solutions rise up dramatically by
increasing the number of design parameters. Therefore, it is an inefficient
approach for computers to try to calculate all the possible solutions. Even if a


computer can generate a large number of possible solutions, no architect has
sufficient time and energy to review all those solutions (Kalay, 2004). Another
procedural approach to computerize arranging rooms in a floor plan is to
enlist the services of the computer in the layout of spaces in a building
according to some rational principles (mostly minimization of distances
between spaces that ought to be close to each other). This approach is known
as “Space Allocation”. The uses of space allocation approaches however are
limited to building types that the main important factor in their design is
distances (like schools, hospitals and warehouses) (Kalay, 2004). Attempts to
improve space allocation with the help of procedural methods continued by
including additional design criteria (e.g., lighting, privacy and orientation) in
the decision-making process of placement algorithm. Different “Constraint
Satisfaction” methods then introduced to include multiple objectives in space
allocation. With some exceptions the results of space allocations with
constraint satisfaction methods were poor compared to the results obtained
by competent architects. In fact, satisfying more constraints with some sort of
satisfying results needs the more heuristic methods of simulation (Kalay,
2004). “Heuristic Methods” are the computational design methods that are
inspired by analogies, just like the design synthesis methods that are typically
inspired by analogies and guided by the architect‟s own or another designer’s
previous experiences. These methods rely on personal and professional
expertise accumulated over lifetimes of confronting a variety of design issues
(Kalay, 2004).

One of the interesting approaches to computerized space layout planning by
means of Heuristic Methods was to borrow the idea of simulating space
arrangements in layouts from the rules that has derived from other sciences.
These methods are known as Final Paper……..…….……………………………...Arch
588- Research Practice 3



2.2 Additive Space Allocation
An example of a program that has implemented additive methods of space
allocation is GRAMPA (for Graph Manipulating Package). Final
Paper……..…….……………………………...Arch 588- Research Practice 4

2.3 Structure of the program
(Grason, 1971)

Grason‟s approach to computerized space planning is based on the methods
of solution for the formal class of floor plan design problems. The methods of
solution depend on a special linear graph representation for floor plans called
the „dual graph‟1 representation.

1 In mathematics, a dual graph of a given planar graph G has a vertex for each
plane region of G, and an edge for each edge joining two neighboring regions.
The term "dual" is used because this property is symmetric, meaning that if G
is a dual of H, then H is a dual of G; in effect, these graphs come in pairs.

As shown in Figure 1 a “space” is defined to be either a room or one of the four
outside spaces. A problem statement will consist of a set of adjacency and
physical dimension requirements that have to be satisfied, and a problem
solution is a floor plan that satisfies all of the design requirements.

2.4 A typical floor plan
(Grason, 1971)

In applying graph theory to floor plan layout, rooms are pictured as labeled
nodes possessing certain attributes, such as intended use, area, and shape.
Adjacencies between rooms are indicated by drawing lines (edges) connecting
the nodes to the corresponding rooms. These notions can be implemented by
dealing with the dual graph of a floor plan Final
Paper……..…….……………………………...Arch 588- Research Practice 5



which is itself treated as a linear graph. An example of such a floor plan graph
is shown in Figure 2, with black nodes. In the floor plan graph, “edges” and
“nodes” will be called “wall segments” and “corners” respectively. A special
dual of the floor plan graph can be obtained by placing a node inside each
space and constructing edges to join the nodes of adjacent spaces. This special
type of dual graph of the floor plan is the design representation to be used for
the class of problems described in this paper. The general idea of its
application is to first set down the four nodes and four edges of the dual graph
that represent the four outside walls of a building. Then nodes and edges are
added one by one to the dual graph in response to design requirements and
other considerations until a completed dual graph is obtained.

2.5 Floor plan graph with dual graph
(Grason, 1971)

The incomplete dual graphs that are produced in the intermediate stages of
this design process present special problems. Since edges can be colored,
directed, and weighted, it is not always clear whether or not there exists at
least one physically realizable floor plan satisfying the relationships expressed
in the incomplete dual graph. To treat this problem, appropriate properties of
the dual graph representation have been developed and are presented in
Grason‟s paper. These include the definitions of “Planarity”, “Well-Formed
Nodes”, “Well-Formed Terminal Regions” and “The Turn Concept”. Based on
these properties three theorems on physical realizability are established.
Final Paper……..…….……………………………...Arch 588- Research Practice 6

The use of these theorems enables the program to configure whether the
graph is planer or not. It also makes it possible to generate various possible
geometric realizations of the dual graph. A geometric realization of a planar
graph is simply one of the possibly many ways in which it can be drawn in a
plane. Four different realizations of a particular planar graph are shown in
Figure 3.



3 Methodology

3. 1 Overview of Work

So far the task accomplished is is identifying, filtering and filling up
questionnaire from respondents which are suitable for the research. The
applicants are filtered based on age groups and if they belonged to Bangalore.
A broad database was gathered which consists of a pool of applicant who may
or may not fall in the target bracket.

Amongst these, the potential ones are selected, met and kept track of. The
whole idea is to collect as many prospects as possible and then filter them as
per the requirements.

Source:
https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B3Sl
pIaVJoeXJmLWFmLURkT2c6MQ

3.2 Sources of data

Primary data:

We mainly collected primary data by taking survey among Alliance student.
On the basis of questionnaire we get our primary data.

3.3 Sample design:-

The sample design used for the purpose of the research is convenient non-
probability sampling. Population is totally unknown we are just taking sample
for our research .

The sample design used for the purpose of the research was applicants within
Bangalore only. It basically comprised of all corporate from manufacturing &
IT sector that fill the questionnaire and were ready to give their valuable
feedback.



