Market Research Report

Ma Market Research Project Alliance University, Bangalore 2011

P r e p a r e d b y : S U N A M P A L C H A N D R A D E E P

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School of Business

MR: Marketing Research

Impact of Space layout on consumer buying

preference.

PREPARED BY:

GROUP-1

SUNAM PAL

CHANDRADEEP

BHATTACHATYA



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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



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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



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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



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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



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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



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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



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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.



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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=dHNSS3B3SlpIaVJoeXJmLWFmLURkT2c6MQ

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.

https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B3SlpIaVJoeXJmLWFmLURkT2c6MQ




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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



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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.



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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 (p

k) 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:



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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

http://en.wikipedia.org/wiki/Expected_value



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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.



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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


1. NAME

* Your Full name

2. AGE * 20-25

3. GENDER *

MALE

FEMALE





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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



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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

Strongly

Agree Agree

Neither

agree nor

disagree

Disagree Strongly

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



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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



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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



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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%



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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 Disagree

6

Disagree 4

Strongly Disagree 2

Rank-1 10

Rank-2 8

Rank-3 6

Rank-4 4

Rank-5 2



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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

Excludeda

3 10.7

Total 30 100.0

a. Listwise deletion based on all

variables in the procedure.

Reliability Statistics



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Cronbach's

Alpha

Cronbach's

Alpha

Based on

Standardize

d Items

N of

Items

.238 .284 16

Summary Item Statistics

Mean

Minimu

m

Maximu

m Range

Maximum /

Minimum

Varianc

e

N of

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

Covariances

1.310 -4.667 17.333 22.000 -3.714 12.924 16

Inter-Item

Correlations

.323 -1.000 1.000 2.000 -1.000 .386 16

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



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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



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5.3.4 XPSS OUTPUT

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .804a .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

ANOVAb

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



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Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) -3.611 9.000 -.401 .069

MULTIPLEX .030 .205 .042 .148 .088

SHOPPINGEXPERI

ENCE

-.118 .292 -.127 -.404 .069

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

ND

.151 .522 .078 .889 .037

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

IDTH

1.168 .474 1.397 .961 .003

RETAILOUTLETSP

ACE

-.990 .604 -1.225 -.639 .013

DINNINGTABLESP

ACe

.329 .405 .270 .811 .043

SECUTITYSCHECK -.361 .501 -.244 -.721 .048

EASELOCATE .179 .493 .156 .863 .022

a. Dependent Variable: IMPLAYOUT



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Charts



Page 28


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



Page 29

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:



Page 30

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, .

http://en.wikipedia.org/wiki/File:Linear_regression.png



Page 31

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.



Page 32

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



Page 33

5.4.3 Correlation Matrix XPSS OUTPUT

PARKING

SPACE

WALKA

ROUND

SPAC

ESIT

HELP

DESK

CLSO

ETOH

OME

SPACE

STORE

S

ACCELE

RATORS

WIDTH

RETAILO

UTLETSP

ACE

DINNING

TABLESP

ACe

SECUTI

TYSCH

ECK

EASELO

CATE

IMPLA

YOUT

PARKING 1.000 .239 .393* .254 .161 .353 .083 .185 .303 .143 .383 .347

. .243 .046 .251 .461 .085 .685 .366 .177 .527 .057 .066

25 24 25 20 19 24 21 21 20 19 24 24

SPACEWAL

KAROUND

.239 1.000 .327 -.025 .255 .342 -.146 -.014 .282 .286 .152 .080

.243 . .096 .906 .253 .093 .476 .944 .198 .191 .444 .668

24 26 26 21 19 25 22 22 21 21 25 25

SPACESIT .393* .327 1.000 .322 .059 .435

* .006 .112 -.177 -.058 -.071 .400

*

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

25 26 27 22 20 26 23 23 22 21 26 26

HELPDESK .254 -.025 .322 1.000 -.291 .446* .560

* .521

* -.024 .067 .136 .149

.251 .906 .120 . .222 .042 .011 .017 .918 .771 .507 .453

20 21 22 22 16 21 18 18 18 18 22 21

CLSOETOH

OME

.161 .255 .059 -.291 1.000 -.106 .113 .375 .163 .068 .168 .073

.461 .253 .781 .222 . .618 .594 .082 .455 .767 .441 .706

19 19 20 16 21 21 19 18 19 18 20 21

SPACESTOR

ES

.353 .342 .435* .446

* -.106 1.000 .432

* .362 .325 .520

* .152 .328

.085 .093 .027 .042 .618 . .032 .070 .120 .015 .435 .073

24 25 26 21 21 27 23 23 23 22 26 26

ACCELERAT

ORSWIDTH

.083 -.146 .006 .560* .113 .432

* 1.000 .821

** .319 .403 .015 .197

.685 .476 .974 .011 .594 .032 . .000 .141 .056 .937 .282

21 22 23 18 19 23 24 23 20 20 23 23

RETAILOUTL

ETSPACE

.185 -.014 .112 .521* .375 .362 .821

** 1.000 .511

* .396 .319 .131

.366 .944 .565 .017 .082 .070 .000 . .019 .067 .100 .476

21 22 23 18 18 23 23 24 19 19 23 23

DINNINGTAB

LESPACe

.303 .282 -.177 -.024 .163 .325 .319 .511* 1.000 .367 .526

* .100

.177 .198 .399 .918 .455 .120 .141 .019 . .120 .012 .605

20 21 22 18 19 23 20 19 23 19 22 23

SECUTITYS

CHECK

.143 .286 -.058 .067 .068 .520* .403 .396 .367 1.000 .301 -.096

.527 .191 .786 .771 .767 .015 .056 .067 .120 . .151 .636

19 21 21 18 18 22 20 19 19 22 22 21



Page 34

EASELOCAT

E

.383 .152 -.071 .136 .168 .152 .015 .319 .526* .301 1.000 -.022

.057 .444 .709 .507 .441 .435 .937 .100 .012 .151 . .904

24 25 26 22 20 26 23 23 22 22 27 26

IMPLAYOUT .347 .080 .400* .149 .073 .328 .197 .131 .100 -.096 -.022 1.000

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

24 25 26 21 21 26 23 23 23 21 26 27


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.



