Transcript
Page 1: Research Methodology Project

We the Urban People:

An empirical Analysis of Cities

SUBMITTED BY –GAURAV BHATI

ROLL NO. – 13101B.M.S 2 B

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SHAHEED SUKHDEV COLLEGE OF BUSINESS STUDIES

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Content1. Acknowledgement………………………………………………………………………….……………….….032. Introduction………………………………………………………………………………….………………......043. Research Methodology…………………………………………………………………….……………......054. Research Objective……………………….……………………………………………….……………….……065. Questionnaire…..……………….……………….……………………………………………………….……..076. Data……………………………………………………………………………………….……………………………107. Observation From The table……………..……………………………………..……………….…………148. Exploratory Data Analysis.........................................................................................159. Descriptive analysis………………………………………………………………………………………..…..1810.Factor Analysis………………………………………………………………………………………………..….2111.Conclusion………………………………………………………………………………………………………….2612.Reference……………………………………………………………………………………….…………….…….27

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AcknowledgementThis research paper is made possible through the help and support from everyone, including: parents, teachers, family, friends, and in essence, all sentient beings. Especially, please allow me to dedicate my acknowledgment of gratitude toward the following significant advisors and contributors:

First and foremost, I would like to thank my teacher ‘DR. MONA VERMA’ for her exemplary guidance, monitoring and constant encouragement throughout the course of this project. I am extremely grateful to her for providing me such a great project and guidance though she had busy schedule.

Second, I would like to thank our principal ‘DR. POONAM VERMA’ who gave

me the golden opportunity to do this wonderful project on the topic – ‘We the Urban People: An empirical Analysis of Cities’.

Finally, I sincerely thank to my parents, family and friends who helped me a lot in finishing this project within the limited time.

The product of this research paper would not be possible without all of them.

I am really thankful to them.

NAME - GAURAV BHATI

CLASS - B.M.S. 2BROLL NO – 13101

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INTRODUCTION“It’s plain that the health of cities drives the wellbeing of businesses,

people and nations.”

What makes a city thrive?This is one thing that fascinates many of us. A lot many people individuals, societies and organizations, institutions - government and private are performing studies on the same. This becomes more important from the point of view that more than half of our population resides in cities and by 2050 it will reach approx 70% of the total.

This project is a small effort from me to carry out study on the various factors that are at play behind a vibrant and resurgent city.

Although this study was not carried out through rigorous methods and there is scope for the facts not being perfect, as the sample for the project is very small. But we can expect to bring some light on the topic.

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Research MethodologyThis research is descriptive in nature as this tries to indicate the existing symptoms of the present situation instead of trying to establish the causality of relationship.

A small questionnaire was filled by 25 respondents, about their perceptions that where the 10 cities in our survey stands on a 10 point scale on 10 different parameters.

The analysis was done through descriptive, inferential, exploratory, and factor analysis methods. Different tools have been used to analyse the results ranging from graphical to various arithmetical tools.

Questionnaire was filled using Google Docs whereas Analysis was done through Ms Excel & SPSS (software by IBM).

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Research ObjectiveThis research aims to achieve following objectives:

1) To find out and compare the 10 cities on various performance parameters;2) To find out how the two Indian cities Delhi and Mumbai performs in comparison to

other foreign cities in question;3) To compare Delhi and Mumbai on with each other;4) To find out which are the parameters most respondents are more bullish and

bearish about;5) To observe how different respondents perceive the cities overall, based on the

scores they provided.6) To compare the economic and demographic profile of the cities.7) To find out the co-relation in the parameters, if any.

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The Questionnaire

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DataOnly some important extracts of data are presented here:

Table 1: How different nations performed on various parameters

Parameters New York London Hong

KongSing apore Sydney Dubai Tokyo Beijing Mumbai Delhi Total

Ease of Doing Business

199 189 188 195 172 190 191 182 178 172 1856

Economic Clout 211 200 205 194 195 189 204 204 181 186 1969Entrepreneurial Environment

227 221 201 196 204 197 209 184 191 188 2018

Sustainability and Natural Environment

190202

180202

195 192 195 182 172 170 1880

Political Environment

212 208 189 198 183 180 196 191 178 173 1908

Cultural Vibrancy 216 209 197 203 188 194 194 194 211 216 2022Literacy and Enrolment

231 223 206 213 212 195 207 200 170 174 2031

Technological Readiness and Digital Knowhow

227 227210 221 217 217 225 218 188 189 2139

Health, Safety and Security 214 222 205 215 214 209 208 213 195 177 2072

Transportation and Infrastructure

232235

220 221 205 219 218 209 184 193 2136

Total 2159 2136 2001 2058 1985 1982 2047 1977 1848 1838 20031

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Table 2: How different parameters scored -