3.4 Sample size

We took 30 samples for our research.

3.5 Target Group

1. Group: Students of Alliance and IT professionals
2. Location: Bangalore
3. Age: 20-30

3.6 Data collection:-
 Primary data such as name, occupation, gender of the applicant was
collected through questionnaire.
 Data were mainly collected through online.
 Google docs were used to collect data.
 The questionnaire had no open ended questions.

3.7 Type of Research:-

Causative: Relation between Space layout design and various factors
Quantitative: Use of statistical tools
Non-probability: Population size unknown

3.8 Statistical tool used

 Reliability Test
 Regression
 Correlation
 Kendal’s W-test
 Central Tendencies
 Mean
 Median
 Mode
 Standard Deviation
 Variance



 Ranges
 Skewness
 Kurtosis

 Simple percentage analysis
 Graphical Analyis
 Frequency table
 Pi-charts

NORMAL PROBABILITY DISTRIBUTION
In probability theory, the normal (or Gaussian) distribution, is a continuous probability
distribution that is often used as a first approximation to describe real-valued random variables
that tend to cluster around a single mean value. The graph of the associated probability density
function is “bell”-shaped, and is known as the Gaussian function or bell curve:

Where parameter μ is the mean (location of the peak) and σ 2 is the variance (the measure of the
width of the distribution). The distribution with μ = 0 and σ 2 = 1 is called the standard normal.

BINOMIAL PROBABILITY DISTRIBUTION
probability theory and statistics, the binomial distribution is the discrete
probability distribution of the number of successes in a sequence of n
independent yes/no experiments, each of which yields success with
probability p.


Such a success/failure experiment is also called a Bernoulli experiment or
Bernoulli trial. In fact, when n = 1, the binomial distribution is a Bernoulli
distribution. The binomial distribution is the basis for the popular binomial
test of statistical significance

Probability mass function

In general, if the random variable K follows the binomial distribution with parameters n and p,
we write K ~ B(n, p). The probability of getting exactly k successes in n trials is given by the
probability mass function:

For k = 0, 1, 2, ..., n, where

is the binomial coefficient (hence the name of the distribution) "n choose k", also denoted
C(n, k), nCk, or nCk. The formula can be understood as follows: we want k successes (pk) and
n − k failures (1 − p)n − k. However, the k successes can occur anywhere among the n trials, and
there are C(n, k) different ways of distributing k successes in a sequence of n trials.

In creating reference tables for binomial distribution probability, usually the table is filled in up
to n/2 values. This is because for k > n/2, the probability can be calculated by its complement as

So, one must look to a different k and a different p (the binomial is not symmetrical in general).
However, its behavior is not arbitrary. There is always an integer m that satisfies

As a function of k, the expression ƒ(k; n, p) is monotone increasing for k < m and monotone
decreasing for k > m, with the exception of one case where (n + 1)p is an integer. In this case,
there are two maximum values for m = (n + 1)p and m − 1. m is known as the most probable
(most likely) outcome of Bernoulli trials. Note that the probability of it occurring can be fairly
small.

The cumulative distribution function can be expressed as:



where is the "floor" under x, i.e. the greatest integer less than or equal to x.

It can also be represented in terms of the regularized incomplete beta function, as follows:

For k ≤ np, upper bounds for the lower tail of the distribution function can be derived. In
particular, Hoeffding's inequality yields the bound

and Chernoff's inequality can be used to derive the bound

Moreover, these bounds are reasonably tight when p = 1/2, since the following expression holds
for all k ≥ 3n/8

ean and variance
If X ~ B(n, p) (that is, X is a binomially distributed random variable), then the expected value of
X is

and the variance is



This fact is easily proven as follows. Suppose first that we have a single Bernoulli trial. There are
two possible outcomes: 1 and 0, the first occurring with probability p and the second having
probability 1 − p. The expected value in this trial will be equal to μ = 1 · p + 0 · (1−p) = p. The
variance in this trial is calculated similarly: σ2 = (1−p)2·p + (0−p)2·(1−p) = p(1 − p).

The generic binomial distribution is a sum of n independent Bernoulli trials. The mean and the
variance of such distributions are equal to the sums of means and variances of each individual
trial:

Mode and median

Usually the mode of a binomial B(n, p) distribution is equal to ⌊(n + 1)p⌋, where ⌊ ⌋ is the floor
function. However when (n + 1)p is an integer and p is neither 0 nor 1, then the distribution has
two modes: (n + 1)p and (n + 1)p − 1. When p is equal to 0 or 1, the mode will be 0 and n
correspondingly. These cases can be summarized as follows:

In general, there is no single formula to find the median for a binomial distribution, and it may
even be non-unique. However several special results have been established:

 If np is an integer, then the mean, median, and mode coincide.
 Any median m must lie within the interval ⌊np⌋ ≤ m ≤ ⌈np⌉.
 A median m cannot lie too far away from the mean: |m − np| ≤ min{ ln 2,
max{p, 1 − p} }.
 The median is unique and equal to m = round(np) in cases when either p
≤ 1 − ln 2 or p ≥ ln 2 or |m − np| ≤ min{p, 1 − p} (except for the case
when p = ½ and n is odd)
 When p = 1/2 and n is odd, any number m in the interval
½(n − 1) ≤ m ≤ ½(n + 1) is a median of the binomial distribution. If
p = 1/2 and n is even, then m = n/2 is the unique median.



Covariance between two binomials
If two binomially distributed random variables X and Y are observed together, estimating their
covariance can be useful. Using the definition of covariance, in the case n = 1 we have

The first term is non-zero only when both X and Y are one, and μX and μY are equal to the two
probabilities. Defining pB as the probability of both happening at the same time, this gives

and for n such trials again due to independence

If X and Y are the same variable, this reduces to the variance formula given above.