Page 35

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

Squares df

Mean

Square

Friedman's

Chi-Square Sig

Between People 50.042 2 25.021

Within

People

Between

Items

101.917a 15 6.794 20.486 .154

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

http://en.wikipedia.org/wiki/Pearson_correlation_coefficient

http://en.wikipedia.org/wiki/Normally_distributed

http://en.wikipedia.org/wiki/Probability_distribution



Page 36

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



Page 37

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

s

25 7.59b

50 8.64

75 9.65

a. Calculated from grouped data.

b. Percentiles are calculated from grouped data.



Page 38

IMPLAYOUT

Frequency Percent Valid Percent

Cumulative

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%



Page 39

5.7 Graphical percentage Analysis & frequency table.

5.7.1 XPSS OUTPUT

RESTAURANTS


Cumulative

Percent

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

Percent

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



Page 40

TRADESHOW


Cumulative

Percent

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

Percent

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

Percent

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



Page 41

HELPDESK


Cumulative

Percent

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

Percent

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

Percent

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



Page 42

SPACESTORES


Cumulative

Percent

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

Percent

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

Percent

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



Page 43


Cumulative

Percent

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

Percent

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

Percent

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



Page 44

5.7.2 Google Docs output


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%





Page 45

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%



Page 46

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%



Page 47

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%.



Page 48

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



Page 49

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



Page 50

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.



Page 51

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

A V R 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



Page 52

APPENDIX-1

RESPONSES


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%






Page 53

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%



Page 54

11. What makes you visit a shopping mall - Watch out trade shows

1 3 11%

2 5 18%

3 3 11%

4 5 18%

5 12 43%

12. How frequently you visit shopping mall

Every Day 1 4%

On weekends/Once in week 17 61%

Fortnightly 3 11%

Monthly 6 21%

Quarterly 1 4%

Yearly 0 0%

Not even once in a year 0 0%

13. With whom do you prefer going to shopping mall

FRIEND 27 96%

GIRL FRIEND/BOY FRIEND 11 39%

PARENTS 6 21%

KIDS 4 14%

BROTHERS/SISTERS 9 32%

RELATIVES 5 18%

COLLEAGUES 8 29%

SPOUSE 2 7%

ALONE 9 32%

People may select more than one checkbox, so percentages may add up to more than 100%.

14. Mark your preference to choose a cinema multiplex - Position of sit from screen

1 19 68%

2 7 25%

3 0 0%

4 1 4%

5 1 4%



Page 55

14. Mark your preference to choose a cinema multiplex - Screen Size

1

14 50%

2

8 29%

3

3 11%

4

3 11%

5

0 0%

14. Mark your preference to choose a cinema multiplex - Sound Quality

1 20 71%

2 8 29%

3 0 0%

4 0 0%

5 0 0%

14. Mark your preference to choose a cinema multiplex - Space between sits

1

12 43%

2

10 36%

3

3 11%

4

2 7%

5

1 4%

14. Mark your preference to choose a cinema multiplex - Food stalls & offering outside the

cinema

1 3 11%

2 9 32%

3 10 36%

4 2 7%

5 4 14%

14. Mark your preference to choose a cinema multiplex - Combo offers like Movie ticket +

Food

1 8 29%

2 4 14%

3 7 25%

4 4 14%

5 5 18%



Page 56

14. Mark your preference to choose a cinema multiplex - Online booking facility

1 17 61%

2 6 21%

3 2 7%

4 2 7%

5 0 0%

14. Mark your preference while shopping - Impact of Lighting & background display

1 11 39%

2 12 43%

3 4 14%

4 1 4%

5 0 0%

14. Mark your preference while shopping - Sufficient Space to walk inside stores

1 16 57%

2 11 39%

3 1 4%

4 0 0%

5 0 0%

14. Mark your preference while shopping - Sufficient Space & width of accelerators

1 10 36%

2 9 32%

3 4 14%

4 4 14%

5 1 4%

14. Mark your preference while shopping - Space between two retail stores

1

7 25%

2

10 36%

3

4 14%

4

6 21%

5

1 4%



Page 57

14. Mark your preference while shopping - Adequate space between dining tables in

restaurants

1 9 32%

2 13 46%

3 4 14%

4 0 0%

5 1 4%

14. Mark your preference while shopping - Easy of security check at entry

1

10 36%

2

11 39%

3

5 18%

4

1 4%

5

0 0%

14. Mark your preference while shopping - Easy to locate what you are looking for

1 19 68%

2 6 21%

3 1 4%

4 2 7%

5 0 0%

15. How important is space layout to you in a shopping mall

1 12 43%

2 10 36%

3 2 7%

4 1 4%

5 1 4%

6 0 0%

7 2 7%

8 0 0%

9 0 0%

10 0 0%



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20. Your confidence level while filling up the form

100% 12 43%

95-100% 11 39%

80-90% 5 18%

50-80% 0 0%

below 50% 0 0%



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References

www.wikipedia.org

www.mathstool.com

www.probabilitytool.com

www.yahoosearch.com

http://www.wikipedia.org/

http://www.mathstool.com/

http://www.probabilitytool.com/

http://www.yahoosearch.com/



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