Name

Ease of Doing Business

Economic Clout

Entrepreneurial Environment

Sustainability & Natural Environment

Political Environment

Cultural Vibrancy

Literacy & Enrolment

Technological Readiness and Digital Knowhow

Health, Safety & Security

Transportation & Infrastructure

Total

A 71 76 83 80 86 83 83 85 77 81 805B 82 84 90 78 76 77 85 93 89 94 848C 80 85 88 92 92 92 92 96 96 64 877D 87 90 88 81 73 74 86 86 78 87 830E 76 76 67 68 79 82 86 91 90 85 800F 78 84 94 88 80 90 95 91 89 96 885G 79 70 80 70 82 76 75 80 86 82 780H 51 68 77 50 61 74 84 80 67 93 705I 100 100 100 100 92 100 100 100 100 100 992J 53 69 67 50 69 70 78 81 70 81 688K 100 100 90 90 84 89 90 86 84 97 910L 86 93 91 93 87 96 97 98 92 95 928M 55 55 55 55 55 55 58 55 55 55 553N 91 95 90 72 81 79 82 82 79 88 839O 83 86 82 83 84 91 80 88 84 86 847P 92 81 76 77 69 73 90 92 89 77 816Q 57 68 80 73 70 76 87 85 83 83 762R 75 81 76 78 73 86 82 86 84 83 804S 55 62 82 66 62 70 68 90 77 82 714T 55 55 55 55 55 55 55 54 55 55 549U 93 92 93 91 95 93 94 96 90 94 931V 79 87 89 51 75 86 90 91 100 100 848W 85 88 84 89 83 77 86 93 91 98 874X 15 36 55 55 52 86 21 71 79 89 559Y 78 88 86 95 93 92 87 89 88 91 887

Total185

6 1969 20181880

1908202

2 20312139 207

22136 200

31

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Table 3: Cumulative Scores

Table 3(a) : Parameters (score of 2,500)

Parameters TotalEase of Doing Business 1856

Economic Clout 1969Entrepreneurial Environment 2018Sustainability and Natural Environment 1880

Political Environment 1908Cultural Vibrancy 2022Literacy and Enrolment 2031

Technological Readiness and Digital Knowhow 2139Health, Safety and Security 2072Transportation and Infrastructure 2136

Total 20031

Table 3(b): Countries (score of 2,500)

Rank Countries Total1 New York 21592 London 21363 Singapore 20584 Tokyo 20475 Hong Kong 20016 Sydney 19857 Dubai 19828 Beijing 19779 Mumbai 184810 Delhi 1838

Total 20031

Table 3(c): Respondents (total score they gave of 1000)

Name TotalA 805B 848C 877D 830E 800F 885

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G 780H 705I 992

J 688K 910L 928M 553N 839O 847P 816Q 762R 804S 714

T 549

U 931V 848W 874X 559Y 887

Total 20031

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Observations from Table1. New York and London emerges as clear winners leading in 7 and 4 parameters

respectively.2. Singapore (202) leads with London (202) in terms of sustainability and natural

environment.Delhi (216) and New York (216) have good repo with respondents in terms of cultural vibrancy. Mumbai (211) is not far behind.

3. New York (227) and London (227) are head to head in terms of technological readiness and digital knowhow.

4. These 10 cities perform best in terms of technological readiness & digital know how (2139) and transport & Infrastructure (2136).

5. But they perform badly in terms of ease of doing business (1856) and sustainability and natural environment (1880).

6. Overall New York (2159) leads the list, with London (2136) not much behind. Singapore (2058) is at 3rd.

7. Mumbai (1848) and Delhi (1838) needs to work more on their policies as they are far behind their competitors.

8. Respondents have different perceptions about the cities; their scoring varies from 55% (549/1000) to 99% (992/1000).

Most importantly, 9. Ranking of cities also depends upon their national economy. As the cities which

belong to developed nations are on the top of chart.Or one may say,Performance of cities is directly proportional to performance of their nations.

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Exploratory Data Analysis1) How different Indicators of Performed

Respondents are more bullish to technological readiness & digital know how (2139) and transport & Infrastructure (2136).But they are bearish about ease of doing business (1856) and sustainability and natural environment (1880).

170018001900200021002200

18561969 2018

1880 19082022 2031

21392072

2136

Series1

2) How the nations score on Different parameters collectively:Overall New York (2159) leads the list, with London (2136) not much behind. Singapore (2058) is at 3rd. Mumbai (1848) and Delhi (1838) needs to work more on their policies as they are far behind their competitors.