4. Questionnaire

Source:
https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B
3SlpIaVJoeXJmLWFmLURkT2c6MQ

1. NAME

* Your Full name

20-25
2. AGE *

3. GENDER *
MALE
FEMALE



4. PLACE * Place you are living currently

If you are in Bangalore, For how many years you have been staying

5. Which shopping center/Retail Malls you have visited you can choose more than one
option
FORUM MALL
GARUDA MALL
CENTRAL
GOPALAN
MANTRI
ROYAL MEENAKSHI MALL
SHOPPERS STOP
BIG BAZAAR
FOOD BAZAAR
RELIANCE MART
RELIANCE FRESH
TOTAL MALL
Other:

5.A Your Favorite Mall *

5.B MARITAL STATUS
MARRIED
UNMARRIED

5.C You stay with



6.Which cinema multiplex have you visited
PVR
INOX
VISION
CINEPOLIS
FUN CINEMAS
GOPALAN CINEMAS
Other:

7. Name the multiplex in Forum Mall

8. TOTAL MALL has its center in Bangalore at

9. CHOOSE THE ODD ONE

RELIANCE FRESH
FOOD WORLD
FOOD BAZAAR
BIG BAZAAR

10. You would prefer a shopping mall because

Neither
Strongly Strongly
Agree agree nor Disagree
Agree Disagree
disagree

It has space for
parking

It has sufficient space
to walk & roam
around

It has place to sit

Service help desk is
available

Close to your home



11. What makes you visit a shopping mall 1- Very frequently & 5- very rarely?

1 2 3 4 5

Cinema Multiplex

Shopping Experience

Have food in
restaurant

Hang around with
friends

Watch out trade
shows

12. How frequently you visit shopping mall

13. With whom do you prefer going to shopping mall
FRIEND
GIRL FRIEND/BOY FRIEND
PARENTS
KIDS
BROTHERS/SISTERS
RELATIVES
COLLEAGUES
SPOUSE
ALONE



14. Mark your preference to choose a cinema multiplex
1-High Preference 5-Low Preference

1 2 3 4 5

Position of sit from
screen

Screen Size

Sound Quality

Space between sits

Food stalls & offering
outside the cinema

Combo offers like
Movie ticket + Food

Online booking
facility

14. Mark your preference while shopping
1-High Preference 2-Low Preference

1 2 3 4 5

Impact of Lighting &
background display

Sufficient Space to
walk inside stores

Sufficient Space &
width of accelerators

Space between two
retail stores

Adequate space
between dining tables
in restaurants



1 2 3 4 5

Easy of security
check at entry

Easy to locate what
you are looking for

15. How important is space layout to you in a shopping mall
Rate on a scale of 1 - 10 (1- very Important, 10-Least Important)

1 2 3 4 5 6 7 8 9 10

16. Mark your preference

1 2 3 4 5 6 7 8 9 10

Service level Display layout


1 2 3 4 5 6 7 8 9 10

Space layout Display layout


1 2 3 4 5 6 7 8 9 10

Gopalan Cinemas Cinepolis Cinemas

20. Your confidence level while filling up the form
100%
95-100%
80-90%
50-80%
below 50%



5.ANALYISIS
5.1 Tools used

 Reliability Test
 Regression
 Correlation
 Kendal’s W Test
 Central Tendencies
 Perecentage & Graphical Analysis

5.2 ASSIGNING VALUES TO EACH RATINGS & RANKS

RATING VALUE ATTACHED
Strongly Agree 10
Agree 8
Neither Agree nor
6
Disagree
Disagree 4
Strongly Disagree 2
Rank-1 10
Rank-2 8
Rank-3 6
Rank-4 4
Rank-5 2



5.2 Reliability Test
You learned in the Theory of Reliability that it's not possible to calculate
reliability exactly. Instead, we have to estimate reliability, and this is always
an imperfect endeavor. Here, I want to introduce the major reliability
estimators and talk about their strengths and weaknesses.

There are four general classes of reliability estimates, each of which estimates
reliability in a different way. They are:

 Inter-Rater or Inter-Observer Reliability
Used to assess the degree to which different raters/observers give
consistent estimates of the same phenomenon.
 Test-Retest Reliability
Used to assess the consistency of a measure from one time to another.
 Parallel-Forms Reliability
Used to assess the consistency of the results of two tests constructed in
the same way from the same content domain.
 Internal Consistency Reliability
Used to assess the consistency of results across items within a test.

5.2.1 XPSS output

Case Processing Summary
N %
Cases Valid 27 89.3
Excluded 3 10.7
a

Total 30 100.0
a. Listwise deletion based on all
variables in the procedure.

Reliability Statistics



Cronbach's
Alpha
Based on
Cronbach's Standardize N of
Alpha d Items Items
.238 .284 16

Summary Item Statistics
Minimu Maximu Maximum / Varianc N of
Mean m m Range Minimum e Items
Item Means 7.958 3.667 9.333 5.667 2.545 2.265 16
Item Variances 5.375 1.000 17.333 16.333 17.333 37.583 16
Inter-Item 1.310 -4.667 17.333 22.000 -3.714 12.924 16
Covariances
Inter-Item .323 -1.000 1.000 2.000 -1.000 .386 16
Correlations

5.2.2 Interpretation

 Around 27 observations are valid.
 Around 3 observations has to be excluded.
 Cronbach’s alpha is 0.284 which is <0.5 and close to zero shows that the
data are significant.
 Reliability = 89.3% ( > 50%)

5.3 Linear Regression Analysis

In statistics, regression analysis includes any techniques for modeling and
analyzing several variables, when the focus is on the relationship between a
dependent variable and one or more independent variables. More specifically,
regression analysis helps one understand how the typical value of the
dependent variable changes when any one of the independent variables is
varied, while the other independent variables are held fixed. Most commonly,
regression analysis estimates the conditional expectation of the dependent
variable given the independent variables — that is, the average value of the



dependent variable when the independent variables are held fixed. Less
commonly, the focus is on a quantile, or other location parameter of the
conditional distribution of the dependent variable given the independent
variables. In all cases, the estimation target is a function of the independent
variables called the regression function. In regression analysis, it is also of
interest to characterize the variation of the dependent variable around the
regression function, which can be described by a probability distribution.