New Yo

rk

London

Hong Kong

Singa

pore

Sydney

DubaiTo

kyoBeij

ing

MumbaiDelh

i1600

1700

1800

1900

2000

2100

2200 21592136

2001

2058

1985 1982

2047

1977

1848 1838

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3) How respondents perceives the performance of these 10 cities overall:Respondents vary in their perception as it is clear from the below scatter diagram that there is huge difference between the scoring provided by them.Scores ranges from 992 to 549.

0 5 10 15 20 25 300

200

400

600

800

1000

1200

805848877830800885

780705

992

688

910928

553

839847816762804

714

549

931848874

559

887

TotalLinear (Total)

Respondent

Scor

e

4) Comparison between Delhi and Mumbai: Both are head to head, although Mumbai has some advantage in terms of Health, Safety and Security.

Ease o

f Doing B

usiness

Economic C

lout

Enter

preneu

rial E

nviorn

ment

Susta

inibility a

nd Natural

Envio

rnmen

t

Political

Envio

rnment

Cultural

Vibrancy

Literac

y and En

rollmen

t

Technologic

al Rea

diness an

d Digital

Knowhow

Health, Sa

fety a

nd Secu

rity

Transporta

tion and In

frastr

ucture

160

170

180

190

200

210

220

178 181

191

172178

211

170

188195

184

172

186 188

170 173

216

174

189

177

193

MumbaiDelhi

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5) How Delhi and Mumbai scores compare to rest of the world:The two Indian cities are far behind the average of the rest of the world, in almost every parameter.Although, they performs well in terms of cultural vibrancy.Table 4:

Parameters Rest of the world (avg.) Mumbai and Delhi (avg.)Ease of Doing Business 188.25 175Economic Clout 200.25 183.5Entrepreneurial Environment 204.875 189.5Sustainability and Natural Environment 192.25 171Political Environment 194.625 175.5Cultural Vibrancy 199.375 213.5Literacy and Enrolment 210.875 172Technological Readiness and Digital Knowhow 220.25 188.5Health, Safety and Security 212.5 186Transportation and Infrastructure 219.875 188.5Total 2043.125 1843

160

170

180

190

200

210

220

230

188.25

200.25204.875

192.25 194.625199.375

210.875220.25

212.5219.875

175183.5

189.5

171175.5

213.5

172

188.5 186 188.5

Rest of the world (avg.)

Mumbai and Delhi (avg.)

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Descriptive Analysis1) What is the mean score of different cities?

S. No Countries Mean Std. Dev1 New York 86.36 13.8760382 London 85.44 14.253263 Hong Kong 80.44 11.836384584 Singapore 82.32 10.716555 Sydney 79.4 14.5856 Dubai 79.28 12.70877 Tokyo 81.88 11.0368 Beijing 79.08 13.15769 Mumbai 73.92 12.1545610 Delhi 73.52 13.9825

0 1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

Mean Scores of Cities

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2) How respondents perceive cities on different parameters?

Parameters Total Mean Std. Dev

Ease of Doing Business 1856 74.249.27601447

Economic Clout 196978.76

9.550450833

Entrepreneurial Environment 201880.72

13.91082712

Sustainability and Natural Environment 188075.2

11.49879221

Political Environment 190876.32

12.84782731

Cultural Vibrancy 202280.88

10.19585973

Literacy and Enrolment 203181.24

19.41620171

Technological Readiness and Digital Knowhow 2139

85.5614.3716542

Health, Safety and Security 207282.88

12.80451309

Transportation and Infrastructure 213685.44

16.11210725

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Ease o

f Doing B

usiness

Economic C

lout

Enter

preneu

rial E

nviorn

ment

Susta

inibility a

nd Natural

Envio

rnmen

t

Political

Envio

rnment

Cultural

Vibrancy

Literac

y and En

rollmen

t

Technologic

al Rea

diness an

d Digital

Knowhow

Health, Sa

fety a

nd Secu

rity

Transporta

tion and In

frastr

ucture

68

72

76

80

84

88

74.24

78.7680.72

75.2 76.32

80.88 81.24

85.5682.88

85.44

Mean score of various parameters

Mean

3) How these cities perform in terms of Demographic Profile and Economic Profile?

Economic Profile = Ease of Doing Business + Economic Clout + Entrepreneurial Environment + Political Environment + Transportation and Infrastructure.

Demographic Profile = Sustainability & Natural Environment + Cultural Vibrancy + Literacy and Enrolment + Technical Readiness & Digital Know how + Health, Safety & Security.

Parameters Economic Profile Demographic ProfileNew York 1081 1078London 1053 1083

Hong Kong 1003 998Singapore 1004 1054

Sydney 959 1026Dubai 975 1007Tokyo 1018 1029Beijing 970 1007Mumbai 912 936

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Delhi 912 926Total 9887 10144

Mean 395.48 405.76

New Yo

rk

London

Hong Kong

Singa

pore

Sydney

DubaiTo

kyoBeij

ing

MumbaiDelh

i800

850

900

950

1000

1050

11001081

1053

1003 1004

959975

1018

970

912 912

1078 1083

998

10541026

10071029

1007

936 926Economic Profile

Demo-graphic Profile

Cities

Scor

e

Inference: There is not much difference for the nations in two profiles, except in case of Singapore and Sydney. They have nice Demographic profile.

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Factor AnalysisFactor analysis is a multivariate statistical technique in which there is no distinction between dependent and independent variables. In this, all variables are under investigation. The basic principle behind the application of factor analysis is that the initial set of variables should be highly co-related. Otherwise this is not an appropriate test. A co-relation matrix could be computed and tested for its statistical significance. For this I would like to hypothesise that:

H0 – Co-relation matrix is insignificant, i.e., correlation matrix is an identity matrix where diagonal elements are one and off diagonal elements are zero.

H1 – Co-relation matrix is significant.

As noted earlier the various factor in my survey included,

1. Ease of doing business2. Economic clout3. Entrepreneurial environment4. Sustainability and natural environment5. Political environment6. Cultural vibrancy7. Literacy and enrolment8. Technological readiness and digital knowhow9. Health safety and security10. Transportation and infrastructure

These factors have been analysed together to extract the underline factors. These factors explain most of the variations of the original set of data.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.836

Bartlett's Test of Sphericity

Approx. Chi-Square 276.537

df 45Sig. .000

KMO and the Bartlett test of sphericity have been done to establish the strength of the factor analysis

solution because it is essential to establish the validity and reliability of the obtained reduction.

Since the value of KMO is more than 0.05, the factor analysis could be obtained from the data. Bartlett

test of sphericity testing for the significance of correlation matrix of the variables indicated by the p value

corresponding to chi-square stats. The p value is 0.000 which is less than 0.05, the assumed level of

significance, indicating the rejection of hypothesis H0 that the correlation matrix of the variables is

insignificant.

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Correlation MatrixEase

of

doing

busin

ess

Econ

omic

clout

Entrepre

neurial

environm

ent

Sustain

ability

and

natural

environ

ment

Politica

l

environ

ment

Cultur

al

vibran

cy

Literacy

and

enrolment

Technolo

gical

readines

s and

digital

knowhow

Health

safety

and

security

Transpor

tation

and

infrastruc

ture

Si

g.

(1

-

tai

le

d)

Ease of

doing

business

.000 .000 .000 .000 .012 .000 .000 .001 .038

Economic

clout

.000 .000 .000 .000 .001 .000 .000 .000 .002

Entrepreneur

ial

environment

.000 .000 .000 .000 .000 .000 .000 .000 .000

Sustainabilit

y and natural

environment

.000 .000 .000 .000 .000 .001 .000 .000 .038

Political

environment

.000 .000 .000 .000 .000 .000 .000 .000 .012

Cultural

vibrancy

.012 .001 .000 .000 .000 .008 .000 .000 .000

Literacy and

enrolment

.000 .000 .000 .001 .000 .008 .000 .001 .016

Technologic

al readiness

and digital

knowhow

.000 .000 .000 .000 .000 .000 .000 .000 .000

Health safety

and security

.001 .000 .000 .000 .000 .000 .001 .000 .000

Transportati

on and

infrastructure

.038 .002 .000 .038 .012 .000 .016 .000 .000

We can observe from the co-relation matrix that there is moderate co-relation existing between Transportation and Infrastructure with Ease of Doing Business and also with Sustainability and Natural environment.

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CommunalitiesInitial Extraction

Ease of doing business 1.000 .916

Economic clout 1.000 .926

Entrepreneurial environment 1.000 .851

Sustainability and natural environment 1.000 .707

Political environment 1.000 .836

Cultural vibrancy 1.000 .829

Literacy and enrolment 1.000 .823

Technological readiness and digital knowhow 1.000 .864

Health safety and security 1.000 .834

Transportation and infrastructure 1.000 .748

Extraction Method: Principal Component Analysis.Communalities show how much of the variance in the variables has been accounted for the extracted factors. For instance, 91.6% of the variance in ease of doing business is accounted for. Similarly, there is 92.6% variation in economic clout. We can infer for other factors in similar way.