5.3.1 Independent Variable

 Presence of Multiplex ( X1)
 Shopping experience ( X2)
 Hanging around with friends ( X3 )
 Trade show (X4)
 Parking ( X5)
 Space to walk around ( X6 )
 Space to sit ( X7)
 Help Desk service ( X8)
 Closeness to home ( X9)
 Lighting ( X10)
 Space between stores ( X11)
 Width of accelerators ( X12)
 Space inside retail outlets ( X13)
 Dining table space ( X14 )
 Ease of security check ( X15 )
 Easy to locate products ( X16 )

5.3.2 Dependent variable
 Importance of Space layout Design ( Y )

5.3.3 Sample Size
30 samples



5.3.4 XPSS OUTPUT

Model Summaryb
Std. Error of the
Model R R Square Adjusted R Square Estimate
a
1 .804 .747 .746 1.858

a. Predictors: (Constant), EASELOCATE, CLSOETOHOME,
ACCELERATORSWIDTH, SPACESIT, TRADESHOW, SPACEWALKAROUND,
DINNINGTABLESPACe, MULTIPLEX, HANGAROUND, SHOPPINGEXPERIENCE,
RESTAURANTS, PARKING, SECUTITYSCHECK, HELPDESK, SPACESTORES,
LIGHTING, RETAILOUTLETSPACE

b. Dependent Variable: IMPLAYOUT

b
ANOVA

Model Sum of Squares df Mean Square F Sig.
1 Regression 63.122 17 3.713 1.076 .046
Residual 34.508 10 3.451
Total 97.630 27
a. Predictors: (Constant), EASELOCATE, CLSOETOHOME, ACCELERATORSWIDTH, SPACESIT, TRADESHOW,
SPACEWALKAROUND, DINNINGTABLESPACe, MULTIPLEX, HANGAROUND, SHOPPINGEXPERIENCE,
RESTAURANTS, PARKING, SECUTITYSCHECK, HELPDESK, SPACESTORES, LIGHTING, RETAILOUTLETSPACE
b. Dependent Variable: IMPLAYOUT

R$esiduals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 4.51 11.08 8.30 1.501 29
Residual -2.084 2.040 .000 1.110 29
Std. Predicted Value -2.475 1.823 .000 .982 29
Std. Residual -1.122 1.098 .000 .598 29
a. Dependent Variable: IMPLAYOUT



a
Coefficients

Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) -3.611 9.000 -.401 .069
MULTIPLEX .030 .205 .042 .148 .088
SHOPPINGEXPERI -.118 .292 -.127 -.404 .069
ENCE
RESTAURANTS .215 .273 .276 .278 .045
HANGAROUND -.219 .343 -.187 -.163 .057
TRADESHOW .063 .199 .077 .314 .076
PARKING .028 .644 .014 .743 .026
SPACEWALKAROU .151 .522 .078 .889 .037
ND
SPACESIT .465 .496 .330 .936 .037
HELPDESK -.354 .411 -.280 -.862 .040
CLSOETOHOME .351 .277 .389 .267 .023
LIGHTING .212 .557 .146 .380 .071
SPACESTORES .240 .670 .124 .659 .002
ACCELERATORSW 1.168 .474 1.397 .961 .003
IDTH
RETAILOUTLETSP -.990 .604 -1.225 -.639 .013
ACE
DINNINGTABLESP .329 .405 .270 .811 .043
ACe
SECUTITYSCHECK -.361 .501 -.244 -.721 .048
EASELOCATE .179 .493 .156 .863 .022
a. Dependent Variable: IMPLAYOUT



Charts




5.3.6.1 R square value

R Square value = 0.746

It shows that the relationship is 74.6% accurate to define the existing
relationship between Y & X[1,2,3…..16].

5.3.6.2 T-test

The following independent variable had t-value > 0.5.

X5, X6, X7, X8, X9,X11, X12, X13, X14,X15 & X16

Which say that they have a greater impact on the output and forms a strong
relation with it.

5.3.6.3 Significance Level

Out of above X5 > 0.05, hence it is not significant

5.3.6.4 B value & C Value

Slopes X9,X13,X15 -> they are negatively related

Slopes X6,X7,X8,X11,X12,X14,X16- > they are Positively related

Constant -> It is negative

5.3.6.5 Linear Equations

Y = F(X) + C

C = -3.611

F(X) = 0.028 X6 + 0.151 X7 + 0.465 X8 -0.354 X9 + 0.24 X11 + 1.116 X12 -
1.99 X13 + 0.329 X14 -0.361 X15+0.179 X16



5.4 Correlation Analysis

A correlation function is the correlation between random variables at two
different points in space or time, usually as a function of the spatial or
temporal distance between the points. Correlation functions of different
random variables are sometimes called cross correlation functions to
emphasize that different variables are being considered and because they are
made up of cross correlations.

Correlation functions are a useful indicator of dependencies as a function of
distance in time or space, and they can be used to assess the distance required
between sample points for the values to be effectively uncorrelated. In
addition, they can form the basis of rules for interpolating values at points for
which there are observations.