Total Variance ExplainedComponent

Initial Eigen values Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total % of Variance

Cumulative

%

Total % of Varian

ce

Cumulative %

Total % of Variance

Cumulative %

1 7.239 72.389 72.389 7.239 72.389 72.389 4.687 46.875 46.875

2 1.095 10.952 83.340 1.095 10.952 83.340 3.647 36.465 83.340

3 .625 6.249 89.589

4 .351 3.505 93.095

5 .210 2.095 95.190

6 .194 1.945 97.135

7 .117 1.173 98.308

8 .095 .947 99.254

9 .054 .537 99.791

10 .021 .209 100.000

Extraction Method: Principal Component Analysis.

This table shows all the factors extractable from the analysis along with their Eigen values, the percent of variance attributable to each factor, and the communality variance of the factor and previous factors. The first factor accounts for 72.389% whereas second factor for only 10.952%.

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Component MatrixComponent

1 2

Ease of doing business .837 -.464

Economic clout .918 -.287

Entrepreneurial environment .922 -.022

Sustainability and natural environment .826 -.160

Political environment .905 -.129

Cultural vibrancy .807 .421

Literacy and enrolment .845 -.330

Technological readiness and digital knowhow .901 .230

Health safety and security .848 .338

Transportation and infrastructure .669 .549

Extraction Method: Principal Component Analysis.

a. 2 components extracted.This is the Factor matrix/component Matrix. It shows the correlation coefficient between the factor score and the variables include (factor loading). The correlation coefficient between the 3rd variables size and component 1 is 0.922, for example. Similarly, correlation coefficient between 10th variable size and second component size is 0.549.

Rotated Component MatrixComponent

1 2

Ease of doing business .939 .184

Economic clout .887 .372

Entrepreneurial environment .719 .578

Sustainability and natural environment .734 .410

Political environment .775 .484

Cultural vibrancy .346 .842

Literacy and enrolment .859 .292

Technological readiness and digital knowhow .541 .756

Health safety and security .431 .805

Transportation and infrastructure .158 .850

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

The idea of rotation is to reduce the number of factors on which the variables under investigation have high loadings. Rotation does not actually change anything but makes the interpretation of the analysis easier. The cut-off point has been taken as 0.6.

It can be observed that ease of doing business, economic clout, entrepreneurial environment, sustainability & natural environment, political environment and literacy & enrolment are substantially loaded on factor (component 1).

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Whereas cultural vibrancy, health, safety & security, technological readiness & digital knowhow and transportation & infrastructure are loaded on the other factor (component 2).

Component Transformation MatrixComponent 1 2

1 .765 .644

2 -.644 .765

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Component transformation matrix is a matrix by which we multiply the unrotated factor matrix to get the rotated matrix.

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ConclusionNow, we can answer all the questions which were important to achieve our research objectives as mentioned earlier:

1) To find out and compare the 10 cities on various performance parameters.New York City and London are the two cities which are lead the lot. They share the maximum score in various parameters. New York was 1st in 7 of the 10 parameters. London topped in 4 parameters. Whereas, Singapore and New Delhi were at the top in 2 parameters. There were three ties in the result.

2) To find out how the two Indian cities Delhi and Mumbai performs in comparison to other foreign cities in question.Delhi and Mumbai are far behind the other cities in every parameter except the cultural vibrancy.

3) To compare Delhi and Mumbai on with each other.

Both are head to head in each parameter, although Mumbai has some advantage in terms of Health, Safety and Security.

4) To find out which are the parameters most respondents are more bullish and bearish about.

Respondents are more bullish to technological readiness & digital know how (2139) and transport & Infrastructure (2136). But their sentiments are not so positive about ease of doing business (1856) and sustainability and natural environment (1880).

5) To observe how different respondents perceive the cities overall, based on the scores they provided.Respondents have different perceptions about the cities; their scoring varies from 55% (549/1000) to 99% (992/1000).

6) To compare the economic and demographic profile of the cities.There is not much difference for the nations in two profiles, except in case of Singapore and Sydney. They have nice Demographic profile.

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7) To find out the co-relation in the parameters, if any.We can observe from the co-relation matrix that there is moderate co-relation existing between

a) Transportation and Infrastructure - Ease of Doing Business, and

b) Transportation and Infrastructure - Sustainability and Natural environment.

REFERENCES Chawla, Deepak & Sondhi, Neena; Research Methodology: Concepts and Cases ; 1st edition; 201; Vikas Publishing House .

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