For random variables X(s) and X(t) at different points s and t of some space,
the correlation function is

where is described in the article on correlation. In this definition, it has
been assumed that the stochastic variable is scalar-valued. If it is not, then
more complicated correlation functions can be defined. For example, if one
has a vector Xi(s), then one can define the matrix of correlation functions

Regression Analysis

In linear regression, the model specification is that the dependent variable, yi
is a linear combination of the parameters (but need not be linear in the
independent variables). For example, in simple linear regression for modeling
n data points there is one independent variable: xi, and two parameters, β0 and
β1 :

straight line:



In multiple linear regression, there are several independent variables or
functions of independent variables. For example, adding a term in xi2 to the
preceding regression gives:

parabola:

This is still linear regression; although the expression on the right hand side is
quadratic in the independent variable xi, it is linear in the parameters β0, β1
and β2.In both cases, is an error term and the subscript i indexes a particular
observation. Given a random sample from the population, we estimate the
population parameters and obtain the sample linear regression model:

The residual, , is the difference between the value of the
dependent variable predicted by the model, and the true value of the
dependent variable yi. One method of estimation is ordinary least squares.
This method obtains parameter estimates that minimize the sum of squared
residuals, SSE:

Minimization of this function results in a set of normal equations, a set of
simultaneous linear equations in the parameters, which are solved to yield the
parameter estimators, .



Illustration of linear regression on a data set.

In the case of simple regression, the formulas for the least squares estimates
are

where is the mean (average) of the x values and is the mean of the y values.
See simple linear regression for a derivation of these formulas and a
numerical example. Under the assumption that the population error term has
a constant variance, the estimate of that variance is given by:

This is called the mean square error (MSE) of the regression. The standard
errors of the parameter estimates are given by

Under the further assumption that the population error term is normally
distributed, the researcher can use these estimated standard errors to create
confidence intervals and conduct hypothesis tests about the population
parameters.



Correlation

The population correlation coefficient ρX,Y between two random variables X
and Y with expect values μX and μY and standard deviations σX and σY is
defined as:

where E is the expected value operator, cov means covariance, and, corr a
widely used alternative notation for Pearson's correlation.

The Pearson correlation is defined only if both of the standard deviations are
finite and both of them are nonzero. It is a corollary of the Cauchy–Schwarz
inequality that the correlation cannot exceed 1 in absolute value. The
correlation coefficient is symmetric: corr(X,Y) = corr(Y,X).

5.4.1 Correlation Variable
Only those variable that are a part of regression equations are taken into
account

 Parking ( X5)
 Space to walk around ( X6 )
 Space to sit ( X7)
 Help Desk service ( X8)
 Closeness to home ( X9)
 Space between stores ( X11)
 Width of accelerators ( X12)
 Space inside retail outlets ( X13)
 Dining table space ( X14 )
 Ease of security check ( X15 )
 Easy to locate products ( X16 )

5.4.2 Sample Size
30 samples



5.4.3 Correlation Matrix XPSS OUTPUT

SPACE CLSO SPACE ACCELE RETAILO DINNING SECUTI
WALKA SPAC HELP ETOH STORE RATORS UTLETSP TABLESP TYSCH EASELO IMPLA
PARKING ROUND ESIT DESK OME S WIDTH ACE ACe ECK CATE YOUT
*
PARKING 1.000 .239 .393 .254 .161 .353 .083 .185 .303 .143 .383

. .243 .046 .251 .461 .085 .685 .366 .177 .527 .057

25 24 25 20 19 24 21 21 20 19 24

SPACEWAL .239 1.000 .327 -.025 .255 .342 -.146 -.014 .282 .286 .152
KAROUND .243 . .096 .906 .253 .093 .476 .944 .198 .191 .444

24 26 26 21 19 25 22 22 21 21 25
* *
SPACESIT .393 .327 1.000 .322 .059 .435 .006 .112 -.177 -.058 -.071

.046 .096 . .120 .781 .027 .974 .565 .399 .786 .709

25 26 27 22 20 26 23 23 22 21 26
* * *
HELPDESK .254 -.025 .322 1.000 -.291 .446 .560 .521 -.024 .067 .136

.251 .906 .120 . .222 .042 .011 .017 .918 .771 .507

20 21 22 22 16 21 18 18 18 18 22

CLSOETOH .161 .255 .059 -.291 1.000 -.106 .113 .375 .163 .068 .168
OME .461 .253 .781 .222 . .618 .594 .082 .455 .767 .441

19 19 20 16 21 21 19 18 19 18 20
* * * *
SPACESTOR .353 .342 .435 .446 -.106 1.000 .432 .362 .325 .520 .152
ES .085 .093 .027 .042 .618 . .032 .070 .120 .015 .435

24 25 26 21 21 27 23 23 23 22 26
* * **
ACCELERAT .083 -.146 .006 .560 .113 .432 1.000 .821 .319 .403 .015
ORSWIDTH .685 .476 .974 .011 .594 .032 . .000 .141 .056 .937

21 22 23 18 19 23 24 23 20 20 23
* ** *
RETAILOUTL .185 -.014 .112 .521 .375 .362 .821 1.000 .511 .396 .319
ETSPACE .366 .944 .565 .017 .082 .070 .000 . .019 .067 .100

21 22 23 18 18 23 23 24 19 19 23
* *
DINNINGTAB .303 .282 -.177 -.024 .163 .325 .319 .511 1.000 .367 .526
LESPACe .177 .198 .399 .918 .455 .120 .141 .019 . .120 .012

20 21 22 18 19 23 20 19 23 19 22
*
SECUTITYS .143 .286 -.058 .067 .068 .520 .403 .396 .367 1.000 .301
CHECK .527 .191 .786 .771 .767 .015 .056 .067 .120 . .151
19 21 21 18 18 22 20 19 19 22 22



*
EASELOCAT .383 .152 -.071 .136 .168 .152 .015 .319 .526 .301 1.000
E .057 .444 .709 .507 .441 .435 .937 .100 .012 .151 .

24 25 26 22 20 26 23 23 22 22 27
*
IMPLAYOUT .347 .080 .400 .149 .073 .328 .197 .131 .100 -.096 -.022

.066 .668 .027 .453 .706 .073 .282 .476 .605 .636 .904

24 25 26 21 21 26 23 23 23 21 26


The following parameters were strongly correlated with correlation
coefficient value above R > 0.50 and significance value < 0.06

 Parking & retail space are positively correlated
 Parking & space to walk are positively correlated
 Space to walk & space to sit are positively correlated
 Closesness to home & service are positively correlated
 Service & store space are positively correlated
 Retail space & dinning space are positively correlated
 Retail space & accelerator width are positively correlated

5.5 Kendal’s W-Test

Kendall's W (also known as Kendall's coefficient of concordance) is a non-
parametric statistics. It is a normalization of the statistic of the Friedman test,
and can be used for assessing agreement among raters. Kendall's W ranges
from 0 (no agreement) to 1 (complete agreement).

Suppose, for instance, that a number of people have been asked to rank a list
of political concerns, from most important to least important. Kendall's W can
be calculated from these data. If the test statistic W is 1, then all the survey
respondents have been unanimous, and each respondent has assigned the
same order to the list of concerns. If W is 0, then there is no overall trend of
agreement among the respondents, and their responses may be regarded as
essentially random. Intermediate values of W indicate a greater or lesser
degree of unanimity among the various responses.



While tests using the standard Pearson correlation coefficient assume
normally distributed values and compare two sequences of outcomes at a
time, Kendall's W makes no assumptions regarding the nature of the
probability distribution and can handle any number of distinct outcomes.

5.5.1 Variable


5.5.2 Sample Size
30 samples

5.5.3 XPSS OUTPUT
ANOVA with Friedman's Test
Sum of Mean Friedman's
Squares df Square Chi-Square Sig
Between People 50.042 2 25.021
Within Between 101.917a 15 6.794 20.486 .154
People Items
Residual 121.958 30 4.065
Total 223.875 45 4.975
Total 273.917 47 5.828

Grand Mean = 7.96

Kendall's coefficient of concordance W = .772.

The grand weighted mean is 7.96, which states that average scores rated to
importance of space layout design is 7.9.

However the kendals’ W test, say that 77.2% of ranking provided by
respondents are inclined to each other & is jutify enough to satisfy the
relationship as it is geater than 0.5.

W>0.5



5.6 Central Tendencies
The terms mean, median, mode, and range describe properties of statistical
distributions. In statistics, a distribution is the set of all possible values for
terms that represent defined events. The value of a term, when expressed as a
variable, is called a random variable.

Mean
The most common expression for the mean of a statistical distribution with a
discrete random variable is the mathematical average of all the terms. To
calculate it, add up the values of all the terms and then divide by the number
of terms. This expression is also called the arithmetic mean. There are other
expressions for the mean of a finite set of terms but these forms are rarely
used in statistics.

Median
The median of a distribution with a discrete random variable depends on
whether the number of terms in the distribution is even or odd. If the number
of terms is odd, then the median is the value of the term in the middle. This is
the value such that the number of terms having values greater than or equal to
it is the same as the number of terms having values less than or equal to it.

Mode
The mode of a distribution with a discrete random variable is the value of the
term that occurs the most often. It is not uncommon for a distribution with a
discrete random variable to have more than one mode, especially if there are
not many terms. This happens when two or more terms occur with equal
frequency, and more often than any of the others. A distribution with two
modes is called bimodal.

Range
The range of a distribution with a discrete random variable is the difference
between the maximum value and the minimum value. For a distribution with
a continuous random variable, the range is the difference between the two
extreme points on the distribution curve, where the value of the function falls



to zero. For any value outside the range of a distribution, the value of the
function is equal to 0

5.6.1 Variable


5.6.2 Sample Size
30 samples

5.6.3 XPSS OUTPUT
Statistics
IMPLAYOUT
N Valid 27
Missing 2
Mean 8.30
Std. Error of Mean .373
Median 8.64a
Mode 8
Std. Deviation 1.938
Variance 3.755
Skewness -1.653
Std. Error of Skewness .448
Kurtosis 2.709
Std. Error of Kurtosis .872
Range 7
Minimum 3
Maximum 10
Sum 224
Percentile 25 7.59b
s 50 8.64
75 9.65
a. Calculated from grouped data.
b. Percentiles are calculated from grouped data.



IMPLAYOUT

Cumulative
Frequency Percent Valid Percent Percent

Valid 3 2 6.9 7.4 7.4

5 1 3.4 3.7 11.1

7 1 3.4 3.7 14.8

8 10 34.5 37.0 51.9

9 4 13.8 14.8 66.7

10 9 31.0 33.3 100.0

Total 27 93.1 100.0
Missing 6 1 3.4
System 1 3.4
Total 2 6.9
Total 29 100.0

 The average rating score is is 8.3 on an 1-10 scale.
 People have rated ‘8’ for maximum times with frquency of 10.
 50% of observation lies below 8.6 and 50% lies above it.
 25% of observation lies below 7.6, 25% between 7.6 to 8.6, 25% between
8.6 to 9.65 & rest 25% between 9.6 to 10
 The expected deviation can be expected to be 1.9 from mean.
 The range of rating is 7.
 The maximum rating has been 10, where as minimum rating has been 3.
 Skewness of mean from median is 0.44.
 37% of sample people have rated 8
 Lease rating rating were given as 5 & 7 that is around just 3.7%



5.7 Graphical percentage Analysis & frequency table.
5.7.1 XPSS OUTPUT
RESTAURANTS

Cumulative

Valid 2 2 6.9 9.5 9.5

4 7 24.1 33.3 42.9

8 7 24.1 33.3 76.2

10 5 17.2 23.8 100.0

Total 21 72.4 100.0
Missing 6 7 24.1
System 1 3.4
Total 8 27.6
Total 29 100.0

HANGAROUND

Cumulative

Valid 4 2 6.9 8.3 8.3

8 8 27.6 33.3 41.7

10 14 48.3 58.3 100.0

Total 24 82.8 100.0
Missing 6 4 13.8
System 1 3.4
Total 5 17.2
Total 29 100.0



TRADESHOW

Cumulative

Valid 1 3 10.3 12.0 12.0

2 12 41.4 48.0 60.0

4 5 17.2 20.0 80.0

8 5 17.2 20.0 100.0

Total 25 86.2 100.0
Missing 6 3 10.3
System 1 3.4
Total 4 13.8
Total 29 100.0

SPACEWALKAROUND

Cumulative

Valid 8 12 41.4 46.2 46.2

10 14 48.3 53.8 100.0

Total 26 89.7 100.0
Missing 6 2 6.9
System 1 3.4
Total 3 10.3
Total 29 100.0

SPACESIT

Cumulative

Valid 4 1 3.4 3.7 3.7

8 14 48.3 51.9 55.6

10 12 41.4 44.4 100.0

Total 27 93.1 100.0
Missing 6 1 3.4
System 1 3.4
Total 2 6.9
Total 29 100.0



HELPDESK

Cumulative

Valid 4 2 6.9 9.1 9.1

8 12 41.4 54.5 63.6

10 8 27.6 36.4 100.0

Total 22 75.9 100.0
Missing 6 6 20.7
System 1 3.4
Total 7 24.1
Total 29 100.0

CLSOETOHOME

Cumulative

Valid 4 5 17.2 23.8 23.8

8 6 20.7 28.6 52.4

10 10 34.5 47.6 100.0

Total 21 72.4 100.0
Missing 6 7 24.1
System 1 3.4
Total 8 27.6
Total 29 100.0

LIGHTING

Cumulative

Valid 4 1 3.4 4.2 4.2

8 12 41.4 50.0 54.2

10 11 37.9 45.8 100.0

Total 24 82.8 100.0
Missing 6 4 13.8
System 1 3.4
Total 5 17.2
Total 29 100.0



SPACESTORES

Cumulative

Valid 8 11 37.9 40.7 40.7

10 16 55.2 59.3 100.0

Total 27 93.1 100.0
Missing 6 1 3.4
System 1 3.4
Total 2 6.9
Total 29 100.0

DINNINGTABLESPACe

Cumulative

Valid 2 1 3.4 4.3 4.3

8 13 44.8 56.5 60.9

10 9 31.0 39.1 100.0

Total 23 79.3 100.0
Missing 6 5 17.2
System 1 3.4
Total 6 20.7
Total 29 100.0

SECUTITYSCHECK

Cumulative

Valid 4 1 3.4 4.5 4.5

8 11 37.9 50.0 54.5

10 10 34.5 45.5 100.0

Total 22 75.9 100.0
Missing 6 6 20.7
System 1 3.4
Total 7 24.1
Total 29 100.0

RETAILOUTLETSPACE



Cumulative

Valid 2 1 3.4 4.2 4.2

4 6 20.7 25.0 29.2

8 10 34.5 41.7 70.8

10 7 24.1 29.2 100.0

Total 24 82.8 100.0
Missing 6 4 13.8
System 1 3.4
Total 5 17.2
Total 29 100.0

ACCELERATORSWIDTH

Cumulative

Valid 2 1 3.4 4.2 4.2

4 4 13.8 16.7 20.8

8 9 31.0 37.5 58.3

10 10 34.5 41.7 100.0

Total 24 82.8 100.0
Missing 6 4 13.8
System 1 3.4
Total 5 17.2
Total 29 100.0

EASELOCATE

Cumulative

Valid 4 2 6.9 7.4 7.4

8 6 20.7 22.2 29.6

10 19 65.5 70.4 100.0

Total 27 93.1 100.0
Missing 6 1 3.4
System 1 3.4
Total 2 6.9
Total 29 100.0



5.7.2 Google Docs output

Source:

AGE
Below 18 0%
18-20 0%
20-25 86%
25-30 14%
above 30 0 0%

GENDER

MALE 79%
FEMALE 21%

PLACE

BANGALORE 82%
Not Bangalore 18%



Shopping center/Retail Malls have visited

Shopping Mall frequency %

FORUM MALL 25 89%
GARUDA MALL 25 89%
CENTRAL 22 79%
GOPALAN 17 61%
MANTRI 17 61%
ROYAL MEENAKSHI MALL 12 43%
SHOPPERS STOP 24 86%
BIG BAZAAR 25 89%
FOOD BAZAAR 17 61%
RELIANCE MART 14 50%
RELIANCE FRESH 20 71%
TOTAL MALL 22 79%
Other 5 18%



Favorite Mall


OTHER 1 4%
GOPALAN 0 0%
GARUDA 5 18%
MEENAKSHI 2 7%
SHOPPERS STOP 2 7%
CENTRAL 7 25%
MANTRI 2 7%
TOTAL 0 0%
FORUM MALL 9 32%



MARITAL STATUS

MARRIED 1 4%
UNMARRIED 24 86%

Cinema multiplex have you visited


PVR 24 86%
INOX 19 68%
VISION 11 39%
CINEPOLIS 13 46%
FUN CINEMAS 12 43%
GOPALAN CINEMAS 12 43%
Other 4 14%
People may select more than one checkbox, so percentages may add up to more than 100%.



CHOOSE THE ODD ONE

RELIANCE FRESH 3 11%
FOOD WORLD 5 18%
FOOD BAZAAR 1 4%
BIG BAZAAR 19 68%

6.FINDINGS

Reliability = 89.3% ( > 50%)

R Square value = 0.746

Linear Equations

Y = F(X) + C

C = -3.611

F(X) = 0.028 X6 + 0.151 X7 + 0.465 X8 -0.354 X9 + 0.24 X11 + 1.116 X12 -
1.99 X13 + 0.329 X14 -0.361 X15+0.179 X16

 Parking & retail space are positively correlated
 Parking & space to walk are positively correlated
 Space to walk & space to sit are positively correlated
 Closesness to home & service are positively correlated
 Service & store space are positively correlated
 Retail space & dinning space are positively correlated
 Retail space & accelerator width are positively correlated



 The average rating score is is 8.3 on an 1-10 scale.
 People have rated ‘8’ for maximum times with frquency of 10.
 50% of observation lies below 8.6 and 50% lies above it.
 25% of observation lies below 7.6, 25% between 7.6 to 8.6, 25% between
8.6 to 9.65 & rest 25% between 9.6 to 10
 The expected deviation can be expected to be 1.9 from mean.
 The range of rating is 7.
 The maximum rating has been 10, where as minimum rating has been 3.
 Skewness of mean from median is 0.44.
 Lease rating rating were given as 5 & 7 that is around just 3.7%

The grand weighted mean is 7.96, which states that average scores rated to
importance of space layout design is 7.9.

However the kendals’ W test, say that 77.2% of ranking provided by
respondents are inclined to each other & is jutify enough to satisfy the
relationship as it is geater than 0.5.

7.Learning Outcome

 How space layout is related to buying behaviour
 Varius factors related to space layout
 Relationship between space layout design and various other factors
 Concordance in ratings
 Corelation between various factors
 Reliability of respondents
 Descrptive statistics



8.Conclusion

Space layout design is an important parameter that enhances buying
behaviour inside a retail mall. Parking space,retail outlets space,dinning
space,width of accelerators.closeness to home add value to customer
percieved value and thus enhances buying behaviour.



APPENDIX-1

LIST OF RESPONDENTS

1. NAME 2. AGE 3. GENDER 4. PLACE
SUNAM PAL 20-25 MALE BANGALORE
hemant kumar 20-25 MALE Not Bangalore
Ghulam 20-25 MALE BANGALORE
BHAVYA
JANARDHAN 20-25 FEMALE BANGALORE
aditya narayan
patra 20-25 MALE Not Bangalore
AVR
PHANIKRISHNA
M 20-25 MALE BANGALORE
Rohan Prasad 20-25 MALE Not Bangalore
saumya shukla 20-25 FEMALE BANGALORE
RITUPARNA
DUTTA 20-25 FEMALE BANGALORE
saswat kumar 20-25 MALE Not Bangalore
DILIP KUMAT 25-30 MALE BANGALORE
sandeep almiya 20-25 MALE BANGALORE
Ritesh Kumar
Agrawal 20-25 MALE BANGALORE
Debjan
Bhowmik 20-25 MALE BANGALORE
C. Bhattacharya 20-25 MALE BANGALORE
Nitesh Tripathi 20-25 MALE Not Bangalore
ANIRBAN
KAUSHIK 25-30 MALE BANGALORE
Vinyith Sisinty 20-25 MALE BANGALORE
Ujjawal Kumar 20-25 MALE BANGALORE
Rishabh Jain 20-25 MALE BANGALORE
Akshay Modi 25-30 MALE BANGALORE
Anupriya Verma 20-25 FEMALE BANGALORE
PUSHPANJALI
KUMARI 20-25 FEMALE BANGALORE
IRFAN HABIB 25-30 MALE BANGALORE
Subodh 20-25 MALE BANGALORE
Vishal Janendra 20-25 MALE BANGALORE
Kiran Jacob 20-25 MALE BANGALORE
pavithra 20-25 FEMALE BANGALORE
Anshuman 20-25 MALE BANGALORE



APPENDIX-1

RESPONSES

Source:

10. You would prefer a shopping mall because - It has space for parking
Strongly Agree 10 36%
Agree 15 54%
Neither agree nor disagree 3 11%
Disagree 0 0%
Strongly Disagree 0 0%

10. You would prefer a shopping mall because - It has sufficient space to walk & roam
around
Agree 11 39%
Disagree 0 0%

10. You would prefer a shopping mall because - It has place to sit
Agree 14 50%
Disagree 1 4%

10. You would prefer a shopping mall because - Service help desk is available
Agree 12 43%
Disagree 2 7%



10. You would prefer a shopping mall because - Close to your home
Agree 6 21%
Disagree 5 18%

11. What makes you visit a shopping mall - Cinema Multiplex
1 19 68%
2 3 11%
3 2 7%
4 1 4%
5 3 11%

11. What makes you visit a shopping mall - Shopping Experience
1 5 18%
2 13 46%
3 5 18%
4 4 14%
5 1 4%

11. What makes you visit a shopping mall - Have food in restaurant
1 5 18%
2 7 25%
3 7 25%
4 7 25%
5 2 7%

11. What makes you visit a shopping mall - Hang around with friends
1 14 50%
2 8 29%
3 4 14%
4 2 7%
5 0 0%


Layout Impact Consumer Buying

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